R/dynrRecipe.R

Defines functions gslcovariate.front gslVectorCopy gslVector2Column blasMM blasMV destroyGslVector createGslVector destroyGslMatrix createGslMatrix setGslVectorElements setGslMatrixElements addLLFormulas addFormulas matrix2formula formula2string prep.tfun prep.initial processFormula trans2CFunction parseNested parseFormula isSymbolNumberFunction prep.matrixDynamics prep.formulaDynamics autojacob prep.regimes checkSymmetric replaceDiagZero prep.noise prep.measurement prep.loadings extractValues extractParams extractWhichParams preProcessNames checkMultipleStart symmExtract coProcessValuesParams preProcessParams preProcessValues reverseldl dynr.ldl transldl mat2vec vec2mat makeldlchar exchangeNamesInFormula replaceNamesbyNumbers exchangeformulaNamesAndNumbers exchangeNamesAndNumbers xtableMatrix formula2tex mvpaste printRecipeOrModel

Documented in dynr.ldl prep.formulaDynamics prep.initial prep.loadings prep.matrixDynamics prep.measurement prep.noise prep.regimes prep.tfun

# A Recipe is a helper function that takes user input
#  and produces a C function definition that can be
#  compiled by dynrFuncaddress.

# TODO add check that there is no cross loading on one of the ID variables

# TODO output the starting values for the parameters 
# TODO add documentation


#---------------------------------------------------------------------------------------------
# Create dynrRecipe object class

##' The dynrRecipe Class
##' 
##' @aliases
##' $,dynrRecipe-method
##' print,dynrRecipe-method
##' show,dynrRecipe-method
##' 
##' @details
##' This is an internal class structure.  You should not use it directly.
##' The following are all subclasses of this class: \code{\link{dynrMeasurement-class}}, 
##' \code{\link{dynrDynamics-class}}, \code{\link{dynrRegimes-class}}, 
##' \code{\link{dynrInitial-class}}, \code{\link{dynrNoise-class}}, 
##' and \code{\link{dynrTrans-class}}.  Recipes are the things that 
##' go into a \code{\link{dynrModel-class}} using \code{\link{dynr.model}}.
##' Use the recipe prep functions (\code{\link{prep.measurement}}, 
##' \code{\link{prep.formulaDynamics}}, \code{\link{prep.matrixDynamics}}, 
##' \code{\link{prep.regimes}}, \code{\link{prep.initial}}, \code{\link{prep.noise}}, 
##' or \code{\link{prep.tfun}}) to create these classes instead.
setClass(Class =  "dynrRecipe",
         representation = representation(
           c.string =  "character",
           startval = "numeric",
           paramnames = "character"
         )
)

##' The dynrMeasurement Class
##' 
##' @details
##' This is an internal class structure.  You should not use it directly.
##' Use \code{\link{prep.measurement}} or \code{\link{prep.loadings}} instead.
setClass(Class = "dynrMeasurement",
         representation = representation(
           c.string =  "character",
           startval = "numeric",
           paramnames = "character",
           values.load = "list",
           params.load = "list",
           values.exo = "list",
           params.exo = "list",
           values.int = "list",
           params.int = "list",
           state.names="character",
           obs.names="character",
           exo.names="character"),
         contains = "dynrRecipe"
)

##' The dynrDynamics Class
##' 
##' @aliases
##' dynrDynamicsFormula-class
##' dynrDynamicsMatrix-class
##' 
##' @details
##' This is an internal class structure.  The classes \code{dynrDynamicsFormula-class} 
##' and \code{dynrDynamicsMatrix-class} are subclasses of this.  However, you should 
##' not use it directly.
##' Use \code{\link{prep.matrixDynamics}} or \code{\link{prep.formulaDynamics}} instead.
setClass(Class = "dynrDynamics",
         representation = representation(
           c.string =  "character",
           startval = "numeric",
           paramnames = "character",
           isContinuousTime = "logical"
           ),
         contains = "dynrRecipe"
)

setClass(Class = "dynrDynamicsFormula",
         representation = representation(
           c.string =  "character",
           startval = "numeric",
           paramnames = "character",
           formula = "list",
           jacobian = "list",
           isContinuousTime = "logical"
           ),
         contains = "dynrDynamics"
)

setClass(Class = "dynrDynamicsMatrix",
         representation = representation(
           c.string =  "character",
           startval = "numeric",
           paramnames = "character",
           values.dyn = "list",
           params.dyn = "list",
           values.exo = "list",
           params.exo = "list",
           values.int = "list",
           params.int = "list",
           covariates = "character",
           isContinuousTime = "logical"
           ),
         contains = "dynrDynamics"
)

##' The dynrRegimes Class
##' 
##' @details
##' This is an internal class structure.  You should not use it directly.
##' Use \code{\link{prep.regimes}} instead.
setClass(Class = "dynrRegimes",
         representation = representation(
           c.string =  "character",
           startval = "numeric",
           paramnames = "character",
           values = "matrix",
           params = "matrix",
           covariates = "character",
           deviation = "logical",
           refRow = "numeric"),
         contains = "dynrRecipe"
)

##' The dynrInitial Class
##' 
##' @details
##' This is an internal class structure.  You should not use it directly.
##' Use \code{\link{prep.initial}} instead.
setClass(Class = "dynrInitial",
         representation = representation(
           c.string =  "character",
           startval = "numeric",
           paramnames = "character",
           values.inistate = "list",
           params.inistate = "list",
           values.inicov = "list",
           values.inicov.inv.ldl = "list",
           params.inicov = "list",
           values.regimep = "matrix",
           params.regimep = "matrix",
           covariates = "character",
           deviation = "logical",
           refRow = "numeric"),
         contains = "dynrRecipe"
)


##' The dynrNoise Class
##' 
##' @details
##' This is an internal class structure.  You should not use it directly.
##' Use \code{\link{prep.noise}} instead.
setClass(Class = "dynrNoise",
         representation = representation(
           c.string =  "character",
           startval = "numeric",
           paramnames = "character",
           values.latent = "list",
           params.latent = "list",
           values.observed = "list",
           params.observed = "list",
           values.latent.inv.ldl = "list",
           values.observed.inv.ldl = "list"),
         contains = "dynrRecipe"
)
#TODO we should emphasize that either the full noise covariance structures should be freed or the diagonals because we are to apply the ldl trans  

##' The dynrTrans Class
##' 
##' @details
##' This is an internal class structure.  You should not use it directly.
##' Use \code{\link{prep.tfun}} instead.
setClass(Class = "dynrTrans",
         representation = representation(
           c.string =  "character",
           startval = "numeric",#not sure if needed in dynrTrans
           paramnames = "character",#not sure if needed in dynrTrans
           tfun="function",
           inv.tfun="function",
           inv.tfun.full="function",
           formula.trans="list",
           formula.inv="list",
           transCcode="logical"
           ),
         contains = "dynrRecipe",
         prototype = prototype(
           tfun=function(x){x}
         )
)

setMethod("initialize", "dynrRecipe",
		function(.Object, x){
			for(i in names(x)){
				slot(.Object, name=i, check = TRUE) <- x[[i]]
			}
			return(.Object)
		}
)

setMethod("$", "dynrRecipe",
		function(x, name){slot(x, name)}
)

printRecipeOrModel <- function(x, ...){
	for(aslot in slotNames(x)){
		if( !(aslot %in% c("c.string", "startval", "paramnames")) ){
			cat(" $", aslot, "\n", sep="")
			print(slot(x, aslot))
			cat("\n")
		}
	}
	return(invisible(x))
}

setMethod("print", "dynrRecipe", printRecipeOrModel)
setMethod("show", "dynrRecipe", function(object){printRecipeOrModel(object)})


#------------------------------------------------------------------------------
# printex method definitions

##' The printex Method
##' 
##' @aliases
##' printex,dynrModel-method
##' printex,dynrCook-method
##' printex,dynrMeasurement-method
##' printex,dynrDynamicsFormula-method
##' printex,dynrDynamicsMatrix-method
##' printex,dynrNoise-method
##' printex,dynrInitial-method
##' printex,dynrRegimes-method
##' 
##' @param object The dynr object (recipe, model, or cooked model).
##' @param ParameterAs The parameter values or names to plot. The underscores in parameter names are 
##' saved for use of subscripts.  Greek letters can be specified as corresponding LaTeX symbols without ##' backslashes (e.g., "lambda") and printed as greek letters.
##' @param printDyn logical. Whether or not to print the dynamic model. The default is TRUE.
##' @param printMeas logical. Whether or not to print the measurement model. The default is TRUE.


##' @param printInit logical. Whether or not to print the initial conditions. The default is FALSE.
##' @param printRS logical. Whether or not to print the regime-switching model. The default is FALSE.
##' @param outFile The name of the output tex file.
##' @param show logical indicator of whether or not to show the result in the console. 
##' @param ... Further named arguments, passed to internal method. 
##' \code{AsMatrix} is a logical indicator of whether to put the object in matrix form.
##' 
##' @details
##' This is a general way of getting a LaTeX string for recipes, 
##' models, and cooked models.  It is a great way to check that 
##' you specified the model or recipe you think you did before 
##' estimating its free parameters (cooking).  After the model 
##' is cooked, you can use it to get LaTeX code with the estimated 
##' parameters in it.
##' 
##' Typical inputs to the \code{ParameterAs} argument are (1) the starting values for a model, (2) the final estimated values for a model, and (3) the parameter names.  These are accessible with (1) \code{model$xstart}, (2) \code{coef(cook)}, and (3) \code{model$param.names} or \code{names(coef(cook))}, respectively.
##' 
##' @seealso A way to put this in a plot with \code{\link{plotFormula}}
setGeneric("printex", function(object, ParameterAs, 
	printDyn=TRUE, printMeas=TRUE, printInit=FALSE, printRS=FALSE, outFile, show, ...) { 
	return(standardGeneric("printex")) 
})

setMethod("printex", "dynrMeasurement",
          function(object, ParameterAs, 
			  printDyn=TRUE, printMeas=TRUE, printInit=FALSE, printRS=FALSE, outFile, 
			  show=TRUE, AsMatrix=TRUE){
            if (AsMatrix){
              meas_loadings=lapply(object$values.load, .xtableMatrix, show)
              meas_int=lapply(object$values.int, .xtableMatrix, show)
              meas_exo=lapply(object$values.exo, .xtableMatrix, show)
              meas_exo.names=.xtableMatrix(matrix(object$exo.names,ncol=1), show)
              meas_list = list(meas_loadings=meas_loadings,meas_int=meas_int,
                               meas_exo=meas_exo,meas_exo.names=meas_exo.names)
              return(invisible(meas_list))
            }else{#not to use matrix
              #Matrix to Formula
              meas_loadings <- lapply(object$values.load,matrix2formula,multbyColnames=T)
              meas_int <- lapply((object)$values.int,matrix2formula,multbyColnames=F)
              meas_exo <- lapply((object)$values.exo,matrix2formula,multbyColnames=T)
              meas_fml <- addLLFormulas(list_list_formulae = meas_loadings, 
                                        VecNamesToAdd = c("meas_int","meas_exo"))
              #Formula to LaTex
              meas_tex=lapply(meas_fml, formula2tex, 
                              LHSvarPre = "", LHSvarPost = "", RHSvarPre = "", RHSvarPost = "", 
                              LHSeqPre = "", LHSeqPost = "", RHSeqPre = "", RHSeqPost = "")
              return(meas_tex)
            }
            
          }
)

setMethod("printex", "dynrDynamicsMatrix",
          function(object, ParameterAs, 
			  printDyn=TRUE, printMeas=TRUE, printInit=FALSE, printRS=FALSE, outFile, 
			  show=TRUE, AsMatrix=TRUE){
            if (AsMatrix){
              dyn_tran=lapply((object)$values.dyn,.xtableMatrix, show)
              dyn_int=lapply((object)$values.int,.xtableMatrix, show)
              dyn_exo=lapply((object)$values.exo,.xtableMatrix, show)
              dyn_exo.names=.xtableMatrix(matrix((object)$covariates,ncol=1),show)
              return(invisible(list(dyn_tran = dyn_tran, dyn_int = dyn_int, dyn_exo = dyn_exo, dyn_exo.names = dyn_exo.names) ))
            }else{#not to use matrix
              #Matrix to Formula
              dyn_tran <- lapply(object$values.dyn,matrix2formula,multbyColnames=T)
              dyn_int <- lapply((object)$values.int,matrix2formula,multbyColnames=F)
              dyn_exo <- lapply((object)$values.exo,matrix2formula,multbyColnames=T)
              dyn_fml <- addLLFormulas(list_list_formulae = dyn_tran, 
                                       VecNamesToAdd = c("dyn_int","dyn_exo"))
              #Formula to LaTex
              if (object$isContinuousTime){
                LHSvarPre <- "d("
                LHSvarPost <- "(t))"
                RHSeqPre <- "("
                RHSeqPost <- ")dt"
              }else{
                LHSvarPre <- ""
                LHSvarPost <- "(t+1)"
                RHSeqPre <- ""
                RHSeqPost <- ""
              }
              RHSvarPost="(t)"
              dyn_tex=lapply(dyn_fml, formula2tex, 
                             LHSvarPre = LHSvarPre, LHSvarPost = LHSvarPost, RHSvarPre = "", RHSvarPost = RHSvarPost, 
                             LHSeqPre = "", LHSeqPost = "", RHSeqPre = RHSeqPre, RHSeqPost = RHSeqPost)
              return(dyn_tex)
            }
          }    
)

setMethod("printex", "dynrRegimes",
          function(object, ParameterAs, 
			  printDyn=TRUE, printMeas=TRUE, printInit=FALSE, printRS=FALSE, outFile, 
			  show=TRUE, AsMatrix=TRUE){
            lG <- ifelse(nrow(object$values) != 0, .xtableMatrix(object$values, show), "")
            return(invisible(list(regimes=lG)))
          }
)


mvpaste <- function(m, v, a){
	nr <- nrow(m)
	res <- matrix("", nrow=nr, ncol=1)
	for(r in 1:nr){
		mat1 <- m[r,]
		mat2 <- v
		mat2 <- mat2[mat1 !=0 ]
		mat1 <- mat1[mat1 !=0 ]
		mat3 <- paste(mat1, mat2, sep="*")
		mat3 <- gsub("*1", "", mat3, fixed=TRUE)
		a <- a[ a != 0]
		b <- implode(c(mat3, a), sep=" + ")
		b[b %in% ""] <- "0"
		res[r,] <- b
	}
	return(res)
}

setMethod("printex", "dynrInitial",
	function(object, ParameterAs, printDyn=TRUE, printMeas=TRUE,
	printInit=FALSE, printRS=FALSE, outFile, show=TRUE, AsMatrix=TRUE){
			values.regimep <- object$values.regimep
			params.regimep <- object$params.regimep
			numRegimes <- nrow(values.regimep)
			covariates <- object$covariates
			numCovariates <- length(covariates)
			deviation <- object$deviation
			refRow <- object$refRow
			
			nx <- nrow(object$values.inistate[[1]])
			covar <- c("1", object$covariates)
			x0val <- lapply(object$values.inistate, mvpaste, v=covar, a=rep("0", nx))
			lx0 <- lapply(x0val, .xtableMatrix, show)
			lP0 <- lapply(object$values.inicov, .xtableMatrix, show)
			
			if(deviation){
				if(nrow(values.regimep)!=0 && nrow(params.regimep)!=0){
					values.regIntercept <- matrix(values.regimep[refRow, 1], nrow=numRegimes, 1)
					params.regIntercept <- matrix(params.regimep[refRow, 1], nrow=numRegimes, 1)
					values.regimep[refRow, 1] <- 0
					params.regimep[refRow, 1] <- 0
				}
			} else {
				values.regIntercept <- matrix(0, numRegimes, 1)
				params.regIntercept <- matrix(0, numRegimes, 1)
			}
			
			p0val <- mvpaste(values.regimep, covar, values.regIntercept)
			lr0 <- .xtableMatrix(p0val, show)
			
			return(invisible(list(initial.state=lx0, initial.covariance=lP0, initial.probability=lr0)))
		}
)


setMethod("printex", "dynrNoise",
          function(object, ParameterAs, 
			  printDyn=TRUE, printMeas=TRUE, printInit=FALSE, printRS=FALSE, outFile, 
			  show=TRUE, AsMatrix=TRUE){
            lQ <- lapply(object$values.latent, .xtableMatrix, show=show)
            lR <- lapply(object$values.observed, .xtableMatrix, show=show)
            return(invisible(list(dynamic.noise=lQ, measurement.noise=lR)))
          }
)

setMethod("printex", "dynrDynamicsFormula",
          function(object, ParameterAs, 
			  printDyn=TRUE, printMeas=TRUE, printInit=FALSE, printRS=FALSE, outFile, 
			  show=TRUE, AsMatrix=TRUE){
            if (object$isContinuousTime){
            LHSvarPre <- "d("
            LHSvarPost <- "(t))"
            RHSeqPre <- "("
            RHSeqPost <- ")dt"
            }else{
            LHSvarPre <- ""
            LHSvarPost <- "(t+1)"
            RHSeqPre <- ""
            RHSeqPost <- ""
            }
            RHSvarPost="(t)"
            dyn=lapply(object$formula, formula2tex, 
                       LHSvarPre = LHSvarPre, LHSvarPost = LHSvarPost, RHSvarPre = "", RHSvarPost = RHSvarPost, 
                       LHSeqPre = "", LHSeqPost = "", RHSeqPre = RHSeqPre, RHSeqPost = RHSeqPost)
            return(dyn)
          }
)

#This function transforms a list of formulae to lists of latex code.
#returns a list of left, right, and equation latex code, each a vector.
formula2tex<-function(list_formulae, 
                      LHSvarPre = "", LHSvarPost = "", RHSvarPre = "", RHSvarPost = "", 
                      LHSeqPre = "$", LHSeqPost = "", RHSeqPre = "", RHSeqPost = "$"){
  eq.char=lapply(list_formulae, as.character)
  str.left=sapply(eq.char,"[",2)
  str.right=sapply(eq.char,"[",3)
  neq=length(list_formulae)
  mulpatn<-"([[:print:]]*)"
  sigpatn<-"([0-9A-Za-z_. ^*]*)"
  for (j in 1:neq){
    #The order of gsub matters.
    str.right[j]=gsub(paste0("\\(",mulpatn,"\\)/\\(",mulpatn,"\\)"),"\\\\frac{\\1}{\\2}",str.right[j])
    str.right[j]=gsub(paste0("\\(",mulpatn,"\\)/",sigpatn),"\\\\frac{\\1}{\\2}",str.right[j])
    str.right[j]=gsub(paste0(sigpatn,"/\\(",mulpatn,"\\)"),"\\\\frac{\\1}{\\2}",str.right[j])
    str.right[j]=gsub(paste0(sigpatn,"/",sigpatn),"\\\\frac{\\1}{\\2}",str.right[j])
    str.right[j]=gsub("\\bexp\\(", "\\\\exp\\(", str.right[j])
    str.right[j]=gsub("\\blog\\(", "\\\\log\\(", str.right[j])
    str.right[j]=gsub("\\bsin\\(", "\\\\sin\\(", str.right[j])
    str.right[j]=gsub("\\bcos\\(", "\\\\cos\\(", str.right[j])
    str.right[j]=gsub("\\*","\\\\times",str.right[j])
  }
  
  #Modify the presentation of the variables on RHS
  for (j in 1:neq){
    for (i in 1:neq){
      str.right[j]=gsub(paste0("\\<",str.left[i],"\\>"),paste0(RHSvarPre,str.left[i],RHSvarPost),str.right[j])
    }
  }
  #Modify the presentation of the variables on LHS
  for (j in 1:neq){
    str.left[j]=gsub(paste0(sigpatn),paste0(LHSvarPre,"\\1",LHSvarPost),str.left[j])
  }
  
  return(invisible(#list(left=str.left,right=str.right,equation=
    paste0(LHSeqPre, str.left, LHSeqPost," = ", RHSeqPre, str.right, RHSeqPost))
    )#)
}

.xtableMatrix <- function(m, show){
	x <- xtable::xtable(m, align=rep("", ncol(m)+1))
	out <- print(x, floating=FALSE, tabular.environment="bmatrix", 
		hline.after=NULL, include.rownames=FALSE, include.colnames=FALSE,
		print.results=show)
	return(out)
}


#------------------------------------------------------------------------------
# paramName2Number method definitions

setGeneric("paramName2Number", function(object, names) { 
	return(standardGeneric("paramName2Number")) 
})

setGeneric("paramName2NumericNumber", function(object, paramList) { 
  return(standardGeneric("paramName2NumericNumber")) 
})

.exchangeNamesAndNumbers <- function(params, names){
	matrix(match(params, names, nomatch=0), nrow(params), ncol(params))
}

.exchangeformulaNamesAndNumbers <- function(formula, paramnames, names){
	string<-paste0(deparse(formula,width.cutoff = 500L),collapse="")
	pattern <- paramnames
	#pattern=gsub("\\{","\\\\\\{",paramnames)
	#pattern=gsub("\\}","\\\\\\}",pattern)
	pattern=gsub("\\\\","\\\\\\\\",pattern)
	
	for(i in 1:length(paramnames)){
		string<-gsub(paste0("\\<",pattern[i],"\\>"),paste0("param[",match(paramnames[i], names, nomatch=0)-1,"]"), string)
	}
	eval(parse(text=string))
}

.replaceNamesbyNumbers <- function(formula, paramtoPlot){
  string<-paste0(deparse(formula,width.cutoff = 500L),collapse="")
  names <-   paste0("param\\[",(1:length(paramtoPlot))-1,"\\]")
  #names(paramtoPlot)
  #pattern=gsub("\\{","\\\\\\{",names)
  #pattern=gsub("\\}","\\\\\\}",pattern)
  #pattern=gsub("\\\\","\\\\\\\\",pattern)
  for (i in 1:length(names)){
  string<-gsub(paste0("\\<",names[i],"\\>"),paramtoPlot[i], string)
  }
  eval(parse(text=string))
}


setMethod("paramName2Number", "dynrMeasurement",
	function(object, names){
		object@params.load <- lapply(object$params.load, .exchangeNamesAndNumbers, names=names)
		object@params.exo <- lapply(object$params.exo, .exchangeNamesAndNumbers, names=names)
		object@params.int <- lapply(object$params.int, .exchangeNamesAndNumbers, names=names)
		return(object)
	}
)

.exchangeNamesInFormula <- function(formula, names){
	sall <- formula2string(formula)
	sr <- sall$rhs
	for(i in 1:length(sr)){
		for(j in 1:length(names)){
			pat <- paste0('\\<', names[j], '\\>')
			sr[i] <- gsub(pat, paste0('param[', j, ']'), sr[i])
		}
		formula[[i]] <- as.formula(paste(sall$lhs[i], '~', sr[i]))
	}
	return(formula)
}

# Example
## dynamics
#formula=list(
#  list(x1~a*x1,
#       x2~b*x2),
#  list(x1~a*x1+e*(exp(abs(x2))/(1+exp(abs(x2))))*x2,
#       x2~b*x2+f*(exp(abs(x1))/(1+exp(abs(x1))))*x1) 
#)
#paramnames <- c('a', 'b', 'e', 'f')

#.exchangeNamesInFormula(formula[[1]], paramnames)
#lapply(formula, .exchangeNamesInFormula, names=paramnames)


setMethod("paramName2Number", "dynrDynamicsFormula",
	function(object, names){
	  object@formula = .exchangeformulaNamesAndNumbers(object@formula, object@paramnames, names)
	  object@jacobian = .exchangeformulaNamesAndNumbers(object@jacobian, object@paramnames, names)
	  return(object)
	}
)

setMethod("paramName2NumericNumber", "dynrDynamicsFormula",
          function(object, paramList){
            object@formula = .replaceNamesbyNumbers(object@formula, paramList)
            object@jacobian =.replaceNamesbyNumbers(object@jacobian, paramList)
            return(object)
          }
)


setMethod("paramName2Number", "dynrDynamicsMatrix",
	function(object, names){
		object@params.dyn <- lapply(object$params.dyn, .exchangeNamesAndNumbers, names=names)
		object@params.exo <- lapply(object$params.exo, .exchangeNamesAndNumbers, names=names)
		object@params.int <- lapply(object$params.int, .exchangeNamesAndNumbers, names=names)
		return(object)
	}
)


setMethod("paramName2Number", "dynrRegimes",
	function(object, names){
		object@params <- .exchangeNamesAndNumbers(object$params, names)
		return(object)
	}
)


setMethod("paramName2Number", "dynrInitial",
	function(object, names){
		object@params.inistate <- lapply(object$params.inistate, .exchangeNamesAndNumbers, names=names)
		object@params.inicov <- lapply(object$params.inicov, .exchangeNamesAndNumbers, names=names)
		object@params.regimep <- .exchangeNamesAndNumbers(object$params.regimep, names=names)
		return(object)
	}
)


setMethod("paramName2Number", "dynrNoise",
	function(object, names){
		object@params.latent <- lapply(object$params.latent, .exchangeNamesAndNumbers, names=names)
		object@params.observed <- lapply(object$params.observed, .exchangeNamesAndNumbers, names=names)
		return(object)
	}
)

setMethod("paramName2Number", "dynrTrans",
  function(object, names){
    if (length(object@formula.trans)>0){
      object@formula.trans = .exchangeformulaNamesAndNumbers(object@formula.trans, object@paramnames, names)
    }
    return(object)
  }
)


#------------------------------------------------------------------------------
# writeCcode method definitions

setGeneric("writeCcode", 
           function(object, covariates, show=TRUE) { 
             return(standardGeneric("writeCcode")) 
           })



setMethod("writeCcode", "dynrMeasurement",
	function(object, covariates){
		values.load <- object$values.load
		params.load <- object$params.load
		values.exo <- object$values.exo
		params.exo <- object$params.exo
		values.int <- object$values.int
		params.int <- object$params.int
		hasCovariates <- length(values.exo) > 0
		hasIntercepts <- length(values.int) > 0
		nregime <- length(values.load)
		ret <- "void function_measurement(size_t t, size_t regime, double *param, const gsl_vector *eta, const gsl_vector *co_variate, gsl_matrix *Ht, gsl_vector *y){\n\n"
		if(hasCovariates){
		  ret <- paste0(ret, gslcovariate.front(object@exo.names, covariates))
		  ret <- paste(ret, createGslMatrix(nrow(params.exo[[1]]), ncol(params.exo[[1]]), "Bmatrix"), sep="\n\t")
		  }#create covariates matrix
		if(hasIntercepts){ret <- paste(ret, createGslVector(nrow(params.int[[1]]), "intVector"), sep="\n\t")}#create intercepts vector
		if(nregime > 1) {
			ret <- paste(ret, "\tswitch (regime) {\n", sep="\n")
			for(reg in 1:nregime){
				ret <- paste0(ret, paste0("\t\tcase ", reg-1, ":"), "\n")
				ret <- paste0(ret, "\t\t\t", setGslMatrixElements(values.load[[reg]], params.load[[reg]], "Ht"))
				if(hasCovariates){ #set covariates matrix
					ret <- paste0(ret, "\t\t\t", setGslMatrixElements(values.exo[[reg]], params.exo[[reg]], "Bmatrix"))
				}
				if(hasIntercepts){ret <- paste0(ret, "\t\t\t", setGslVectorElements(values.int[[reg]], params.int[[reg]], "intVector"))}#set intercepts vector
				ret <- paste0(ret, "\t\t", "break;", "\n") 
			}
			ret <- paste0(ret, "\t", "}\n\n")
		} else {
			ret <- paste(ret, setGslMatrixElements(values.load[[1]], params.load[[1]], "Ht"), sep="\n")
			if(hasCovariates){ret <- paste(ret, setGslMatrixElements(values.exo[[1]], params.exo[[1]], "Bmatrix"), sep="\n")}#set covariates matrix
			if(hasIntercepts){ret <- paste(ret, setGslVectorElements(values.int[[1]], params.int[[1]], "intVector"), sep="\n")}#set intercepts vector
		}
		ret <- paste(ret, "\n\tgsl_blas_dgemv(CblasNoTrans, 1.0, Ht, eta, 0.0, y);\n")
		if(hasCovariates){
		  ret <- paste(ret, "\n\tgsl_blas_dgemv(CblasNoTrans, 1.0, Bmatrix, covariate_local, 1.0, y);\n", destroyGslMatrix("Bmatrix"))
		  ret <- paste0(ret, destroyGslVector("covariate_local"))
		  }#multiply, add, and destroy covariates matrix
		if(hasIntercepts){ret <- paste(ret, "\n\tgsl_vector_add(y, intVector);\n", destroyGslVector("intVector"))}#add and destroy intercepts vector
		ret <- paste(ret, "\n}\n\n")
		object@c.string <- ret
		return(object)
	}
)


# not sure what to do here yet
setMethod("writeCcode", "dynrDynamicsFormula",
	function(object, covariates){
	  formula <- object$formula
	  jacob <- object$jacobian
	  nregime=length(formula)
	  n=sapply(formula,length)
	  
	  fml=lapply(formula,processFormula)
	  lhs=lapply(fml,function(x){lapply(x,"[[",1)})
	  rhs=lapply(fml,function(x){lapply(x,"[[",2)})
	  
	  fmlj=lapply(jacob,processFormula)
	  row=lapply(fmlj,function(x){lapply(x,"[[",1)})
	  col=lapply(fmlj,function(x){lapply(x,"[[",2)})
	  rhsj=lapply(fmlj,function(x){lapply(x,"[[",3)})
	  
	  for (i in 1:length(covariates)){
	    selected <- covariates[i]
	    get <- paste0("gsl_vector_get(co_variate, ", which(covariates == selected)-1,")")
	    rhs <- lapply(gsub(paste0("\\<",selected,"\\>"), get, rhs), function(x){eval(parse(text=x))})
	    rhsj <- lapply(gsub(paste0("\\<",selected,"\\>"), get, rhsj), function(x){eval(parse(text=x))})
	  }
	  
	  if (object@isContinuousTime){
	    #function_dx_dt
	    ret="void function_dx_dt(double t, size_t regime, const gsl_vector *x, double *param, size_t n_param, const gsl_vector *co_variate, gsl_vector *F_dx_dt){"
	    
	    if (nregime>1){
	      ret=paste(ret,"switch (regime) {",sep="\n\t")
	      for (r in 1:nregime){
	        ret=paste(ret,paste0("\tcase ",r-1,":"),sep="\n\t")
	        for (i in 1:n[r]){
	          for (j in 1:length(lhs[[r]])){
	            rhs[[r]][[i]]=gsub(paste0("\\<",lhs[[r]][[j]],"\\>"),paste0("gsl_vector_get(x,",j-1,")"),rhs[[r]][[i]])
	          }
	          ret=paste(ret,paste0("\tgsl_vector_set(F_dx_dt,",i-1,",",rhs[[r]][[i]],");"),sep="\n\t")    
	        }
	        ret=paste(ret,paste0("break;\n"),sep="\n\t")
	        
	      }
	      ret=paste(ret,paste0("\t}"),sep="\n\t")
	      
	    }else{
	      for (i in 1:n){
	        for (j in 1:length(lhs[[1]])){
	          rhs[[1]][[i]]=gsub(paste0("\\<",lhs[[1]][[j]],"\\>"),paste0("gsl_vector_get(x,",j-1,")"),rhs[[1]][[i]])
	        }
	        ret=paste(ret,paste0("\tgsl_vector_set(F_dx_dt,",i-1,",",rhs[[1]][[i]],");"),sep="\n\t")    
	      }
	    }
	    
	    ret=paste0(ret,"\n\t}")
	    
	    #function_dF_dx
	    ret=paste0(ret,"\n\n/**\n* The dF/dx function\n* The partial derivative of the jacobian of the DE function with respect to the variable x\n* @param param includes at the end the current state estimates in the same order as the states following the model parameters\n*/void function_dF_dx(double t, size_t regime, double *param, const gsl_vector *co_variate, gsl_matrix *F_dx_dt_dx){")
	    if (nregime>1){
	      ret=paste(ret,"switch (regime) {",sep="\n\t")
	      for (r in 1:nregime){
	        ret=paste(ret,paste0("case ",r-1,":"),sep="\n\t")
	        for (i in 1:length(jacob[[r]])){
	          for (j in 1:length(lhs[[r]])){
	            rhsj[[r]][[i]]=gsub(paste0("\\<",lhs[[r]][[j]],"\\>"),paste0("param[NUM_PARAM+",j-1,"]"),rhsj[[r]][[i]])
	          }
	          
	          ret=paste(ret,paste0("\tgsl_matrix_set(F_dx_dt_dx,",which(lhs[[r]]==row[[r]][[i]])-1,",",which(lhs[[r]]==col[[r]][[i]])-1,",",rhsj[[r]][[i]],");"),sep="\n\t")    
	        }
	        ret=paste(ret,paste0("break;\n"),sep="\n\t")
	        
	      }
	      ret=paste(ret,paste0("\t}"),sep="\n\t")
	      
	    }else{
	      for (i in 1:length(jacob[[1]])){
	        for (j in 1:length(lhs[[1]])){
	          rhsj[[1]][[i]]=gsub(paste0("\\<",lhs[[1]][[j]],"\\>"),paste0("param[NUM_PARAM+",j-1,"]"),rhsj[[1]][[i]])
	        }
	        
	        ret=paste(ret,paste0("\tgsl_matrix_set(F_dx_dt_dx,",which(unlist(lhs[[1]])==row[[1]][[i]])-1,",",which(unlist(lhs[[1]])==col[[1]][[i]])-1,",",rhsj[[1]][[i]],");"),sep="\n\t")    
	      }
	    }
	    
	    ret=paste0(ret,"\n\t}")
	    
	  }else{
	    #function_dynam
	    ret="void function_dynam(const double tstart, const double tend, size_t regime, const gsl_vector *xstart,\n\tdouble *param, size_t n_gparam,const gsl_vector *co_variate,\n\tvoid (*g)(double, size_t, const gsl_vector *, double *, size_t, const gsl_vector *, gsl_vector *),\n\tgsl_vector *x_tend){"
	    
	    if (nregime>1){
	      ret=paste(ret,"switch (regime) {",sep="\n\t")
	      for (r in 1:nregime){
	        ret=paste(ret,paste0("\tcase ",r-1,":"),sep="\n\t")
	        for (i in 1:n[r]){
	          for (j in 1:length(lhs[[r]])){
	            rhs[[r]][[i]]=gsub(paste0("\\<",lhs[[r]][[j]],"\\>"),paste0("gsl_vector_get(xstart,",j-1,")"),rhs[[r]][[i]])
	          }
	          ret=paste(ret,paste0("\tgsl_vector_set(x_tend,",i-1,",",rhs[[r]][[i]],");"),sep="\n\t")    
	        }
	        ret=paste(ret,paste0("break;\n"),sep="\n\t")
	        
	      }
	      ret=paste(ret,paste0("\t}"),sep="\n\t")
	      
	    }else{
	      for (i in 1:n){
	        for (j in 1:length(lhs[[1]])){
	          rhs[[1]][[i]]=gsub(paste0("\\<",lhs[[1]][[j]],"\\>"),paste0("gsl_vector_get(xstart,",j-1,")"),rhs[[1]][[i]])
	        }
	        ret=paste(ret,paste0("\tgsl_vector_set(x_tend,",i-1,",",rhs[[1]][[i]],");"),sep="\n\t")    
	      }
	    }
	    
	    ret=paste0(ret,"\n\t}")
	    
	    #function_jacob_dynam
	    ret=paste0(ret,"\n\nvoid function_jacob_dynam(const double tstart, const double tend, size_t regime, const gsl_vector *xstart,\n\tdouble *param, size_t num_func_param, const gsl_vector *co_variate,\n\tvoid (*g)(double, size_t, double *, const gsl_vector *, gsl_matrix *),\n\tgsl_matrix *Jx){")
	    if (nregime>1){
	      ret=paste(ret,"switch (regime) {",sep="\n\t")
	      for (r in 1:nregime){
	        ret=paste(ret,paste0("case ",r-1,":"),sep="\n\t")
	        for (i in 1:length(jacob[[r]])){
	          for (j in 1:length(lhs[[r]])){
	            rhsj[[r]][[i]]=gsub(paste0("\\<",lhs[[r]][[j]],"\\>"),paste0("gsl_vector_get(xstart,",j-1,")"),rhsj[[r]][[i]])
	          }
	          
	          ret=paste(ret,paste0("\tgsl_matrix_set(Jx,",which(lhs[[r]]==row[[r]][[i]])-1,",",which(lhs[[r]]==col[[r]][[i]])-1,",",rhsj[[r]][[i]],");"),sep="\n\t")    
	        }
	        ret=paste(ret,paste0("break;\n"),sep="\n\t")
	        
	      }
	      ret=paste(ret,paste0("\t}"),sep="\n\t")
	      
	    }else{
	      for (i in 1:length(jacob[[1]])){
	        for (j in 1:length(lhs[[1]])){
	          rhsj[[1]][[i]]=gsub(paste0("\\<",lhs[[1]][[j]],"\\>"),paste0("gsl_vector_get(xstart,",j-1,")"),rhsj[[1]][[i]])
	        }
	        
	        ret=paste(ret,paste0("\tgsl_matrix_set(Jx,",which(unlist(lhs[[1]])==row[[1]][[i]])-1,",",which(unlist(lhs[[1]])==col[[1]][[i]])-1,",",rhsj[[1]][[i]],");"),sep="\n\t")    
	      }
	    }
	    
	    ret=paste0(ret,"\n\t}")
	  }
	  object@c.string <- ret
	  return(object)
	}
)
#Example
# formula=list(
#   list(x1~a1*x1,
#        x2~a2*x2),
#   list(x1~a1*x1+c12*(exp(x2)/(1+exp(x2)))*x2,
#        x2~a2*x2+c21*(exp(x1)/(1+exp(x1)))*x1) 
# )
# dynam<-prep.formulaDynamics(formula=formula,startval=c(a1=.3,a2=.4,c12=-.5,c21=-.5),isContinuousTime=FALSE)
# cat(writeCcode(dynam)$c.string)

setMethod("writeCcode", "dynrDynamicsMatrix",
	function(object, covariates){
		isContinuousTime <- object$isContinuousTime
		params.dyn <- object$params.dyn
		values.dyn <- object$values.dyn
		params.exo <- object$params.exo
		values.exo <- object$values.exo
		params.int <- object$params.int
		values.int <- object$values.int
		
		nregime <- length(values.dyn)
		hasCovariates <- length(values.exo) > 0
		hasIntercepts <- length(values.int) > 0
		
		time <- ifelse(isContinuousTime, 'continuous', 'discrete')
		if(time == 'continuous'){
			# Construct matrices for A and B with A ~ dyn, B ~ exo
			# dx/dt ~ A %*% x + B %*% u
			# x is the latent state vector, u is the covariate vector
			# F_dx_dt = A %*% x + B %*% u
			# F_dx_dt_dx = A
			dynHead <- "void function_dx_dt(double t, size_t regime, const gsl_vector *x, double *param, size_t n_param, const gsl_vector *co_variate, gsl_vector *F_dx_dt){"
			jacHead <- "void function_dF_dx(double t, size_t regime, double *param, const gsl_vector *co_variate, gsl_matrix *F_dx_dt_dx){"
			inName <- "x"
			outName <- "F_dx_dt"
			jacName <- "F_dx_dt_dx"
		} else if(time == 'discrete'){
			# Construct matrices for A and B with A ~ dyn, B ~ exo
			# x[t] ~ A %*% x[t-1] + B %*% u
			# x is the latent state vector, u is the covariate vector
			dynHead <- "void function_dynam(const double tstart, const double tend, size_t regime, const gsl_vector *xstart, double *param, size_t n_gparam, const gsl_vector *co_variate, void (*g)(double, size_t, const gsl_vector *, double *, size_t, const gsl_vector *, gsl_vector *), gsl_vector *x_tend){"
			jacHead <- "void function_jacob_dynam(const double tstart, const double tend, size_t regime, const gsl_vector *xstart, double *param, size_t num_func_param, const gsl_vector *co_variate, void (*g)(double, size_t, double *, const gsl_vector *, gsl_matrix *), gsl_matrix *Jx){"
			inName <- "xstart"
			outName <- "x_tend"
			jacName <- "Jx"
		}
		
		#If numRegimes > 1
		if(nregime > 1){
			# Create dynamics (state-transition or drift matrix) with covariate effects
			ret <- paste(dynHead,
				createGslMatrix(nrow(params.dyn[[1]]), ncol(params.dyn[[1]]), "Amatrix"),
				ifelse(hasCovariates, 
				       paste0(gslcovariate.front(object@covariates, covariates), createGslMatrix(nrow(params.exo[[1]]), ncol(params.exo[[1]]), "Bmatrix")),
				       ""),
				ifelse(hasIntercepts, createGslVector(nrow(params.int[[1]]), "intVector"), ""),
				sep="\n")
			
			#Loop through regimes for dynamics
			ret <- paste(ret, "\tswitch (regime) {\n", sep="\n")
			for(reg in 1:nregime){
				ret <- paste0(ret, paste0("\t\tcase ", reg-1, ":"), "\n")
				ret <- paste(ret,
					setGslMatrixElements(values=values.dyn[[reg]], params=params.dyn[[reg]], name="Amatrix"),
					ifelse(hasCovariates, paste0("\t\t\t", setGslMatrixElements(values=values.exo[[reg]], params=params.exo[[reg]], name="Bmatrix")), ""),
					ifelse(hasIntercepts, paste0("\t\t\t", setGslVectorElements(values=values.int[[reg]], params=params.int[[reg]], name="intVector")), ""),
					sep="\n")
				ret <- paste0(ret, "\t\t", "break;", "\n") 
			}
			ret <- paste0(ret, "\t", "}\n\n")
			
			ret <- paste(ret,
				blasMV(FALSE, "1.0", "Amatrix", inName, "0.0", outName),
				ifelse(hasCovariates, blasMV(FALSE, "1.0", "Bmatrix", "covariate_local", "1.0", outName), ""),
				ifelse(hasIntercepts, paste0("\tgsl_vector_add(", outName, ", intVector);\n"), ""),
				destroyGslMatrix("Amatrix"),
				ifelse(hasCovariates, paste0(destroyGslMatrix("Bmatrix"), destroyGslVector("covariate_local")), ""),
				ifelse(hasIntercepts, destroyGslVector("intVector"), ""),
				"}\n\n", sep="\n")
			
			
			# Create jacobian function
			ret <- paste(ret, jacHead)
			#Loop through regimes for jacobian
			ret <- paste(ret, "\tswitch (regime) {\n", sep="\n")
			for(reg in 1:nregime){
				ret <- paste0(ret, paste0("\t\tcase ", reg-1, ":"), "\n")
				ret <- paste(ret,
					setGslMatrixElements(values=values.dyn[[reg]], params=params.dyn[[reg]], name=jacName),
					sep="\n")
				ret <- paste0(ret, "\t\t", "break;", "\n") 
			}
			ret <- paste0(ret, "\t", "}\n\n")
			ret <- paste(ret, "}\n\n")
		} else { #Else If numRegimes == 1
			# Create dynamics (state-transition or drift matrix) with covariate effects
			ret <- paste(dynHead,
				createGslMatrix(nrow(params.dyn[[1]]), ncol(params.dyn[[1]]), "Amatrix"),
				setGslMatrixElements(values=values.dyn[[1]], params=params.dyn[[1]], name="Amatrix"),
				blasMV(FALSE, "1.0", "Amatrix", inName, "0.0", outName),
				destroyGslMatrix("Amatrix"),
				sep="\n")
			
			if(hasCovariates){
				ret <- paste(ret,
				  gslcovariate.front(object@covariates, covariates),
					createGslMatrix(nrow(params.exo[[1]]), ncol(params.exo[[1]]), "Bmatrix"),
					setGslMatrixElements(values=values.exo[[1]], params=params.exo[[1]], name="Bmatrix"),
					blasMV(FALSE, "1.0", "Bmatrix", "covariate_local", "1.0", outName),
					destroyGslMatrix("Bmatrix"),
					destroyGslVector("covariate_local"),
					sep="\n")
			}
			
			if(hasIntercepts){
				ret <- paste0(ret, "\n",
					createGslVector(nrow(params.int[[1]]), "intVector"), "\n",
					setGslVectorElements(values=values.int[[1]], params=params.int[[1]], name="intVector"), "\n",
					"\tgsl_vector_add(", outName, ", intVector);", "\n",
					destroyGslVector("intVector"))
			}
			
			ret <- paste(ret, "}\n\n", sep="\n")
			
			
			# Create jacobian function
			ret <- paste(ret,
				jacHead,
				setGslMatrixElements(values=values.dyn[[1]], params=params.dyn[[1]], name=jacName),
				"}\n\n",
				sep="\n")
		}
		object@c.string <- ret
		return(object)
	}
)


setMethod("writeCcode", "dynrRegimes",
	function(object, covariates){
		values <- object$values
		params <- object$params
		covariates_local <- object$covariates
		numCovariates <- length(covariates_local)
		numRegimes <- nrow(values)
		deviation <- object$deviation
		refRow <- object$refRow
		
		if(deviation){
			if(nrow(values)!=0 && nrow(params)!=0){
				# I need to look into this one
				interceptSel <- seq(1, ncol(values), by=numCovariates+1)
				intercept.values <- matrix(values[refRow, interceptSel], numRegimes, 1)
				intercept.params <- matrix(params[refRow, interceptSel], numRegimes, 1)
				values[refRow, interceptSel] <- 0
				params[refRow, interceptSel] <- 0
			}
		} else {
			intercept.values <- matrix(0, numRegimes, 1)
			intercept.params <- matrix(0, numRegimes, 1)
		}
		
		#Restructure values matrix for row-wise
		if(nrow(values)!=0 && nrow(params)!=0){
			values <- matrix(t(values), nrow=numRegimes*numRegimes, ncol=numCovariates+1, byrow=TRUE)
			params <- matrix(t(params), nrow=numRegimes*numRegimes, ncol=numCovariates+1, byrow=TRUE)
			rowBeginSeq <- seq(1, nrow(values), by=numRegimes)
			rowEndSeq <- seq(numRegimes, nrow(values), by=numRegimes)
		}
		
		ret <- "void function_regime_switch(size_t t, size_t type, double *param, const gsl_vector *co_variate, gsl_matrix *regime_switch_mat){"
		if(prod(nrow(values), nrow(params), numCovariates) != 0){
			ret <- paste(ret,
			             gslcovariate.front(covariates_local, covariates),
			             createGslMatrix(numRegimes, numCovariates, "Gmatrix"),
			             createGslVector(numRegimes, "Pvector"),
			             createGslVector(numRegimes, "Presult"),
			             createGslVector(numRegimes, "Pintercept"),
			             setGslVectorElements(values=intercept.values, params=intercept.params, name="Pintercept"),
			             sep="\n")
			for(reg in 1L:numRegimes){
				selRows <- rowBeginSeq[reg]:rowEndSeq[reg]
				ret <- paste(ret,
					setGslVectorElements(values=values[selRows, 1, drop=FALSE], params=params[selRows, 1, drop=FALSE], name="Pvector"),
					setGslMatrixElements(values=values[selRows, -1, drop=FALSE], params=params[selRows, -1, drop=FALSE], name="Gmatrix"),
					blasMV(FALSE, "1.0", "Gmatrix", "covariate_local", "1.0", "Pvector"),
					"\tgsl_vector_add(Pvector, Pintercept);",
					"\tmathfunction_softmax(Pvector, Presult);",
					gslVector2Column("regime_switch_mat", reg-1, "Presult", 'row'),
					"\tgsl_vector_set_zero(Pvector);",
					"\tgsl_matrix_set_zero(Gmatrix);",
					sep="\n")
			}
			ret <- paste(ret,
				destroyGslMatrix("Gmatrix"),
				destroyGslVector("Pvector"),
				destroyGslVector("Presult"),
				destroyGslVector("Pintercept"),
				destroyGslVector("covariate_local"),
				sep="\n")
		} else if(nrow(values) != 0 && nrow(params) != 0 && numCovariates == 0){
			# same as above processing, but without the Gmatrix for the covariates
			ret <- paste(ret,
				createGslVector(numRegimes, "Pvector"),
				createGslVector(numRegimes, "Presult"),
				createGslVector(numRegimes, "Pintercept"),
				setGslVectorElements(values=intercept.values, params=intercept.params, name="Pintercept"),
				sep="\n")
			for(reg in 1L:numRegimes){
				selRows <- rowBeginSeq[reg]:rowEndSeq[reg]
				ret <- paste(ret,
					setGslVectorElements(values=values[selRows, 1, drop=FALSE], params=params[selRows, 1, drop=FALSE], name="Pvector"),
					"\tgsl_vector_add(Pvector, Pintercept);",
					"\tmathfunction_softmax(Pvector, Presult);",
					gslVector2Column("regime_switch_mat", reg-1, "Presult", 'row'),
					"\tgsl_vector_set_zero(Pvector);",
					sep="\n")
			}
			ret <- paste(ret,
				destroyGslVector("Pvector"),
				destroyGslVector("Presult"),
				destroyGslVector("Pintercept"),
				sep="\n")
		} else {
			ret <- paste(ret, "\tgsl_matrix_set_identity(regime_switch_mat);", sep="\n")
		}
		ret <- paste(ret, "}\n\n", sep="\n")
		object@c.string <- ret
		return(object)
	}
)


setMethod("writeCcode", "dynrInitial",
	function(object, covariates){
		values.inistate <- object$values.inistate
		params.inistate <- object$params.inistate
		values.inicov <- object@values.inicov.inv.ldl
		params.inicov <- object$params.inicov
		values.regimep <- object$values.regimep
		params.regimep <- object$params.regimep
		covariates_local <- object$covariates
		numCovariates <- length(covariates_local)
		hasCovariates <- numCovariates > 0
		deviation <- object$deviation
		refRow <- object$refRow
		
		
		nregime <- max(length(values.inistate), length(values.inicov), nrow(values.regimep))
		if(nregime != 1 && length(values.inistate) == 1){
			values.inistate <- rep(values.inistate, nregime)
			params.inistate <- rep(params.inistate, nregime)
		}
		if(nregime != 1 && length(values.inicov) == 1){
			values.inicov <- rep(values.inicov, nregime)
			params.inicov <- rep(params.inicov, nregime)
		}
		someStatesNotZero <- sapply(values.inistate, function(x){!all(x == 0)})
		someStatesNotZero2 <- sapply(params.inistate, function(x){!all(x == 0)})
		someStatesNotZero <- someStatesNotZero | someStatesNotZero2
		someStatesNotZero <- someStatesNotZero | rep(TRUE, length(someStatesNotZero))
		
		numLatent <- nrow(values.inistate[[1]])
		
		values.etaIntercept <- lapply(values.inistate, function(x){x[,1, drop=FALSE]})
		params.etaIntercept <- lapply(params.inistate, function(x){x[,1, drop=FALSE]})
		values.covEffects <- lapply(values.inistate, function(x){x[,-1, drop=FALSE]})
		params.covEffects <- lapply(params.inistate, function(x){x[,-1, drop=FALSE]})
		
		if(deviation){
			if(nrow(values.regimep)!=0 && nrow(params.regimep)!=0){
				values.regIntercept <- matrix(values.regimep[refRow, 1], nrow=nregime, 1)
				params.regIntercept <- matrix(params.regimep[refRow, 1], nrow=nregime, 1)
				values.regimep[refRow, 1] <- 0
				params.regimep[refRow, 1] <- 0
			}
		} else {
			values.regIntercept <- matrix(0, nregime, 1)
			params.regIntercept <- matrix(0, nregime, 1)
		}
		
		ret <- "void function_initial_condition(double *param, gsl_vector **co_variate, gsl_vector **pr_0, gsl_vector **eta_0, gsl_matrix **error_cov_0, size_t *index_sbj){\n\t"
		ret <- paste0(ret, "\n", createGslVector(nregime, "Pvector"))
		ret <- paste0(ret, createGslVector(nregime, "Pintercept"))
		ret <- paste0(ret, createGslVector(nregime, "Padd"))
		ret <- paste0(ret, createGslVector(nregime, "Preset"))
		ret <- paste0(ret, setGslVectorElements(values.regimep[ , 1, drop=FALSE], params.regimep[ , 1, drop=FALSE], "Pvector"))
		ret <- paste0(ret, setGslVectorElements(values.regIntercept, params.regIntercept, "Pintercept"))
		ret <- paste0(ret, "\tgsl_vector_add(Padd, Pvector);\n")
		ret <- paste0(ret, "\tgsl_vector_add(Padd, Pintercept);\n")
		ret <- paste0(ret, "\tgsl_vector_add(Preset, Pvector);\n")
		ret <- paste0(ret, "\tgsl_vector_add(Preset, Pintercept);\n")
		
		ret <- paste0(ret, createGslVector(numLatent, "eta_local"))
		if(hasCovariates){
			ret <- paste0(ret, createGslVector(numCovariates, "covariate_local"))
			ret <- paste0(ret, createGslMatrix(numLatent, numCovariates, "Cmatrix"))
			ret <- paste0(ret, createGslMatrix(nregime, numCovariates, "Pmatrix"))
			ret <- paste0(ret, setGslMatrixElements(values=values.regimep[ , -1, drop=FALSE], params=params.regimep[ , -1, drop=FALSE], name="Pmatrix"))
			ret <- paste0(ret, "\t\n")
		}
		
		ret <- paste0(ret,"\tsize_t num_regime=pr_0[0]->size;\n\tsize_t dim_latent_var=error_cov_0[0]->size1;")
		if (any(someStatesNotZero)){
			ret <- paste0(ret,"\n\tsize_t num_sbj=(eta_0[0]->size)/(dim_latent_var);\n\tsize_t i;")
		}
		ret <- paste0(ret,"\n\tsize_t regime;\n")
		
		if(nregime > 1){
			ret <- paste0(ret, "\tfor(regime=0; regime < num_regime; regime++){")
			ret <- paste(ret, "\t\tswitch (regime) {\n", sep="\n")
				for(reg in 1:nregime){
					ret <- paste0(ret, paste0("\t\t\tcase ", reg-1, ":"), "\n")
					if (any(someStatesNotZero[reg])){
						ret <- paste0(ret,"\t\t\t\tfor(i=0; i < num_sbj; i++){\n")
						ret <- paste0(ret, setGslVectorElements(values=values.etaIntercept[[reg]], params=params.etaIntercept[[reg]], name='eta_local', depth=5))
						if(hasCovariates){
							ret <- paste0(ret, gslVectorCopy("co_variate[index_sbj[i]]", "covariate_local", fromLoc=match(covariates_local, covariates), toLoc=1:numCovariates, depth=5))
							ret <- paste0(ret, setGslMatrixElements(values=values.covEffects[[reg]], params=params.covEffects[[reg]], name="Cmatrix", depth=5))
							ret <- paste0(ret, "\t\t\t\t", blasMV(FALSE, "1.0", "Cmatrix", "covariate_local", "1.0", "eta_local"))
						}
						ret <- paste0(ret, gslVectorCopy("eta_local", "eta_0[regime]", 1:numLatent, 1:numLatent, toFill="i*dim_latent_var+", depth=5))
						ret <- paste0(ret, "\t\t\t\t\tgsl_vector_set_zero(eta_local);\n")
						if(hasCovariates){
							ret <- paste0(ret, "\t\t\t\t\tgsl_matrix_set_zero(Cmatrix);\n")
						}
						ret <- paste0(ret,"\t\t\t\t}") # close i loop
					}
					ret <- paste(ret, setGslMatrixElements(values.inicov[[reg]], params.inicov[[reg]], "(error_cov_0)[regime]", depth=4), sep="\n")
					ret <- paste0(ret, "\t\t\t", "break;", "\n")
				}
			ret <- paste0(ret, "\t\t", "}\n") # close case switch
			ret <- paste0(ret, "\t}") # close regime loop
		} else{
			ret <- paste0(ret, "\tfor(regime=0; regime < num_regime; regime++){")
			if (any(someStatesNotZero)){
				ret <- paste0(ret,"\n\t\tfor(i=0; i < num_sbj; i++){\n")
				ret <- paste0(ret, setGslVectorElements(values=values.etaIntercept[[1]], params=params.etaIntercept[[1]], name='eta_local', depth=3))
				if(hasCovariates){
					ret <- paste0(ret, gslVectorCopy("co_variate[index_sbj[i]]", "covariate_local", fromLoc=match(covariates_local, covariates), toLoc=1:numCovariates, depth=3))
					ret <- paste0(ret, setGslMatrixElements(values=values.covEffects[[1]], params=params.covEffects[[1]], name="Cmatrix", depth=3))
					ret <- paste0(ret, "\t\t", blasMV(FALSE, "1.0", "Cmatrix", "covariate_local", "1.0", "eta_local"))
				}
				ret <- paste0(ret, gslVectorCopy("eta_local", "eta_0[regime]", 1:numLatent, 1:numLatent, toFill="i*dim_latent_var+", depth=3))
				ret <- paste0(ret, "\t\t\tgsl_vector_set_zero(eta_local);\n")
				if(hasCovariates){
					ret <- paste0(ret, "\t\t\tgsl_matrix_set_zero(Cmatrix);\n")
				}
				ret <- paste0(ret,"\t\t}") # close i loop
			}
			ret <- paste(ret, setGslMatrixElements(values.inicov[[1]], params.inicov[[1]], "(error_cov_0)[regime]"), sep="\n")
			ret <- paste0(ret, "\t}") # close regime loop
		}
		
		ret <- paste0(ret,"\n\tfor(i=0; i < num_sbj; i++){\n")
		if(hasCovariates){
			ret <- paste0(ret, gslVectorCopy("co_variate[index_sbj[i]]", "covariate_local", fromLoc=match(covariates_local, covariates), toLoc=1:numCovariates, depth=2))
			ret <- paste0(ret, "\t", blasMV(FALSE, "1.0", "Pmatrix", "covariate_local", "1.0", "Padd"))
		}
		ret <- paste0(ret, "\t\tmathfunction_softmax(Padd, pr_0[i]);\n")
		if(hasCovariates){
			ret <- paste0(ret, "\t\tgsl_vector_memcpy(Padd, Preset);\n")
		}
		ret <- paste0(ret,"\t}") # close i loop
		
		ret <- paste0(ret, "\n", destroyGslVector("Pvector"), destroyGslVector("Pintercept"), destroyGslVector("Padd"), destroyGslVector("Preset"), destroyGslVector("eta_local"))
		if(hasCovariates){
			ret <- paste0(ret, destroyGslVector("covariate_local"))
			ret <- paste0(ret, destroyGslMatrix("Cmatrix"))
			ret <- paste0(ret, destroyGslMatrix("Pmatrix"))
		}
		ret <- paste0(ret, "}\n") #Close function definition
		object@c.string <- ret
		
		return(object)
	}
)

setMethod("writeCcode", "dynrNoise",
	function(object, covariates){
		params.latent <- object$params.latent
		params.observed <- object$params.observed
		#Note: use mutated values, instead of raw values
		values.latent <- object@values.latent.inv.ldl
		values.observed <- object@values.observed.inv.ldl
		
		nregime <- length(values.latent)
		
		ret <- "void function_noise_cov(size_t t, size_t regime, double *param, gsl_matrix *y_noise_cov, gsl_matrix *eta_noise_cov){\n\n"
		if(nregime > 1){
			ret <- paste(ret, "\tswitch (regime) {\n", sep="\n")
			for(reg in 1:nregime){
				ret <- paste0(ret, paste0("\t\tcase ", reg-1, ":"), "\n")
				ret <- paste(ret, setGslMatrixElements(values.latent[[reg]], params.latent[[reg]], "eta_noise_cov", depth=3), sep="\n")
				ret <- paste(ret, setGslMatrixElements(values.observed[[reg]], params.observed[[reg]], "y_noise_cov", depth=3), sep="\n")
				ret <- paste0(ret, "\t\t", "break;", "\n")
			} # close for loop
			ret <- paste0(ret, "\t", "}\n\n") # close case switch
		} else {
			ret <- paste(ret, setGslMatrixElements(values.latent[[1]], params.latent[[1]], "eta_noise_cov"), sep="\n")
			ret <- paste(ret, setGslMatrixElements(values.observed[[1]], params.observed[[1]], "y_noise_cov"), sep="\n")
		}
		ret <- paste(ret, "\n}\n\n") # close C function
		
		object@c.string <- ret
		
		return(object)
	}
)

setMethod("writeCcode", "dynrTrans",
          function(object, covariates){
            #function_transform
            ret="/**\n * This function modifies some of the parameters so that it satisfies the model constraint.\n * Do not include parameters in noise_cov matrices \n */\nvoid function_transform(double *param){"
            n=length(object$formula.trans)
            if(n == 0){
              object@c.string <- paste0(ret, "\n}\n\n")
              return(object)
            }else if (object@transCcode){
              formula.trans=object$formula.trans
              fml=processFormula(formula.trans)
              lhs=lapply(fml,function(x){x[1]})
              rhs=lapply(fml,function(x){x[2]})
              
              for (i in 1:n){
                ret=paste(ret,paste0(lhs[i],"=",rhs[i],";"),sep="\n\t") 
              }
            }
            
            ret=paste0(ret,"\n\t}\n")
            object@c.string <- ret
            return(object)
          }
)

setGeneric("createRfun", function(object, param.data, params.observed, params.latent, params.inicov,
                                  values.observed, values.latent, values.inicov,
                                  values.observed.orig, values.latent.orig, values.inicov.orig, show=TRUE) { 
  return(standardGeneric("createRfun")) 
})

setMethod("createRfun", "dynrTrans",
          function(object, param.data, params.observed, params.latent, params.inicov,
                   values.observed, values.latent, values.inicov,
                   values.observed.orig, values.latent.orig, values.inicov.orig){
            #inv.tfun
            inv.tf <- NULL
            tf <- NULL
            f.string<-"inv.tf<-function(vec){"
            if (length(object@formula.inv) > 0){
              #TODO If formula.inv is missing (i.e. length == 0), point-wise inverse functions that are based on the uniroot function will be used to calculate starting values.
              #inverse = function (f, lower = -100, upper = 100) {function (y) uniroot((function (x) f(x) - y), lower = lower, upper = upper)$root}
              #eval(parse(text="inv.tf<-function(namedvec){return(c(a=inverse(exp)(namedvec[\"a\"]),b=inverse(function(x)x^2,0.0001,100)(namedvec[\"b\"])))}"))
              fml.str=formula2string(object@formula.inv)
              lhs=fml.str$lhs
              rhs=fml.str$rhs
              sub=sapply(lhs,function(x){paste0("vec[",param.data$param.number[param.data$param.name==x],"]")})
              for (j in 1:length(rhs)){
                for (i in 1:length(lhs)){
                  rhs[j]=gsub(paste0("\\<",lhs[i],"\\>"),sub[i],rhs[j])
                }
                eq=paste0(sub[j],"=",rhs[j])
                f.string<-paste(f.string,eq,sep="\n\t")
              }
            }
            f.string2 <- f.string
            #observed
            if (sum(param.data$ldl.observed)>0){
            f.string2 <- paste(f.string2, makeldlchar(param.data, lapply(values.observed.orig, replaceDiagZero), params.observed, reverse=TRUE),sep="\n\t")
            }
            #latent
            if (sum(param.data$ldl.latent)>0){
            f.string2 <- paste(f.string2, makeldlchar(param.data, lapply(values.latent.orig, replaceDiagZero), params.latent, reverse=TRUE),sep="\n\t")
            }
            #inicov
            if (sum(param.data$ldl.inicov)>0){
            f.string2 <- paste(f.string2, makeldlchar(param.data, lapply(values.inicov.orig, replaceDiagZero), params.inicov, reverse=TRUE),sep="\n\t")
            }
            f.string2<-paste0(f.string2,"\n\treturn(vec)}")
            f.string<-paste0(f.string,"\n\treturn(vec)}")
            
            eval(parse(text=f.string2))
            object@inv.tfun.full <- inv.tf
            
            eval(parse(text=f.string)) 
            object@inv.tfun <- inv.tf
            
            f.string<-"tf<-function(vec){"
              
              if (length(object@formula.trans)>0){
              fml.str=formula2string(object@formula.trans)
              lhs=fml.str$lhs
              rhs=fml.str$rhs
              sub=sapply(lhs,function(x){paste0("vec[",param.data$param.number[param.data$param.name==x],"]")})
              
              for (j in 1:length(rhs)){
                for (i in 1:length(lhs)){
                  rhs[j]=gsub(paste0("\\<",lhs[i],"\\>"),sub[i],rhs[j])
                }
                eq=paste0(sub[j],"=",rhs[j])
                f.string<-paste(f.string,eq,sep="\n\t")
              }
              object@paramnames<-lhs
              }
            
            #observed
            if (sum(param.data$ldl.observed)>0){
              f.string<-paste(f.string, makeldlchar(param.data, values.observed, params.observed),sep="\n\t")
            }
            #latent
            if (sum(param.data$ldl.latent)>0){
              f.string<-paste(f.string, makeldlchar(param.data, values.latent, params.latent),sep="\n\t")
            }
            #inicov
            if (sum(param.data$ldl.inicov)>0){
              f.string<-paste(f.string, makeldlchar(param.data, values.inicov, params.inicov),sep="\n\t")
            }
              
            f.string<-paste0(f.string,"\n\treturn(vec)}")
            eval(parse(text=f.string))   
            object@tfun <- tf
            
            return(object)
          }
)

makeldlchar<-function(param.data, values, params, reverse=FALSE){
  #Note: Because we need to gurantee the uniqueness of the parameter values in estimation after applying reverseldl transformations, 
  #there should be no intersections between the parameter names used in different matrices that are subject to the ldl transformations
  #See below for an example where the (2,2) parameters are the same in two original matrices but the reverseldl-ed parameters differ.
  #This is problematic in estimation. We should treat the parameters in matrix(c(2,1,2,5) and matrix(c(3,1,2,5) as two different sets of parameters.
  #Also, the noise structure can only be either fully freed or with diagonals freed.
  #   > dynr:::reverseldl(matrix(c(2,1,2,5),ncol=2))
  #   [,1]     [,2]
  #   [1,] 0.6931472 0.000000
  #   [2,] 1.0000000 1.098612
  #   > dynr:::reverseldl(matrix(c(3,1,2,5),ncol=2))
  #   [,1]     [,2]
  #   [1,] 1.0986123 0.000000
  #   [2,] 0.6666667 1.299283
  params.numbers <- lapply(params, .exchangeNamesAndNumbers, param.data$param.name)
  values <- PopBackMatrix(values, params.numbers, paste0("vec[",param.data$param.number,"]"))
  char <- character(0)
  char.vec <- character(0)
  if (length(values) > 0){
    for (i in 1:length(values)){
      param.number <- unique(params.numbers[[i]][which(params.numbers[[i]]!=0,arr.ind = TRUE)])
      if (length(param.number)!=0){
        vec.noise <- paste0("vec[",paste0("c(",paste(param.number,collapse=","),")"),"]")
        vec.sub <- as.vector(values[[i]])
        mat.index <- sapply(param.number,function(x){min(which(params.numbers[[i]]==x))})
        char.i <- paste0(vec.noise,"=as.vector(", ifelse(reverse, 'reverseldl', 'transldl'), "(matrix(", paste0("c(",paste(vec.sub,collapse=","),")"),",ncol=",ncol(values[[i]]),")))[",
                      paste0("c(",paste(mat.index,collapse=","),")"),"]", ifelse(i!=length(values),"\n\t",""))
        char.vec <- c(char.vec, char.i)
      }
    }
    # Check for and remove any duplicates
    dups <- duplicated(gsub('\n\t$', '', char.vec))
    if(length(char.vec) > 1 && any(dups)){
      char.vec <- char.vec[!dups]
    }
    char <- paste0(char.vec, collapse='')
  }
  return(char)
}

vec2mat<-function(vectr,dimension){
  n.vec=length(vectr)
  if (n.vec==dimension){
    #diagonal
    mat=diag(vectr)
  }else if (dimension==sqrt(2*n.vec+.25) - .5){
    #symmetric
    mat=matrix(0,dimension,dimension)
    diag(mat)<-vectr[1:dimension]
    for (i in 1:(dimension-1)){
      for (j in (i+1):dimension){
        mat[i,j]<-vectr[i+j+dimension-2]
        mat[j,i]<-vectr[i+j+dimension-2]
      }
    }
  }else{
    cat('Length of the vector does not match the matrix dimension!\n')
  }	
  return(mat)	
}

mat2vec<-function(mat,n.vec){
  dimension=dim(mat)[1]
  vec=numeric(n.vec)
  if (n.vec==dimension){
    #diagonal
    vec=diag(mat)
  }else if (dimension==sqrt(2*n.vec+.25) - .5){
    #symmetric
    vec[1:dimension]<-diag(mat)
    for (i in 1:(dimension-1)){
      for (j in (i+1):dimension){
        vec[i+j+dimension-2]<-mat[i,j]
      }
    }
  }else{
    cat('Length of the vector does not match the matrix dimension!\n')
  }	
  return(vec)	
}
#transldl function for caluaclating the LDL values
transldl <- function(mat){
  L <- mat
  diag(L) <- 1
  L[upper.tri(L)] <- 0
  D <- diag(exp(diag(mat)), nrow=nrow(mat))
  # final caluclation
  outldl <- L %*% D %*% t(L)
  return(outldl)
}


##' LDL Decomposition for Matrices
##' @param x a numeric matrix
##'
##' This is a wrapper function around the \code{\link{chol}} function.
##' The goal is to factor a square, symmetric, positive (semi-)definite matrix into the product of a lower triangular matrix, a diagonal matrix, and the transpose of the lower triangular matrix.
##' The value returned is a lower triangular matrix with the elements of D on the diagonal.
dynr.ldl <- function(x){
	ret <- t(chol(x))
	d <- diag(ret)
	ret <- ret %*% diag(1/d, nrow=length(d))
	diag(ret) <- d^2
	return(ret)
}

reverseldl <- function(values){
	if(dim(values)[1]==1){
		return(log(values))
	} else if(any(is.na(values))){
		if(all(is.na(values))){
			# if everything is NA, then we're giving up.
			return(values)
		} else if(is.matrix(values) && all(is.na(values[lower.tri(values, diag=FALSE)]))){
			# if it's a matrix and all the lower triangular parts are NA
			# then we're just setting bounds on the variances
			values[lower.tri(values, diag=FALSE)] <- 0
			values[upper.tri(values, diag=FALSE)] <- 0
			mat <- dynr.ldl(values)
			diag(mat) <- log(diag(mat))
			mat[lower.tri(mat, diag=FALSE)] <- NA
			return(mat)
		} else if(is.matrix(values) && all(is.na(diag(values))) && all( values[lower.tri(values, diag=FALSE)] == 0)){
			# if the matrix has NA diagonal and is otherwise 0
			# then leave it alone
			return(values)
		} else{
			# only warn when values is
			#  1 not all missing
			#  2 not missing everywhere except the diagonal
			#  3 not missing on the diagonal with zero everywhere else
			warning("Avast ye swarthy dog! NA was passed to LDL in confusing way. Not doing LDL.")
			return(values)
		}
	} else{
		mat <- dynr.ldl(values)
		diag(mat) <- log(diag(mat))
		return(mat)
	}
}

#------------------------------------------------------------------------------
# Some usefull helper functions
#

##' Create a diagonal matrix from a character vector
##' @aliases diag.character diag
##' 
##' @param x Character vector used to create the matrix
##' @param nrow Numeric. Number of rows for the resulting matrix.
##' @param ncol Numeric. Number of columns for the resulting matrix.
##' 
##' @details
##' The default behavior for missing \code{nrow} and/or \code{ncol} arguments is the same
##' as for the \code{\link{diag}} function in the base package.  Off-diagonal entries
##' are filled with "0".
##' 
##' @examples
##' diag(letters[1:3])
# a new method for diag with character input
setMethod("diag", "character", #diag.character <-
	function(x=1, nrow, ncol){
		n <- length(x)
		if(!missing(nrow)) n <- nrow
		if(missing(ncol)) ncol <- n
		r <- matrix("0", n, ncol)
		diag(r) <- x
		return(r)
	}
)


# allow free/fixed to be used in values/params arguments
preProcessValues <- function(x){
	x[is.na(x)] <- 'freed'
	if(is.null(dim(x))){
		numRow <- length(x)
		numCol <- 1
		rowNam <- NULL
		colNam <- NULL
	} else {
		numRow <- nrow(x)
		numCol <- ncol(x)
		rowNam <- rownames(x)
		colNam <- colnames(x)
	}
	x <- c(x)
	xl <- tolower(x)
	sel <- pmatch(xl, "freed", duplicates.ok=TRUE)
	notAnumber <- is.na(sapply(x, function(x){suppressWarnings(try(as.numeric(x), silent=TRUE))}))
	if(any( pchars <- (is.na(sel) & notAnumber))){
		msg <- paste0("The following are not numbers or some version of 'freed': ", paste(x[pchars], collapse=', '), "\nEnter numerical starting values for free parameters, and 0 or other meaningful values for fixed parameters.")
		stop(msg)
	}
	# Set starting values to zero for 'free'
	x[sel %in% 1] <- 0
	x <- matrix(as.numeric(x), numRow, numCol, dimnames=list(rowNam, colNam))
	return(x)
}

preProcessParams <- function(x){
	if (!is.null(x)) {x[is.na(x)] <- 'fixed'}
	if(is.null(dim(x))){
		numRow <- length(x)
		numCol <- 1
		rowNam <- NULL
		colNam <- NULL
	} else {
		numRow <- nrow(x)
		numCol <- ncol(x)
		rowNam <- rownames(x)
		colNam <- colnames(x)
	}
	x <- c(x)
	xl <- tolower(x)
	sel <- xl %in% c("0", "fix", "fixed")
	x[sel] <- "fixed"
	validName <- sapply(x, function(x){x == make.names(x)})
	if(any(!validName)){
		msg <- paste0("Whoa nelly! The following are not valid free parameter names: ", paste(x[!validName], collapse=", "), ".\n  Valid names are things that could be variable names in R.  See ?make.names")
		stop(msg)
	}
	x <- matrix(as.character(x), numRow, numCol, dimnames=list(rowNam, colNam))
	return(x)
}

coProcessValuesParams <- function(values=NULL, params=NULL, missingOK=FALSE){
	if(is.null(values) || missing(values)){
		if(missingOK){
			values <- list()
			params <- list()
		} else {
			stop("values missing and it's not okay.")
		}
	}
	if(!is.list(values)){
		values <- list(values)
	}
	if(is.null(params) || missing(params)){
		params <- rep(list(matrix(0, nrow(values[[1]]), ncol(values[[1]]))), length(values))
	}
	if(!is.list(params)){
		params <- list(params)
	}
	if(length(values) != length(params)){
		stop(paste0("Mismatch between values and params.  'values' argument indicates ", length(values), " regimes but 'params' argument indicates ", length(params), " regimes.  Get your mind right."))
	}
	vdim <- sapply(lapply(values, as.matrix), dim)
	pdim <- sapply(lapply(params, as.matrix), dim)
	if(length(vdim) > 0 && apply(vdim, 1, function(x) length(unique(x)) > 1)){
		stop("Some of the 'values' list elements are not the same size as each other\nNot cool, Donny.")
	}
	if(length(pdim) > 0 && apply(pdim, 1, function(x) length(unique(x)) > 1)){
		stop("Some of the 'params' list elements are not the same size as each other\nNo-go for launch.")
	}
	if(any(vdim != pdim)){
		stop("'values' and 'params' are not all the same size.\nWalter Sobchak says you can't do that.")
	}
	return(list(values=values, params=params))
}

symmExtract <- function(x, s){if(s){x[lower.tri(x, diag=TRUE)]}else{x}}

checkMultipleStart <- function(values, params, symmetric=FALSE){
	if(is.list(values) & is.list(params)){
		values <- unlist(lapply(values, symmExtract, s=symmetric))
		params <- unlist(lapply(params, symmExtract, s=symmetric))
	} else if(is.matrix(values) && is.matrix(params)){
		values <- c(symmExtract(values, symmetric))
		params <- c(symmExtract(params, symmetric))
	} else if(xor(is.list(values), is.list(params)) || xor(is.matrix(values), is.matrix(params))){
		stop("Invalid input to checkMultipleStart() function.\nMust be a pair of vectors, a pair of lists, or a pair of matrices.\nFound a mix.")
	}
	names(values) <- params
	chparam <- setdiff(unique(params), c("0", "fixed")) # Don't check parameters labeled "fixed" or "0"
	for(i in chparam){
		ch <- values[ names(values) %in% i]
		if(sum(!duplicated(ch)) > 1){
			stop(paste0("Found multiple (transformed) start values for parameter '", i, "': ", paste(ch, sep="", collapse=", ")), call.=FALSE)
		}
	}
}


preProcessNames <- function(x, rnames=character(0), cnames=character(0), xname=character(0), rarg=character(0), carg=character(0)){
	d <- dim(x)
	if(d[1] != length(rnames)){
		stop(paste0("Matrix ", xname, " has ", d[1], " rows and ", length(rnames), " ", rarg, " (i.e., row names).\nHint: they should match.\nNot even the King of Pop could set these row names."), call.=FALSE)
	}
	rownames(x) <- rnames
	if(!(length(cnames)==0 & length(carg)==0) && d[2] != length(cnames)){
	stop(paste0("Matrix ", xname, " has ", d[2], " columns and ", length(cnames), " ", carg, " (i.e., column names).\nHint: they should match.\nNot even the King of Soul could set these column names."), call.=FALSE)
	}
	if(!length(cnames)==0 & length(carg)==0) colnames(x) <- cnames
	return(x)
}

extractWhichParams <- function(p){
	p!="fixed" & !duplicated(p, MARGIN=0) & (p!=0)
}

extractParams <- function(p){
	if(is.list(p) && length(p) > 0){
		ret <- c()
		for(i in 1:length(p)){
			ret <- c(ret, extractParams(p[[i]]))
		}
		return(ret)
	} else if(length(p) == 0){
		return(character(0))
	}else {
		return(p[extractWhichParams(p)])
	}
}

extractValues <- function(v, p, symmetric=FALSE){
	if(is.list(v) && is.list(p) && length(v) == length(p) && length(v) > 0){
		ret <- c()
		for(i in 1:length(v)){
			ret <- c(ret, extractValues(v[[i]], p[[i]], symmetric))
		}
		return(ret)
	} else if(length(v) == 0){
		return(numeric(0))
	}else {
		checkMultipleStart(v, p, symmetric)
		return(v[extractWhichParams(p)])
	}
}

#------------------------------------------------------------------------------
# Create recipe for function measurement


#--------------------------------------
# brief input version

##' Recipe function to quickly create factor loadings
##'
##' @param map list giving how the latent variables map onto the observed variables
##' @param params parameter numbers
##' @param idvar names of the variables used to identify the factors
##' @param exo.names names of the exogenous covariates
##' @param intercept logical. Whether to include freely esimated intercepts
##' 
##' @details
##' The default pattern for 'idvar' is to fix the first factor loading 
##' for each factor to one.  The variable names listed in 'idvar' have 
##' their factor loadings fixed to one.  However, if the names of the 
##' latent variables are used for 'idvar', then all the factor loadings
##' will be freely estimated and you should fix the factor variances 
##' in the noise part of the model (e.g. \code{\link{prep.noise}}).
##'
##' This function does not have the full set of features possible in 
##' the dynr package. In particular, it does not have any regime-swtiching.
##' Covariates can be included with the \code{exo.names} argument, but
##' all covariate effects are freely estimated and the starting values
##' are all zero.  Likewise, intercepts can be included with the \code{intercept}
##' logical argument, but all intercept terms are freely estimated with 
##' zero as the starting value.
##' For complete functionality use \code{\link{prep.measurement}}.
##' 
##' @examples
##' #Single factor model with one latent variable fixing first loading
##' prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 2:4))
##'
##' #Single factor model with one latent variable fixing the fourth loading
##' prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 1:3), idvar='y4')
##' 
##' #Single factor model with one latent variable freeing all loadings
##' prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 1:4), idvar='eta1')
##' 
##' #Single factor model with one latent variable fixing first loading
##' # and freely estimated intercept
##' prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 2:4),
##'  intercept=TRUE)
##' 
##' #Single factor model with one latent variable fixing first loading
##' # and freely estimated covariate effects for u1 and u2
##' prep.loadings(list(eta1=paste0('y', 1:4)), paste0("lambda_", 2:4),
##'  exo.names=paste0('u', 1:2))
##' 
##' # Two factor model with simple structure
##' prep.loadings(list(eta1=paste0('y', 1:4), eta2=paste0('y', 5:7)), 
##' paste0("lambda_", c(2:4, 6:7)))
##' 
##' #Two factor model with repeated use of a free parameter
##' prep.loadings(list(eta1=paste0('y', 1:4), eta2=paste0('y', 5:8)), 
##' paste0("lambda_", c(2:4, 6:7, 4)))
##' 
##' #Two factor model with a cross loading
##' prep.loadings(list(eta1=paste0('y', 1:4), eta2=c('y5', 'y2', 'y6')), 
##' paste0("lambda_", c("21", "31", "41", "22", "62")))
prep.loadings <- function(map, params, idvar, exo.names=character(0), intercept=FALSE){
	if(missing(idvar)){
		idvar <- sapply(map, '[', 1)
	}
	
	allVars <- unique(unlist(map))
	
	nx <- length(allVars)
	ne <- length(map)
	nu <- length(exo.names)
	
	if(!all(idvar %in% c(names(map), unlist(map)))){
		stop("The 'idvar' must all be either in the names of the 'map' argument or parts of the 'map' argument.")
	}
	
	paramsNeeded <- length(unlist(map)) - sum(idvar %in% allVars)
	if(length(params) != paramsNeeded){
		stop(paste0("Number of free parameters provided (", length(params), ") does not match number needed (", paramsNeeded, ")."))
	}
	
	
	k <- 1
	valuesMat <- matrix(0, nx, ne)
	paramsMat <- matrix(0, nx, ne)
	for(j in 1:ne){
		for(i in 1:nx){
			if(allVars[i] %in% map[[j]] & !(allVars[i] %in% idvar)){
				paramsMat[i, j] <- params[k]
				valuesMat[i, j] <- .8
				k <- k+1
			} else if(allVars[i] %in% map[[j]] & allVars[i] %in% idvar){
				valuesMat[i, j] <- 1
			}
		}
	}
	
	if(nu > 0){
		exoVal <- matrix(0, nx, nu)
		exoPar <- outer(paste0("b_", allVars), exo.names, paste0)
	} else {
		exoVal <- NULL
		exoPar <- NULL
	}
	
	if(intercept){
		intVal <- matrix(0, nx, 1)
		intPar <- matrix(paste0("int_", allVars), nx, 1)
	}else{
		intVal <- NULL
		intPar <- NULL
	}
	
	rownames(valuesMat) <- allVars
	rownames(paramsMat) <- allVars
	colnames(valuesMat) <- names(map)
	colnames(paramsMat) <- names(map)
	x <- prep.measurement(values.load=valuesMat, params.load=paramsMat, values.exo=exoVal, params.exo=exoPar, values.int=intVal, params.int=intPar, state.names=names(map), obs.names=allVars, exo.names=exo.names)
	return(x)
}



#--------------------------------------
# matrix input version

# values, and params are all MxN matrices
# a zero param is taken to be fixed.

##' Prepare the measurement recipe
##'
##' @param values.load matrix of starting or fixed values for factor loadings. 
##' For models with regime-specific factor loadings provide a list of matrices of factor loadings.
##' @param params.load matrix or list of matrices. Contains parameter names of the factor loadings. 
##' @param values.exo matrix or list of matrices. Contains starting/fixed values of the covariate regression slopes.
##' @param params.exo matrix or list of matrices. Parameter names of the covariate regression slopes.
##' @param values.int vector of intercept values specified as matrix or list of matrices. Contains starting/fixed values of the intercepts.
##' @param params.int vector of names for intercept parameters specified as a matrix or list of matrices. 
##' @param obs.names  vector of names for the observed variables in the order they appear in the measurement model.
##' @param state.names  vector of names for the latent variables in the order they appear in the measurement model.
##' @param exo.names  (optional) vector of names for the exogenous variables in the order they appear in the measurement model.
##'
##' @details
##' The values.* arguments give the starting and fixed values for their respective matrices.
##' The params.* arguments give the free parameter labels for their respective matrices.
##' Numbers can be used as labels.
##' The number 0 and the character 'fixed' are reserved for fixed parameters.
##'
##' When a single matrix is given to values.*, that matrix is not regime-switching.
##' Correspondingly, when a list of length r is given, that matrix is regime-switching with values and params for the r regimes in the elements of the list.
##' 
##' @seealso 
##' Methods that can be used include: \code{\link{print}}, \code{\link{printex}}, \code{\link{show}} 
##'
##' @examples
##' prep.measurement(diag(1, 5), diag("lambda", 5))
##' prep.measurement(matrix(1, 5, 5), diag(paste0("lambda_", 1:5)))
##' prep.measurement(diag(1, 5), diag(0, 5)) #identity measurement model
##' 
##' #Regime-switching measurement model where the first latent variable is
##' # active for regime 1, and the second latent variable is active for regime 2
##' # No free parameters are present.
##' prep.measurement(values.load=list(matrix(c(1,0), 1, 2), matrix(c(0, 1), 1, 2)))
prep.measurement <- function(values.load, params.load=NULL, values.exo=NULL, params.exo=NULL, values.int=NULL, params.int=NULL,
                             obs.names, state.names, exo.names){
	# Handle load
	r <- coProcessValuesParams(values.load, params.load)
	values.load <- r$values
	params.load <- r$params
	
	# Handle exo
	r <- coProcessValuesParams(values.exo, params.exo, missingOK=TRUE)
	values.exo <- r$values
	params.exo <- r$params
	
	# Handle int
	r <- coProcessValuesParams(values.int, params.int, missingOK=TRUE)
	values.int <- r$values
	params.int <- r$params
	
	
	if(missing(obs.names)){
		obs.names = paste0('y',1:nrow(values.load[[1]]))
	}
	
	if(missing(state.names)){
		state.names = paste0('state',1:ncol(values.load[[1]]))
	}
	
	if(missing(exo.names)){
		exo.names = character(0)
	}
	if(any(duplicated(obs.names))){
		stop("'obs.names' uses some of the same names more than once.\n  I repeat myself when under stress.  I repeat myself when under stress.  I repeat myself when under stress.\nI repeat myself when under stress.  I repeat myself when under stress.")
	}
	if(any(duplicated(state.names))){
		stop("'state.names' uses some of the same names more than once.\n  Sensei says this lacks discipline.")
	}
	if(any(duplicated(exo.names))){
		stop("'exo.names' uses some of the same names more than once.\n  You cannot be the King of Crimson with this indiscipline.")
	}
	
	values.load <- lapply(values.load, preProcessValues)
	params.load <- lapply(params.load, preProcessParams)
	values.exo <- lapply(values.exo, preProcessValues)
	params.exo <- lapply(params.exo, preProcessParams)
	values.int <- lapply(values.int, preProcessValues)
	params.int <- lapply(params.int, preProcessParams)
	
	values.load <- lapply(values.load, preProcessNames, obs.names, state.names, 'values.load', 'obs.names', 'state.names')
	#params.load <- lapply(params.load, preProcessNames, obs.names, state.names, 'values.load', 'obs.names', 'state.names')
	values.exo <- lapply(values.exo, preProcessNames, obs.names, exo.names, 'values.exo', 'obs.names', 'exo.names')
	#params.exo <- lapply(params.exo, preProcessNames, obs.names, exo.names, 'values.exo', 'obs.names', 'exo.names')
	values.int <- lapply(values.int, preProcessNames, obs.names, character(0), 'values.int', 'obs.names')
	#params.int <- lapply(params.int, preProcessNames, obs.names, 'values.int', 'obs.names')
	
	sv <- c(extractValues(values.load, params.load), extractValues(values.exo, params.exo), extractValues(values.int, params.int))
	pn <- c(extractParams(params.load), extractParams(params.exo), extractParams(params.int))
	sv <- extractValues(sv, pn)
	pn <- extractParams(pn)
	
	x <- list(startval=sv, paramnames=pn, values.load=values.load, params.load=params.load,
		values.exo=values.exo, params.exo=params.exo, values.int=values.int, params.int=params.int,
		obs.names=obs.names, state.names=state.names, exo.names=exo.names)
	return(new("dynrMeasurement", x))
}


#------------------------------------------------------------------------------
# Error covariance matrix
# N.B. This function produces BOTH the latent and observed error covariance matrices.


##' Recipe function for specifying the measurement error and process noise covariance structures
##' 
##' @param values.latent a positive definite matrix or a list of positive definite matrices of the starting or fixed values of the process noise covariance structure(s) in one or more regimes. If only one matrix is specified for a regime-switching dynamic model, the process noise covariance structure stays the same across regimes. To ensure the matrix is positive definite in estimation, we apply LDL transformation to the matrix. Values are hence automatically adjusted for this purpose.
##' @param params.latent a matrix or list of matrices of the parameter names that appear in the process noise covariance(s) in one or more regimes. If an element is 0 or "fixed", the corresponding element is fixed at the value specified in the values matrix; Otherwise, the corresponding element is to be estimated with the starting value specified in the values matrix. If only one matrix is specified for a regime-switching dynamic model, the process noise structure stays the same across regimes. If a list is specified, any two sets of the parameter names as in two matrices should be either the same or totally different to ensure proper parameter estimation.  See Details.
##' @param values.observed a positive definite matrix or a list of positive definite matrices of the starting or fixed values of the measurement error covariance structure(s) in one or more regimes. If only one matrix is specified for a regime-switching measurement model, the measurement noise covariance structure stays the same across regimes. To ensure the matrix is positive definite in estimation, we apply LDL transformation to the matrix. Values are hence automatically adjusted for this purpose. 
##' @param params.observed a matrix or list of matrices of the parameter names that appear in the measurement error covariance(s) in one or more regimes. If an element is 0 or "fixed", the corresponding element is fixed at the value specified in the values matrix; Otherwise, the corresponding element is to be estimated with the starting value specified in the values matrix. If only one matrix is specified for a regime-switching dynamic model, the process noise structure stays the same across regimes. If a list is specified, any two sets of the parameter names as in two matrices should be either the same or totally different to ensure proper parameter estimation.  See Details.
##' 
##' @details
##' The arguments of this function should generally be either matrices or lists of matrices.  Lists of matrices are used for regime-switching models with each list element corresponding to a regime.  Thus, a list of three matrices implies a three-regime model.  Single matrices are for non-regime-switching models.  Some checking is done to ensure that the number of regimes implied by one part of the model matches that implied by the others.  For example, the noise model (\code{prep.noise}) cannot suggest three regimes when the measurement model (\code{\link{prep.measurement}}) suggests two regimes. An exception to this rule is single-regime (i.e. non-regime-switching) components.  For instance, the noise model can have three regimes even though the measurement model implies one regime.  The single-regime components are simply assumed to be invariant across regimes.
##' 
##' Care should be taken that the parameters names for the latent covariances do not overlap with the parameters in the observed covariances.  Likewise, the parameter names for the latent covariances in each regime should either be identical or completely distinct. Because the LDL' transformation is applied to the covariances, sharing a parameter across regimes may cause problems with the parameter estimation.
##' 
##' Use $ to show specific arguments from a dynrNoise object (see examples).
##' @seealso 
##' \code{\link{printex}} to show the covariance matrices in latex.
##'  
##' @examples 
##' # Two latent variables and one observed variable in a one-regime model
##' Noise<-prep.noise(values.latent=diag(c(0.8, 1)), params.latent=diag(c('fixed', "e_x")), 
##' values.observed=diag(1.5,1), params.observed=diag("e_y", 1))
##' # For matrices that can be import to latex:
##' printex(Noise,show=TRUE)
##' # If you want to check specific arguments you've specified, for example,
##' # values for variance structure of the latent variables
##' Noise$values.latent
##' # [[1]]
##' #     [,1] [,2]
##' # [1,]  0.8    0
##' # [2,]  0.0    1
##' 
##' # Two latent variables and one observed variable in a two-regime model
##' Noise<-prep.noise(values.latent=list(diag(c(0.8, 1)),diag(c(0.8, 1))), 
##' params.latent=list(diag(c('fixed', "e_x1")),diag(c('fixed', "e_x2"))), 
##' values.observed=list(diag(1.5,1),diag(0.5,1)), 
##' params.observed=list(diag("e_y1", 1),diag("e_y2",1)))
##' # If the error and noise structures are assumed to be the same across regimes,
##' #  it is okay to use matrices instead of lists.
prep.noise <- function(values.latent, params.latent, values.observed, params.observed){
	# Handle latent covariance
	r <- coProcessValuesParams(values.latent, params.latent)
	values.latent <- r$values
	params.latent <- r$params
	
	# Handle observed covariance
	r <- coProcessValuesParams(values.observed, params.observed)
	values.observed <- r$values
	params.observed <- r$params
	
	if(length(values.observed) != length(values.latent)){
		msg <- paste0("Stuff and nonsense: number of regimes for observed covariance (", length(values.observed),
			") is not equal to that for the latent covariance(", length(values.latent), ").")
		stop(msg)
	}
	
	values.latent <- lapply(values.latent, preProcessValues)
	params.latent <- lapply(params.latent, preProcessParams)
	values.observed <- lapply(values.observed, preProcessValues)
	params.observed <- lapply(params.observed, preProcessParams)
	
	lapply(values.latent, checkSymmetric, name="values.latent")
	lapply(params.latent, checkSymmetric, name="params.latent")
	lapply(values.observed, checkSymmetric, name="values.observed")
	lapply(params.observed, checkSymmetric, name="params.observed")
	
	values.latent.inv.ldl <- lapply(values.latent, replaceDiagZero)
	values.latent.inv.ldl <- lapply(values.latent.inv.ldl, reverseldl)
	values.observed.inv.ldl <- lapply(values.observed, replaceDiagZero)
	values.observed.inv.ldl <- lapply(values.observed.inv.ldl, reverseldl)
	
	sv <- c(extractValues(values.latent.inv.ldl, params.latent, symmetric=TRUE), extractValues(values.observed.inv.ldl, params.observed, symmetric=TRUE))
	pn <- c(extractParams(params.latent), extractParams(params.observed))
	sv <- extractValues(sv, pn)
	pn <- extractParams(pn)
	x <- list(startval=sv, paramnames=pn, values.latent=values.latent, values.observed=values.observed, params.latent=params.latent, params.observed=params.observed, values.latent.inv.ldl=values.latent.inv.ldl, values.observed.inv.ldl=values.observed.inv.ldl)
	return(new("dynrNoise", x))
}


replaceDiagZero <- function(x){
	diag(x)[diag(x) == 0] <- 1e-6
	return(x)
}

checkSymmetric <- function(m, name="matrix"){
	if(any(m != t(m))){
		msg <- paste0("Covariance matrix called '", name, "' is not symmetric.\n",
			"Covariance matrices should be equal to their transposes.")
		stop(msg)
	}
}


#------------------------------------------------------------------------------
# Regime switching matrix/function

# For regime switching function, each element of the transition probability
# matrix should be the result of a multinomial logistic regression.
# That is, for a 2x2 matrix we have
# Behind the scenes it does this
#   p11 ~ softmax(1 + x1 + x2 + ... + xn)
#   p21 ~ softmax(1 + x1 + x2 + ... + xn)
#   p12 ~ softmax(1 + x1 + x2 + ... + xn)
#   p22 ~ softmax(1 + x1 + x2 + ... + xn)
#
# Users specify this
#   p11 ~ 1 + x1 + x2
#   p21 ~ 1 + x2 + x3

# Perhaps have two regime functions
# One is analogous to the linear dynamics function, and has more limited interface
# The other may be more general.
# Even the limited one should be able to handle covariates as above via multinomial logistic regression

# add identification constraints
# 

# Each element of the transition probability matrix (TPM) has a linear predictor (lp).
# LPM = 
# lp(p11) ~ 1 + x1 + x2 + ... + xn,   lp(p12) ~ 1 + x1 + x2 + ... + xn
# lp(p21) ~ 1 + x1 + x2 + ... + xn,   lp(p22) ~ 1 + x1 + x2 + ... + xn
# 
# The softmax function then operates on the columns of the linear predictor matrix (LPM).
# TPM = 
#       cbind( softmax(LPM[,1]), softmax(LPM[,2]), ..., softmax(LPM[,k]) )
# for k regimes

##' Recipe function for creating regime switching (Markov transition) functions
##' 
##' @param values matrix giving the values. Should have (number of Regimes) rows 
##' and (number of regimes x number of covariates) columns
##' @param params matrix of the same size as "values" consisting of the names of the parameters
##' @param covariates a vector of the names of the covariates to be used in the regime-switching functions
##' @param deviation logical. Whether to use the deviation form or not.  See Details.
##' @param refRow numeric. Which row is treated at the reference.  See Details.
##' 
##' @details
##' Note that each row of the transition probability matrix must sum to one. To accomplish this
##' fix at least one transition log odds parameter in each row of "values" (including its intercept 
##' and the regression slopes of all covariates) to 0.
##' 
##' When \code{deviation=FALSE}, the non-deviation form of the multinomial logistic regression is used. This form has a separate intercept term for each entry of the transition probability matrix (TPM). When \code{deviation=TRUE}, the deviation form of the multinomial logistic regression is used. This form has an intercept term that is common to each column of the TPM. The rows are then distinguished by their own individual deviations from the common intercept. The deviation form requires the same reference column constraint as the non-deviation form; however, the deviation form also requires one row to be indicated as the reference row (described below). By default the reference row is taken to be the same as the reference column.
##' 
##' The \code{refRow} argument determines which row is used as the intercept row. It is only
##' used in the deviation form (i.e. \code{deviation=TRUE}). In the deviation form, one row of \code{values} and \code{params} contains the intercepts, other rows contain deviations from these intercepts. The \code{refRow} argument says which row contains the intercept terms. The default behavior for \code{refRow} is to be the same as the reference column.  The reference column is automatically detected. If we have problems detecting which is the reference column, then we provide error messages that are as helpful as we can make them.
##' 
##' @seealso 
##' Methods that can be used include: \code{\link{print}}, \code{\link{printex}}, \code{\link{show}} 
##'
##' @examples
##' #Two-regime example with a covariate, x; log odds (LO) parameters represented in default form,
##' #2nd regime set to be the reference regime (i.e., have LO parameters all set to 0).
##' #The values and params matrices are of size 2 (numRegimes=2) x 4 (numRegimes*(numCovariates+1)).
##' #    The LO of staying within the 1st regime (corresponding to the (1,1) entry in the
##' #              2 x 2 transition probability matrix for the 2 regimes) = a_11 + d_11*x
##' #    The log odds of switching from the 1st to the 2nd regime (the (1,2) entry in the
##' #              transition probability matrix) = 0
##' #    The log odds of moving from regime 2 to regime 1 (the (2,1) entry) = a_21 + d_21*x
##' #    The log odds of staying within the 2nd regime (the (2,2) entry) = 0
##' b <- prep.regimes(
##' values=matrix(c(8,-1,rep(0,2),
##'                -4,.1,rep(0,2)),
##'              nrow=2, ncol=4, byrow=TRUE), 
##' params=matrix(c("a_11","d_11x",rep("fixed",2),
##'                "a_21","d_21x",rep("fixed",2)), 
##'              nrow=2, ncol=4, byrow=TRUE), covariates=c("x"))
##'  
##' # Same example as above, but expressed in deviation form by specifying 'deviation = TRUE'
##' #    The LO of staying within the 1st regime (corresponding to the (1,1) entry in the
##' #              2 x 2 transition probability matrix for the 2 regimes) = a_21 + a_11 + d_11*x
##' #    The log odds of switching from the 1st to the 2nd regime (the (1,2) entry in the
##' #              transition probability matrix) = 0
##' #    The log odds of moving from regime 2 to regime 1 (the (2,1) entry) = a_21 + d_21*x
##' #    The log odds of staying within the 2nd regime (the (2,2) entry) = 0            
##' b <- prep.regimes(
##' values=matrix(c(8,-1,rep(0,2),
##'                -4,.1,rep(0,2)),
##'              nrow=2, ncol=4, byrow=TRUE), 
##' params=matrix(c("a_11","d_11x",rep("fixed",2),
##'                "a_21","d_21x",rep("fixed",2)), 
##'              nrow=2, ncol=4, byrow=TRUE), covariates=c("x"), deviation = TRUE)
##'              
##' #An example of regime-switching with no covariates. The diagonal entries are fixed
##' #at zero for identification purposes
##' b <- prep.regimes(values=matrix(0, 3, 3), 
##' params=matrix(c('fixed', 'p12', 'p13', 
##'                 'p21', 'fixed', 'p23', 
##'                 'p31', 'p32', 'fixed'), 3, 3, byrow=TRUE))
##' 
##' #An example of regime-switching with no covariates. The parameters for the second regime are 
##' #  fixed at zero for identification purposes, making the second regime the reference regime.
##' b <- prep.regimes(values=matrix(0, 3, 3), 
##' params=matrix(c('p11', 'fixed', 'p13',
##'                 'p21', 'fixed', 'p23', 
##'                 'p31', 'fixed', 'p33'), 3, 3, byrow=TRUE))
##' 
##' #2 regimes with three covariates
##' b <- prep.regimes(values=matrix(c(0), 2, 8), 
##' params=matrix(c(paste0('p', 8:15), rep(0, 8)), 2, 8), 
##' covariates=c('x1', 'x2', 'x3'))
##' 
prep.regimes <- function(values, params, covariates, deviation=FALSE, refRow){
	if(!missing(values)){
		values <- preProcessValues(values)
	}
	if(!missing(params)){
		params <- preProcessParams(params)
	}
	if(missing(values)){
		values <- preProcessValues(matrix(0, 0, 0))
		params <- preProcessParams(matrix(0, 0, 0))
	}
	if(missing(covariates)){
		covariates <- character(0)
	}
	if(missing(refRow)){ # or other default values???
		refRow <- numeric(0)
	}
	
	if(!all(dim(values))==all(dim(params))) {
	  stop('The dimensions of values and params matrices must match.')
	}else if(!ncol(values ) == nrow(values)*(length(covariates)+1)){
	  stop(paste0("The matrix values should have ", nrow(values)*(length(covariates)+1), " columns."))
	}
	
	sv <- extractValues(values, params)
	pn <- extractParams(params)
	
	if(length(sv) > nrow(values)*(nrow(values)-1)*(length(covariates)+1)){
		stop("Regime transition probability parameters not uniquely identified.\nFix all parameters in at least one cell of each row of the \ntransition probability matrix to be zero.")
	}
	# Do lots or processing and checking for the reference row and column
	if(deviation == TRUE){
		# if needed set refRow
		if(length(refRow) == 0){
			# check valid reference column (i.e. there is a single column as reference, no diagonal identification)
			# detect reference column
			colIsZero <- apply(params, 2, function(x){all(x %in% c('fixed'))})
			blockSize <- length(covariates)+1
			colIsZeroM <- matrix(colIsZero, nrow=blockSize, ncol=nrow(values))
			refCol <- apply(colIsZeroM, 2, function(x){all(x==TRUE)})
			if(sum(refCol) < 1){ #no single reference column found
				stop(paste("No single reference column found. Identification along the diagonal is not allowed without you specifiy the reference row. Set e.g. refRow=1."))
			}
			if(sum(refCol) > 1){#found more than one reference column
				stop(paste0("Found multiple possible reference columns: ", paste(which(refCol==1), collapse=', '), ".  Reconsider your model specification or just set e.g. refRow=1."))
			}
			# set reference row to reference column
			refRow <- which(refCol)
		}
		if(length(refRow) > 1){
			# error reference row must have length 0 or 1
			stop(paste("refRow must being a single number. That is, have length 0 or 1, we found length", length(refRow)))
		}
		if(refRow > nrow(values)){
			# error reference row must be between 1 and nrow(values)
			stop(paste("refRow must be between 1 and", nrow(values), "but we found", refRow))
		}
	} else {
		if(length(refRow) > 0){
			#warning refRow is ignored when for the non-deviation case (deviation=FALSE)
			warning("refRow argument is ignored in the non-deviation case (i.e. when deviation=FALSE)")
		}
	}
	# Create the list for the object to return
	x <- list(startval=sv, paramnames=pn, values=values, params=params, covariates=covariates, deviation=deviation, refRow=refRow)
	return(new("dynrRegimes", x))
}



autojacob<-function(formula,n){
  #formula=list(x1~a*x1,x2~b*x2)
  tuple=lapply(formula,as.list)
  lhs=sapply(tuple,function(x){deparse(x[[2]])})
  rhs=sapply(tuple,function(x){x[[3]]})
  rhsj=vector("list", n*n)
  jacob=vector("list", n*n)
  for (i in 1:n){
    for (j in 1:n){
      rhsj[[(i-1)*n+j]]=paste0(deparse(D(rhs[[i]],lhs[[j]]),width.cutoff = 500L),collapse="")
      jacob[[(i-1)*n+j]]=as.formula(paste0(lhs[[i]],"~",lhs[[j]],"~",rhsj[[(i-1)*n+j]],collapse=""))
    }
  }
  return(list(row=as.list(rep(lhs,each=2)),col=as.list(rep(lhs,2)),rhsj=rhsj,jacob=jacob))
}

#------------------------------------------------------------------------------
# "Dynamics" functions

##' Recipe function for specifying dynamic functions using formulas
##' 
##' @param formula a list of formulas specifying the drift or state-transition 
##' equations for the latent variables in continuous or discrete time, respectively.
##' @param startval a named vector of starting values of the parameters in the 
##' formulas for estimation with parameter names as its name. If there are no free parameters in 
##' the dynamic functions, leave startval as the default \code{numeric(0)}.
##' @param isContinuousTime if True, the left hand side of the formulas represent 
##' the first-order derivatives of the specified variables; if False, the left hand 
##' side of the formulas represent the current state of the specified variable while 
##' the same variable on the righ hand side is its previous state.  
##' @param jacobian (optional) a list of formulas specifying the analytic jacobian matrices 
##' containing the analytic differentiation function of the dynamic functions with respect to
##' the latent variables. If this is not provided, dynr will invoke an automatic differentiation
##' procedure to compute the jacobian functions.
##' 
##' @details
##' This function defines the dynamic functions of the model either in discrete time or in continuous time.
##' The function can be either linear or nonlinear, with free or fixed parameters, numerical constants, 
##' covariates, and other mathematical functions that define the dynamics of the latent variables.
##' Every latent variable in the model needs to be defined by a differential (for continuous time model), or
##' difference (for discrete time model) equation.  The names of the latent variables should match 
##' the specification in prep.measurement().
##' For nonlinear models, the estimation algorithm generally needs a Jacobian matrix that contains
##' elements of first differentiations of the dynamic functions with respect to the latent variables
##' in the model. For most nonlinear models, such differentiations can be handled automatically by
##' dynr. However, in some cases, such as when the absolute function (abs) is used, the automatic
##' differentiation would fail and the user may need to provide his/her own Jacobian functions.
##'
##' @examples
##' # In this example, we present how to define the dynamics of a bivariate dual change score model
##' # (McArdle, 2009). This is a linear model and the user does not need to worry about 
##' # providing any jacobian function (the default). 
##'  
##' # We start by creating a list of formula that describes the model. In this model, we have four 
##' # latent variables, which are "readLevel", "readSlope", "mathLevel", and "math Slope".  The right-
##' # hand side of each formula gives a function that defines the dynamics.   
##'  
##'  formula =list(
##'           list(readLevel~ (1+beta.read)*readLevel + readSlope + gamma.read*mathLevel,
##'           readSlope~ readSlope,
##'           mathLevel~ (1+beta.math)*mathLevel + mathSlope + gamma.math*readLevel, 
##'           mathSlope~ mathSlope
##'           ))
##'
##' # Then we use prep.formulaDynamics() to define the formula, starting value of the parameters in
##' # the model, and state the model is in discrete time by setting isContinuousTime=FALSE.
##'  
##' dynm  <- prep.formulaDynamics(formula=formula,
##'                              startval=c(beta.read = -.5, beta.math = -.5, 
##'                                         gamma.read = .3, gamma.math = .03
##'                              ), isContinuousTime=FALSE)
##' 
##' 
##' # For a full demo example of regime switching nonlinear discrete time model, you
##' # may refer to a tutorial on 
##' # \url{https://quantdev.ssri.psu.edu/tutorials/dynr-rsnonlineardiscreteexample}
##' 
##' #Not run: 
##' #For a full demo example that uses user-supplied analytic jacobian functions see:
##' #demo(RSNonlinearDiscrete, package="dynr")
##' formula <- list(
##'     list(
##'       x1 ~ a1*x1,
##'       x2 ~ a2*x2),
##'     list(
##'       x1 ~ a1*x1 + c12*(exp(abs(x2)))/(1+exp(abs(x2)))*x2,
##'       x2 ~ a2*x2 + c21*(exp(abs(x1)))/(1+exp(abs(x1)))*x1)
##'   )
##' jacob <- list(
##'   list(x1~x1~a1,
##'       x2~x2~a2),
##'   list(x1~x1~a1,
##'       x1~x2~c12*(exp(abs(x2))/(exp(abs(x2))+1)+x2*sign(x2)*exp(abs(x2))/(1+exp(abs(x2))^2)),
##'       x2~x2~a2,
##'       x2~x1~c21*(exp(abs(x1))/(exp(abs(x1))+1)+x1*sign(x1)*exp(abs(x1))/(1+exp(abs(x1))^2))))
##' dynm <- prep.formulaDynamics(formula=formula, startval=c( a1=.3, a2=.4, c12=-.5, c21=-.5),
##'                              isContinuousTime=FALSE, jacobian=jacob)
##' 
##' #For a full demo example that uses automatic jacobian functions (the default) see:
##' #demo(RSNonlinearODE , package="dynr")
##' formula=list(prey ~ a*prey - b*prey*predator, predator ~ -c*predator + d*prey*predator)
##' dynm <- prep.formulaDynamics(formula=formula,
##'                           startval=c(a = 2.1, c = 0.8, b = 1.9, d = 1.1),
##'                           isContinuousTime=TRUE)
prep.formulaDynamics <- function(formula, startval = numeric(0), isContinuousTime=FALSE, jacobian){
  if(length(startval) > 0 & is.null(names(startval))){
    stop('startval must be a named vector')
  }
  # e.g. for the one-regime case, if we get a list of formula, make a list of lists of formula
  if(is.list(formula) && plyr::is.formula(formula[[1]])){
    formula <- list(formula)
  }
  x <- list(formula=formula, startval=startval, paramnames=c(preProcessParams(names(startval))), isContinuousTime=isContinuousTime)
  if (missing(jacobian)){
    autojcb=try(lapply(formula,autojacob,length(formula[[1]])))
    if (class(autojcb) == "try-error") {
      stop("Automatic differentiation is not supported by part of the dynamic functions.\n 
           Please provide the analytic jacobian functions.")
    }else{
      jacobian=lapply(autojcb,"[[","jacob")
    }
  }
  x$jacobian <- jacobian
  x$paramnames<-names(x$startval)
  return(new("dynrDynamicsFormula", x))
}

##' Recipe function for creating Linear Dynamcis using matrices
##' 
##' @param params.dyn the matrix of parameter names for the transition matrix in the 
##' specified linear dynamic model
##' @param values.dyn the matrix of starting/fixed values for the transition matrix in the 
##' specified linear dynamic model
##' @param params.exo the matrix of parameter names for the regression slopes of covariates on the latent variables (see details)
##' @param values.exo matrix of starting/fixed values for the regression slopes of covariates on the latent variables (see details)
##' @param params.int vector of names for intercept parameters in the dynamic model specified as a matrix or list of matrices. 
##' @param values.int vector of intercept values in the dynamic model specified as matrix or list of matrices. Contains starting/fixed values of the intercepts.
##' @param covariates the names or the index numbers of the covariates used in the dynamic model
##' @param isContinuousTime logical. When TRUE, use a continuous time model.  When FALSE use a discrete time model.
##' 
##' @details
##' A recipe function for specifying the deterministic portion of a set of linear dynamic functions as:
##'
##' Discrete-time model: eta(t+1) = int + dyn*eta(t) + exo*x(t), 
##' where eta(t) is a vector of latent variables, x(t) is a vector of covariates,
##' int, dyn, and exo are vectors and matrices specified via the arguments *.int, *.dyn, and *.exo. 
##'
##' Continuous-time model: d/dt eta(t) = int + dyn*eta(t) + exo*x(t), 
##' where eta(t) is a vector of latent variables, x(t) is a vector of covariates,
##' int, dyn, and exo are vectors and matrices specified via the arguments *.int, *.dyn, and *.exo.
##' 
##' The left-hand side of the dynamic model consists of a vector of latent variables for the next time point in the discrete-time case,
##' and the vector of derivatives for the latent variables at the current time point in the continuous-time case.
##'
##' For models with regime-switching dynamic functions, the user will need to provide a list of the *.int, *.dyn, and *.exo arguments. 
##' (when they are specified to take on values other than the default of zero vectors and matrices), or if a single set of vectors/matrices are provided, the same 
##' vectors/matrices are assumed to hold across regimes.
##' 
##' \code{prep.matrixDynamics} serves as an alternative to \code{\link{prep.formulaDynamics}}.
##' 
##' @seealso 
##' Methods that can be used include: \code{\link{print}}, \code{\link{show}} 
##' 
##' @examples 
##' #Single-regime, continuous-time model. For further details run: 
##' #demo(RSNonlinearDiscrete, package="dynr"))
##' dynamics <- prep.matrixDynamics(
##' values.dyn=matrix(c(0, -0.1, 1, -0.2), 2, 2),
##' params.dyn=matrix(c('fixed', 'spring', 'fixed', 'friction'), 2, 2),
##' isContinuousTime=TRUE)
##' 
##' #Two-regime, continuous-time model. For further details run: 
##' #demo(RSNonlinearDiscrete, package="dynr"))
##' dynamics <- prep.matrixDynamics(
##' values.dyn=list(matrix(c(0, -0.1, 1, -0.2), 2, 2),
##'                 matrix(c(0, -0.1, 1, 0), 2, 2)),
##' params.dyn=list(matrix(c('fixed', 'spring', 'fixed', 'friction'), 2, 2),
##'            matrix(c('fixed', 'spring', 'fixed', 'fixed'), 2, 2)),
##' isContinuousTime=TRUE) 
prep.matrixDynamics <- function(params.dyn=NULL, values.dyn, params.exo=NULL, values.exo=NULL, params.int=NULL, values.int=NULL, 
                                covariates, isContinuousTime){
	# Handle numerous cases of missing or non-list arguments
	# General idea
	# If they give us a non-list argument, make it a one-element list
	# If they don't give us params, assume it's all fixed params
	# If they don't give us values, assume they don't want that part of the model
	
	# Handle dyn
	r <- coProcessValuesParams(values.dyn, params.dyn)
	values.dyn <- r$values
	params.dyn <- r$params
	
	# Handle exo
	r <- coProcessValuesParams(values.exo, params.exo, missingOK=TRUE)
	values.exo <- r$values
	params.exo <- r$params
	
	# Handle int
	r <- coProcessValuesParams(values.int, params.int, missingOK=TRUE)
	values.int <- r$values
	params.int <- r$params
	
	
	if(missing(covariates)){
		covariates <- character(0)
	}
	values.dyn <- lapply(values.dyn, preProcessValues)
	params.dyn <- lapply(params.dyn, preProcessParams)
	values.exo <- lapply(values.exo, preProcessValues)
	params.exo <- lapply(params.exo, preProcessParams)
	values.int <- lapply(values.int, preProcessValues)
	params.int <- lapply(params.int, preProcessParams)
	
	# Check that the number of covariates implied by the 'covariates' arg is the same as that
	#  implied by the number of columns in the 'values.exo' arg.
	matCovariates <- lapply(lapply(values.exo, dim), "[[", 2)
	argCovariates <- length(covariates)
	if(!all(matCovariates == argCovariates)){
		msg <- paste0("Mind your teaspoons and tablespoons.  The 'exo.values' argument says there are\n (", paste(matCovariates, collapse=", "), ") covariates, but the 'covariates' arg says there are (", argCovariates, ").")
		stop(msg)
	}
	
	sv <- c(extractValues(values.dyn, params.dyn), extractValues(values.exo, params.exo), extractValues(values.int, params.int))
	pn <- c(extractParams(params.dyn), extractParams(params.exo), extractParams(params.int))
	sv <- extractValues(sv, pn)
	pn <- extractParams(pn)
	
	x <- list(startval=sv, paramnames=pn, params.dyn=params.dyn, values.dyn=values.dyn, params.exo=params.exo, values.exo=values.exo, params.int=params.int, values.int=values.int, isContinuousTime=isContinuousTime,covariates=covariates)
	return(new("dynrDynamicsMatrix", x))
}


# nonlinear functions for lookup
# logit, logistic, softmax

#https://en.wikipedia.org/wiki/C_mathematical_functions

#carl ~ param[5]*carl + param[7]*logistic(abs(bob))
#bob ~ param[4]*bob + param[6]*logistic(abs(carl))

#cform <- carl ~ param[5] * carl + param[7] * logistic(abs(bob)) + dan**2
#rhs.vars(cform)

isSymbolNumberFunction <- function(x){is.symbol(x) || is.numeric(x) || is.function(x)}

# Recursive function for formula parsing
parseFormula <- function(formula, debug=FALSE){
  tuple <- as.list(formula)
  op<-tuple[[1]]
  left <- tuple[[2]]
  right <- tuple[[3]]
	if(debug){
		print("LEFT")
		print(left)
		print("RIGHT")
		print(right)
	}
	if(!isSymbolNumberFunction(left)){
	  leftTuple <- as.list(left)
		if(length(leftTuple)==3){
			left <- parseFormula(left,debug)
		} else {
			left <- parseNested(left,debug)
		}
	}
	if(!isSymbolNumberFunction(right)){
	  rightTuple <- as.list(right)
		if(length(rightTuple)==3){
			right <- parseFormula(right,debug)
		} else{
			right <- parseNested(right,debug)
		}
	}

  op <- trans2CFunction(op)
  
  tuple.new <- list(op,left,right)
  outFormula <- as.call(tuple.new)
	return(outFormula)
}

parseNested <- function(formula,debug=FALSE){
	tuple <- as.list(formula)
	outer <- tuple[[1]]
	inner <- tuple[[2]]
	if(debug){
	  print(tuple)
	  print("OUTER")
	  print(outer)
	  print("INNER")
	  print(inner)
	}
	if(!isSymbolNumberFunction(inner)){
	  innerTuple=as.list(inner)
	  if(length(innerTuple)==3){
	    inner <- parseFormula(inner,debug)
	  } else{
	    inner <- parseNested(inner,debug)
	  }
	}
	
	outer <- trans2CFunction(outer)
	
	tuple.new <-as.list(c(outer,inner))
	outFormula <- as.call(tuple.new)
	return(outFormula)
}

#TODO check type casting int->double
#TODO check the parameter indices
trans2CFunction<-function(op.symbol){
  op.char=deparse(op.symbol)
  if (op.char %in% c("d","abs","^","**","mod","max","min","sign")) {
    op.symbol<-switch(op.char,
              d = NULL,       
            abs = as.name("fabs"),
            "^" = as.name("pow"),
            "**"= as.name("pow"),
            mod = as.name("fmod"),
            max = as.name("fmax"),
            min = as.name("fmin"),
            sign = c(as.name("copysign"),1))
    }
  return(op.symbol)
}

processFormula<-function(formula.list){
  formula.char=sapply(formula.list,FUN=function(f){paste0(deparse(parseFormula(f),width.cutoff = 500L),collapse="")})
  out=strsplit(formula.char," ~ ")
  return(out)
}
# Example usage
#require(dynr)
#logistic <- function(x){x}
#
#cform <- carl ~ param[5] * carl + param[7] * logistic(abs(bob)) + dan**2
#cform2 <- parseFormula(cform)
#cform2
#str(cform2)
#
# Note also some helpful functions in the pryr package
#pryr::ast(carl ~ param[5] * carl + param[7] * logistic(abs(bob)) + dan**2)
#pryr::call_tree(cform)
#
# The source code for 
#pryr:::tree
# is useful in this regard.
# We are essentially making our own version of pryr:::tree
# that does some substitutions before collapsing back to a 
# string that is composed of C code.


##' Recipe function for preparing the initial conditions for the model. 
##' 
##' @param values.inistate a vector or list of vectors of the starting or fixed values of the initial state vector in one or more regimes.  May also be a matrix or list of matrices.
##' @param params.inistate a vector or list of vectors of the parameter names that appear in the initial state vector in one or more regimes. If an element is 0 or "fixed", the corresponding element is fixed at the value specified in the values vector; Otherwise, the corresponding element is to be estimated with the starting value specified in the values vector.  May also be a matrix or list of matrices.
##' @param values.inicov a positive definite matrix or a list of positive definite matrices of the starting or fixed values of the initial error covariance structure(s) in one or more regimes. If only one matrix is specified for a regime-switching dynamic model, the initial error covariance structure stays the same across regimes. To ensure the matrix is positive definite in estimation, we apply LDL transformation to the matrix. Values are hence automatically adjusted for this purpose.
##' @param params.inicov a matrix or list of matrices of the parameter names that appear in the initial error covariance(s) in one or more regimes. If an element is 0 or "fixed", the corresponding element is fixed at the value specified in the values matrix; Otherwise, the corresponding element is to be estimated with the starting value specified in the values matrix. If only one matrix is specified for a regime-switching dynamic model, the process noise structure stays the same across regimes. If a list is specified, any two sets of the parameter names as in two matrices should be either the same or totally different to ensure proper parameter estimation.
##' @param values.regimep a vector/matrix of the starting or fixed values of the initial probabilities of being in each regime. By default, the initial probability of being in the first regime is fixed at 1.
##' @param params.regimep a vector/matrix of the parameter indices of the initial probabilities of 
##' being in each regime. If an element is 0 or "fixed", the corresponding element is fixed at the value 
##' specified in the "values" vector/matrix; Otherwise, the corresponding element is to be estimated 
##' with the starting value specified in the values vector/matrix.
##' @param covariates character vector of the names of the (person-level) covariates
##' @param deviation logical. Whether to use the deviation form or not.  See Details.
##' @param refRow numeric. Which row is treated at the reference.  See Details.
##' 
##' @details
##' The initial condition model includes specifications for the intial state vector, 
##' initial error covariance matrix, initial probabilities of 
##' being in each regime and all associated parameter specifications.
##' The initial probabilities are specified in multinomial logistic regression form.  When there are no covariates, this implies multinomial logistic regression with intercepts only.  In particular, the initial probabilities not not specified on a 0 to 1 probability scale, but rather a negative infinity to positive infinity log odds scale.  Fixing an initial regime probability to zero does not mean zero probability.  It translates to a comparison log odds scale against which other regimes will be judged.
##' 
##' The structure of the initial state vector and the initial probability vector depends on the presence of covariates.  When there are no covariates these should be vectors, or equivalently single-column matrices.  When there are covariates they should have \eqn{c+1} columns for \eqn{c} covariates.  The number of rows should be the number of regimes for the initial regimes, or the number of latent states for the initial states.
##' 
##' When \code{deviation=FALSE}, the non-deviation form of the multinomial logistic regression is used. This form has a separate intercept term for each entry of the initial probability vector. When \code{deviation=TRUE}, the deviation form of the multinomial logistic regression is used. This form has an intercept term that is common to all rows of the initial probability vector. The rows are then distinguished by their own individual deviations from the common intercept. The deviation form requires the same reference row constraint as the non-deviation form (described below). By default the reference row is taken to be the row with all zero covariate effects.  Of course, if there are no covariates and the deviation form is desired, then the user must provide the reference row.
##' 
##' The \code{refRow} argument determines which row is used as the intercept row. It is only
##' used in the deviation form (i.e. \code{deviation=TRUE}). In the deviation form, one row of \code{values.regimep} and \code{params.regimep} contains the intercepts, other rows contain deviations from these intercepts. The \code{refRow} argument says which row contains the intercept terms. The default behavior for \code{refRow} is to detect the reference row automatically based on which parameters are \code{fixed}.  If we have problems detecting which is the reference row, then we provide error messages that are as helpful as we can make them.
##' 
##' @seealso 
##' Methods that can be used include: \code{\link{print}}, \code{\link{printex}}, \code{\link{show}} 
##'
##' @examples
##' #### No-covariates
##' # Single regime, no covariates
##' # latent states are position and velocity
##' # initial position is free and called 'inipos'
##' # initial slope is fixed at 1
##' # initial covariance is fixed to a diagonal matrix of 1s
##' initialNoC <- prep.initial(
##' 	values.inistate=c(0, 1),
##' 	params.inistate=c('inipos', 'fixed'),
##' 	values.inicov=diag(1, 2),
##' 	params.inicov=diag('fixed', 2))
##' 
##' #### One covariate
##' # Single regime, one covariate on the inital mean
##' # latent states are position and velocity
##' # initial covariance is fixed to a diagonal matrix of 1s
##' # initial latent means have
##' #   nrow = numLatentState, ncol = numCovariates + 1
##' # initial position has free intercept and free u1 effect
##' # initial slope is fixed at 1
##' initialOneC <- prep.initial(
##' 	values.inistate=matrix(
##' 		c(0, .5,
##' 		  1,  0), byrow=TRUE,
##' 		nrow=2, ncol=2),
##' 	params.inistate=matrix(
##' 		c('iniPosInt', 'iniPosSlopeU1',
##' 		'fixed', 'fixed'), byrow=TRUE,
##' 		nrow=2, ncol=2),
##' 	values.inicov=diag(1, 2),
##' 	params.inicov=diag('fixed', 2),
##' 	covariates='u1')
##' 
##' #### Regime-switching, one covariate
##' # latent states are position and velocity
##' # initial covariance is fixed to a diagonal matrix of 1s
##' # initial latent means have
##' #   nrow = numLatentState, ncol = numCovariates + 1
##' # initial position has free intercept and free u1 effect
##' # initial slope is fixed at 1
##' # There are 3 regimes but the mean and covariance
##' #   are not regime-switching.
##' initialRSOneC <- prep.initial(
##' 	values.regimep=matrix(
##' 		c(1, 1,
##' 		  0, 1,
##' 		  0, 0), byrow=TRUE,
##' 		nrow=3, ncol=2),
##' 	params.regimep=matrix(
##' 		c('r1int', 'r1slopeU1',
##' 		  'r2int', 'r2slopeU2',
##' 		  'fixed', 'fixed'), byrow=TRUE,
##' 		nrow=3, ncol=2),
##' 	values.inistate=matrix(
##' 		c(0, .5,
##' 		  1,  0), byrow=TRUE,
##' 		nrow=2, ncol=2),
##' 	params.inistate=matrix(
##' 		c('iniPosInt', 'iniPosSlopeU1',
##' 		'fixed', 'fixed'), byrow=TRUE,
##' 		nrow=2, ncol=2),
##' 	values.inicov=diag(1, 2),
##' 	params.inicov=diag('fixed', 2),
##' 	covariates='u1')
##' 
prep.initial <- function(values.inistate, params.inistate, values.inicov, params.inicov, values.regimep=1, params.regimep=0, covariates, deviation=FALSE, refRow){
	if(missing(covariates)){
		covariates <- character(0)
	}
	if(missing(refRow)){ # or other default values???
		refRow <- numeric(0)
	}
	
	# Handle initial state
	r <- coProcessValuesParams(values.inistate, params.inistate)
	values.inistate <- r$values
	params.inistate <- r$params
	
	# Handle initial covariance
	r <- coProcessValuesParams(values.inicov, params.inicov)
	values.inicov <- r$values
	params.inicov <- r$params
	
	if(length(values.inistate) != length(values.inicov)){
		stop('Initial state means and covariance matrix imply different numbers of regimes.')
	}
	
	
	values.inistate <- lapply(values.inistate, preProcessValues)
	params.inistate <- lapply(params.inistate, preProcessParams)
	
	
	values.inicov <- lapply(values.inicov, preProcessValues)
	params.inicov <- lapply(params.inicov, preProcessParams)
	lapply(values.inicov, checkSymmetric, name="values.inicov")
	lapply(params.inicov, checkSymmetric, name="params.inicov")
	values.inicov.inv.ldl <- lapply(values.inicov, replaceDiagZero)
	values.inicov.inv.ldl <- lapply(values.inicov.inv.ldl, reverseldl)
	
	if(nrow(values.inistate[[1]]) != nrow(values.inicov[[1]])){
		stop(paste0('Number of latent variables implied by initial state and initial covariance differ:\n',
			'initial state (', nrow(values.inistate[[1]]), '), initial cov (', nrow(values.inicov[[1]]), ')'))
	}
	if(ncol(values.inistate[[1]]) != (length(covariates) + 1)){
		stop(paste0('Incorrect dimensions for initial state\nFound ', paste0(dim(values.inistate[[1]]), collapse=' by '),
			' but should be ', nrow(values.inistate[[1]]), ' by ', length(covariates) + 1, '\n',
			'k by (c+1) for k=number of latent variables, c=number of covariates'))
	}
	
	if(identical(values.regimep, 1) && identical(params.regimep, 0)){
		values.regimep <- matrix(c(1, rep(0, length(covariates))), 1, length(covariates)+1)
		params.regimep <- matrix(c(0), nrow(values.regimep), length(covariates)+1)
	}
	values.regimep <- preProcessValues(values.regimep)
	params.regimep <- preProcessParams(params.regimep)
	
	if(ncol(values.regimep) != (length(covariates) + 1)){
		stop(paste0('Incorrect dimensions for initial probabilities\nFound ', paste0(dim(values.regimep), collapse=' by '),
			' but should be ', nrow(values.regimep), ' by ', length(covariates) + 1, '\n',
			'r by (c+1) for r=number of regimes, c=number of covariates'))
	}
	
	# Do lots or processing and checking for the reference row and column
	if(deviation == TRUE){
		# if needed set refRow
		if(length(refRow) == 0){
			# check valid reference row (i.e. there is a single row as reference)
			# detect reference row
			# reference row has all zero covariate effects
			covAreZero <- apply(params.regimep, 1, function(x){all(x[-1] %in% c('fixed'))})
			blockSize <- max(length(covariates), 1)
			covAreZeroM <- matrix(covAreZero, nrow=blockSize, ncol=nrow(values.regimep))
			refRow <- apply(covAreZeroM, 2, function(x){all(x==TRUE)})
			if(sum(refRow) < 1){ #no single reference row found
				stop(paste("No single reference row found. Initial probabilities might not be identified. Set e.g. refRow=1."))
			}
			if(sum(refRow) > 1){#found more than one reference row
				stop(paste0("Found multiple possible reference rows: ", paste(which(refRow==1), collapse=', '), ".  Reconsider your model specification or just set e.g. refRow=1."))
			}
			# set reference row to reference column
			refRow <- which(refRow)
		}
		if(length(refRow) > 1){
			# error reference row must have length 0 or 1
			stop(paste("'refRow' must being a single number. That is, have length 0 or 1, we found length", length(refRow)))
		}
		if(refRow > nrow(values.regimep)){
			# error reference row must be between 1 and nrow(values)
			stop(paste("'refRow' must be between 1 and", nrow(values.regimep), "but we found", refRow))
		}
	} else {
		if(length(refRow) > 0){
			#warning refRow is ignored when for the non-deviation case (deviation=FALSE)
			warning("'refRow' argument is ignored in the non-deviation case (i.e. when deviation=FALSE)")
		}
	}
	
	
	sv <- c(extractValues(values.inistate, params.inistate), extractValues(values.inicov.inv.ldl, params.inicov, symmetric=TRUE), extractValues(values.regimep, params.regimep))
	pn <- c(extractParams(params.inistate), extractParams(params.inicov), extractParams(params.regimep))
	sv <- extractValues(sv, pn)
	pn <- extractParams(pn)
	
	x <- list(startval=sv, paramnames=pn, values.inistate=values.inistate, params.inistate=params.inistate, values.inicov=values.inicov, values.inicov.inv.ldl=values.inicov.inv.ldl, params.inicov=params.inicov, values.regimep=values.regimep, params.regimep=params.regimep, covariates=covariates, deviation=deviation, refRow=refRow)
	return(new("dynrInitial", x))
}

##' Create a dynrTrans object to handle the transformations and inverse 
##' transformations of model paramters
##' 
##' @param formula.trans a list of formulae for transforming freed parameters 
##' other than variance-covariance parameters during the optimization process. 
##' These transformation functions may be helpful for transforming parameters 
##' that would normally appear on a constrained scale to an unconstrained 
##' scale (e.g., parameters that can only take on positive values can be 
##' subjected to exponential transformation to ensure positivity.)
##' @param formula.inv a list of formulae that inverse the transformation 
##' on the free parameters and will be used to calculate the starting values 
##' of the parameters.
##' @param transCcode a logical value indicating whether the functions in 
##' formula.trans need to be transformed to functions in C. The default 
##' for transCcode is TRUE, which means that the formulae will be translated 
##' to C functions and utilized during the optimization process. 
##' If transCcode = FALSE, the transformations are only performed at the end 
##' of the optimization process for standard error calculations but not 
##' during the optimization process.
##' ##' 
##' @details
##' Prepares a dynr recipe that specifies the names of the parameters that are 
##' to be subjected to user-supplied transformation functions and the 
##' corresponding transformation and reverse-transformation functions. 
##' This can be very handy in fitting dynamic models in which certain parameters can 
##' only take on permissible values in particular ranges (e.g., a parameter may 
##' have to positive). Note that all variance-covariance parameters in the model
##' are automatically subjected to transformation functions to ensure that
##' the resultant covariance matrices are positive-definite. Thus, no additional
##' transformation functions are needed for variance-covariance parameters.
##' 
##' @examples
##' #Specifies a transformation recipe, r20, that subjects the parameters
##' #'r10' and 'r20' to exponential transformation to ensure that they are positive.
##' trans <-prep.tfun(formula.trans=list(r10~exp(r10), r20~exp(r20)),
##'                   formula.inv=list(r10~log(r10),r20~log(r20)))
##'
prep.tfun<-function(formula.trans, formula.inv, transCcode = TRUE){
  #input: formula.trans=list(a~exp(a),b~b^2)
  #input: formula.inv=list(a~log(a),b~sqrt(b))
  if (missing(formula.trans)&missing(formula.inv)){
    #TODO modify this part if needed
    x<-list(transCcode = FALSE)
  }else{
    if (missing(formula.inv)){
      x <- list(formula.trans=formula.trans, transCcode = transCcode)
    }else if (missing(formula.trans)){
      x <- list(formula.inv=formula.inv, transCcode = FALSE)
    }else{
      x <- list(formula.trans=formula.trans, formula.inv=formula.inv, transCcode = transCcode)
    }
  }
  return(new("dynrTrans", x))
}

#------------------------------------------------------------------------------

formula2string<-function(formula.list){
  tuple=lapply(formula.list,as.list)
  lhs=sapply(tuple,function(x){paste0(deparse(x[[2]],width.cutoff = 500L),collapse="")})
  rhs=sapply(tuple,function(x){paste0(deparse(x[[3]],width.cutoff = 500L),collapse="")})
  return(list(lhs=lhs,rhs=rhs))
}


matrix2formula <- function(x,multbyColnames=TRUE){
	if(!is.matrix(x)){
		stop("Dude! You have to give me a matrix. If you do, I'll give you a formula. Seriously.")
	}
	if(is.null(rownames(x))){
		rownames(x) <- paste0('y', 1:nrow(x))
	}
	if(is.null(colnames(x))){
		colnames(x) <- paste0('x', 1:ncol(x))
	}
	preds <- character(nrow(x))
	for(i in 1:nrow(x)){
	  if (multbyColnames==FALSE){
	    preds[i] <- paste(rownames(x)[i], paste(x[i,]), sep=' ~ ')
	  }else{
		  preds[i] <- paste(rownames(x)[i], paste(x[i,], 
		                       colnames(x), sep='*', collapse=' + '), sep=' ~ ')
		  } 
	}
	if(is.numeric(x)){
		preds <- gsub(' 1\\*', ' ', preds)
		preds <- gsub(paste(" 0\\*(", paste(colnames(x), collapse = "|"), ") \\+", sep=""), '', preds)
		preds <- gsub(paste(" \\+ 0\\*(", paste(colnames(x), collapse = "|"), ")", sep=""), '', preds)
	} else {
		preds <- gsub(' 1\\*', ' ', preds)
		preds <- gsub(paste(" 0\\*(", paste(colnames(x), collapse = "|"), ") \\+", sep=""), '', preds)
		preds <- gsub(paste(" \\+ 0\\*(", paste(colnames(x), collapse = "|"), ")", sep=""), '', preds)
	}
	form <- lapply(preds, formula, env=.GlobalEnv)
	#eq.char=lapply(form, as.character)
	#str.left=sapply(eq.char,"[",2)
	#str.right=sapply(eq.char,"[",3)
	#if (bothSides) {return(form)}else{return(str.right)}
	return(form)
}

# # Examples
# meas <- prep.loadings(
#  map=list(
#    eta1=paste0('y', 1:3),
#    eta2=paste0('y', 4:6)),
#    params = paste0("lambda",c(1:4)))
# matrix2formula(meas$values.load[[1]])
# matrix2formula(meas$params.load[[1]])
# lapply(meas$values.load,matrix2formula)
# #TODO check if fixed in formula/tex is substituted by fixed values.

addFormulas <- function(f1, f2){
	charf1 <- as.character(f1)
	charf2 <- as.character(f2)
	if((length(charf1) != 3) || (length(charf2) != 3)){
		msg <- paste("Formula 1 and/or 2 look(s) strange, and not in a good way.", "A formula used here should only have one tilde (~) in it", "I don't know what to do with them")
		stop(msg)
	}
	if(charf1[2] != charf2[2]){
		stop('The two formulas do not have the same outcome variable. I simply refuse to add them.')
	}
	formula(paste(charf1[2], "~", charf1[3], "+", charf2[3]), env=.GlobalEnv)
}

## Examples
#f1 <- y~9*x1+x2
#f2 <- y~1.2
#f3 <- y~5*u1
#addFormulas(f1, f2)
#addFormulas(f2, f1)

addLLFormulas <- function(list_list_formulae, VecNamesToAdd){
  nRegime <- length(list_list_formulae)
  for (j in 1:nRegime){
    neq <- length(list_list_formulae[[j]])
    for (k in 1:neq){
      AddedFml <- list_list_formulae[[j]][[k]]
      for (l in 1:length(VecNamesToAdd)){
        list_list_formulae_add=get(VecNamesToAdd[l], parent.frame())#eval(parse(text=VecNamesToAdd[l]),environment())
        if (length(list_list_formulae_add)>0){
          if (length(list_list_formulae_add[[j]])>0){
            AddedFml <- addFormulas(list_list_formulae_add[[j]][[k]],AddedFml)
          }
        }
      }
      list_list_formulae[[j]][[k]] <- AddedFml
    }
  }
  return(list_list_formulae)
}

#------------------------------------------------------------------------------
dP_dt <- "/**\n * The dP/dt function: depend on function_dF_dx, needs to be compiled on the user end\n * but user does not need to modify it or care about it.\n */\nvoid mathfunction_mat_to_vec(const gsl_matrix *mat, gsl_vector *vec){\n\tsize_t i,j;\n\tsize_t nx=mat->size1;\n\t/*convert matrix to vector*/\n\tfor(i=0; i<nx; i++){\n\t\tgsl_vector_set(vec,i,gsl_matrix_get(mat,i,i));\n\t\tfor (j=i+1;j<nx;j++){\n\t\t\tgsl_vector_set(vec,i+j+nx-1,gsl_matrix_get(mat,i,j));\n\t\t\t/*printf(\"%lu\",i+j+nx-1);}*/\n\t\t}\n\t}\n}\nvoid mathfunction_vec_to_mat(const gsl_vector *vec, gsl_matrix *mat){\n\tsize_t i,j;\n\tsize_t nx=mat->size1;\n\t/*convert vector to matrix*/\n\tfor(i=0; i<nx; i++){\n\t\tgsl_matrix_set(mat,i,i,gsl_vector_get(vec,i));\n\t\tfor (j=i+1;j<nx;j++){\n\t\t\tgsl_matrix_set(mat,i,j,gsl_vector_get(vec,i+j+nx-1));\n\t\t\tgsl_matrix_set(mat,j,i,gsl_vector_get(vec,i+j+nx-1));\n\t\t}\n\t}\n}\nvoid function_dP_dt(double t, size_t regime, const gsl_vector *p, double *param, size_t n_param, const gsl_vector *co_variate, gsl_vector *F_dP_dt){\n\t\n\tsize_t nx;\n\tnx = (size_t) floor(sqrt(2*(double) p->size));\n\tgsl_matrix *P_mat=gsl_matrix_calloc(nx,nx);\n\tmathfunction_vec_to_mat(p,P_mat);\n\tgsl_matrix *F_dx_dt_dx=gsl_matrix_calloc(nx,nx);\n\tfunction_dF_dx(t, regime, param, co_variate, F_dx_dt_dx);\n\tgsl_matrix *dFP=gsl_matrix_calloc(nx,nx);\n\tgsl_matrix *dP_dt=gsl_matrix_calloc(nx,nx);\n\tgsl_blas_dgemm(CblasNoTrans, CblasNoTrans, 1.0, F_dx_dt_dx, P_mat, 0.0, dFP);\n\tgsl_matrix_transpose_memcpy(dP_dt, dFP);\n\tgsl_matrix_add(dP_dt, dFP);\n\tsize_t n_Q_vec=(1+nx)*nx/2;\n\tgsl_vector *Q_vec=gsl_vector_calloc(n_Q_vec);\n\tsize_t i;\n\tfor(i=1;i<=n_Q_vec;i++){\n\t\t\tgsl_vector_set(Q_vec,n_Q_vec-i,param[n_param-i]);\n\t}\n\tgsl_matrix *Q_mat=gsl_matrix_calloc(nx,nx);\n\tmathfunction_vec_to_mat(Q_vec,Q_mat);\n\tgsl_matrix_add(dP_dt, Q_mat);\n\tmathfunction_mat_to_vec(dP_dt, F_dP_dt);\n\tgsl_matrix_free(P_mat);\n\tgsl_matrix_free(F_dx_dt_dx);\n\tgsl_matrix_free(dFP);\n\tgsl_matrix_free(dP_dt);\n\tgsl_vector_free(Q_vec);\n\tgsl_matrix_free(Q_mat);\n}\n"

#N.B. The formula 
#	nx = (size_t) floor(sqrt(2*(double) p->size));
# is used instead of the analytic formula
#	nx = (size_t) sqrt(2*double p->size + .25) - .5
# due to issues with rounding.



#------------------------------------------------------------------------------
# Utility functions for writing GSL code

setGslMatrixElements <- function(values, params, name, depth=1){
	ret <- ""
	numRow <- nrow(values)
	numCol <- ncol(values)
	for(j in 1:numCol){
		for(i in 1:numRow){
			if(params[i, j] > 0){
				ret <- paste(ret,
					paste(rep('\t', depth), collapse=""), 'gsl_matrix_set(', name, ', ', i-1, ', ', j-1,
					', param[', params[i, j] - 1, ']);\n', sep='')
			} else if(values[i, j] != 0){
				ret <- paste(ret,
					paste(rep('\t', depth), collapse=""), 'gsl_matrix_set(', name, ', ', i-1, ', ', j-1,
					', ', values[i, j], ');\n', sep='')
			}
		}
	}
	return(ret)
}

setGslVectorElements <- function(values, params, name, fill="", depth=1){
  ret <- ""
  numLength <- length(values)
  for(i in 1:numLength){
      if(params[i] > 0){
        ret <- paste(ret,
                     paste(rep('\t', depth), collapse=""), 'gsl_vector_set(', name, ', ', fill, i-1,
                     ', param[', params[i] - 1, ']);\n', sep='')
      } else if(values[i] != 0){
        ret <- paste(ret,
                     paste(rep('\t', depth), collapse=""), 'gsl_vector_set(', name, ', ', fill, i-1,
                     ', ', values[i], ');\n', sep='')
      }
    }
  return(ret)
}


createGslMatrix <- function(nrow, ncol, name){
	paste0("\tgsl_matrix *", name, " = gsl_matrix_calloc(", nrow, ", ", ncol, ");\n")
}

destroyGslMatrix <- function(name){
	paste0("\tgsl_matrix_free(", name, ");\n")
}

createGslVector <- function(size, name){
	paste0("\tgsl_vector *", name, " = gsl_vector_calloc(", size, ");\n")
}

destroyGslVector <- function(name){
	paste0("\tgsl_vector_free(", name, ");\n")
}

#y <- alpha * transA(A) %*% x + beta * y
blasMV <- function(transA, alpha, A, x, beta, y){
	transA <- ifelse(transA, "CblasTrans", "CblasNoTrans")
	paste0("\tgsl_blas_dgemv(", paste(transA, alpha, A, x, beta, y, sep=", "), ");\n")
}

# C <- alpha * transA(A) %*% transB(B) + beta * C
blasMM <- function(transA, transB, alpha, A, B, beta, C){
	transA <- ifelse(transA, "CblasTrans", "CblasNoTrans")
	transB <- ifelse(transB, "CblasTrans", "CblasNoTrans")
	paste0("\tgsl_blas_dgemm(", paste(transA, transB, alpha, A, B, beta, C, sep=", "), ");\n")
}

gslVector2Column <- function(matrix, index, vector, which){
	if(which=='column'){
		paste0("\tgsl_matrix_set_col(", paste(matrix, index, vector, sep=", "), ");\n")
	} else if(which=='row'){
		paste0("\tgsl_matrix_set_row(", paste(matrix, index, vector, sep=", "), ");\n")
	}
}

# fromName character name of the from vector
# toName character name of the to vector
# fromLoc numeric integer vector of from locations
# toLoc numeric integer vector of to locations
# depth number of tabs to indent
# create logical whether to create the toName before copying.  The vector created will be the length of toLoc
gslVectorCopy <- function(fromName, toName, fromLoc, toLoc, fromFill="", toFill="", depth=1, create=FALSE){
	# check lengths match
	if(length(fromLoc) != length(toLoc)){stop("'fromLoc' and 'toLoc' lengths must match")}
	tabs <- paste(rep('\t', depth), collapse="")
	tabsm1 <- paste(rep('\t', depth-1), collapse="")
	ret <- character(0)
	if(create){
		ret <- paste0(ret, tabsm1, createGslVector(length(toLoc), toName))
	}
	for(i in 1:length(toLoc)){
		get <- paste0("gsl_vector_get(", fromName, ", ", fromFill, fromLoc[i]-1, ")")
		set <- paste0(tabs, "gsl_vector_set(", toName, ", ", toFill, toLoc[i]-1, ", ", get, ");\n")
		ret <- paste0(ret, set)
	}
	return(ret)
}

gslcovariate.front <- function(selected, covariates){
	gslVectorCopy("co_variate", "covariate_local", match(selected, covariates), 1:length(selected), create=TRUE)
}

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dynr documentation built on Sept. 25, 2018, 9:05 a.m.