Nothing
R/theta2alpha.R
In GenHMM1d: Goodness-of-Fit for Univariate Hidden Markov Models
Defines functions
theta2alpha
Documented in
theta2alpha
#'@title Transform constrained parameters to unconstrained parameters
#'
#'@description This function computes the unconstrained parameters alpha of a univariate distribution corresponding to constrainted parameters theta.
#'
#'@param family distribution name; run the command distributions() for help
#'@param param constrained parameters of the univariate distribution
#'
#'@return \item{alpha}{constrained parameters}
#'
#'
#'
#'@export
#'@keywords internal
theta2alpha<-function(family,param){
switch(family,
"asymexppower" = { ## [R+, R+, 01]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
alpha[3] = log(param[3]/(1-param[3]))
} ,
"asymlaplace" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"asympower" = { ## [01, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1]/(1-param[1]))
alpha[2:3] = log(param[2:3])
} ,
"asymt" = { ## [R+, R+, 01]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = log(param[3]/(1-param[3]))
alpha[4] = param[4]
} ,
"beard" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"benini" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"benford" = { ## [1 ou 2]
alpha = matrix(0,1,1)
if (param[1]>log(2)){
alpha[1] = log(2)
} else if (param[1]<0){
alpha[1] = 0
} else {
alpha[1] = log(param[1])
}
} ,
"bernoulli" = { ## [01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"beta" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"betabinomial" = { ## [N+, R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"betageometric" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"betanegativebinomial" = { ## [N+, R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"betaburr" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betaburr7" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betaexponential" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"betafrechet" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betagompertz" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betagumbel" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"betagumbel2" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betalognormal" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"betalomax" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betanormal" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"betaprime" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"betaweibull" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"bhattacharjee" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"binomial" = { ## [N+, 01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"birnbaumsaunders" = { ## [R+, R+, R]
alpha = matrix(0,1,3)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
} ,
"boxcox" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"burr" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"burr2param" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"cauchy" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"chen" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"chi" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"chisquared" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"clg" = { ## [R+, R+, R]
alpha = matrix(0,1,3)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
} ,
"complementarybeta" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"dagum" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"diffzeta" = { ## [R+, >1]
alpha = matrix(0,1,2)
alpha[1] = log(param[1])
alpha[2] = log(exp(-param[2]) + 1)
} ,
"discretegamma" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"discretelaplace" = { ## [R, 01]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])/(1-param[2])
} ,
"discretenormal" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"discreteweibull" = { ## [01, R+]
alpha = matrix(0,1,2)
alpha[1] = log(param[1]/(1-param[1]))
alpha[2] = log(param[2])
} ,
"doubleweibull" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"ev" = {
## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"exponential" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"exponentialextension" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"exponentialgeometric" = { ## [R+, 01]
alpha = matrix(0,1,2)
alpha[1] = log(param[1])
alpha[2] = log(param[2]/(1-param[2]))
} ,
"exponentiallogarithmic" = { ## [R+, 01]
alpha = matrix(0,1,2)
alpha[1] = log(param[1])
alpha[2] = log(param[2]/(1-param[2]))
} ,
"exponentialpoisson" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"exponentialpower" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"exponentiatedexponential" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"exponentiatedlogistic" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"exponentiatedweibull" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"F" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"fellerpareto" = { ## [R(mini), R+, R+, R+, R+]
alpha = matrix(0,1,5)
alpha[1] = param[1]
alpha[2:5] = log(param[2:5])
} ,
"fisk" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"foldednormal" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"frechet" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"gamma" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"gammapoisson" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"gaussian" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"gev" = { ## [R, R+, R]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
} ,
"geninvbeta" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"genlogis" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"genlogis3" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"genlogis4" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"geometric" = { ## [01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"generalizedhyperbolic" = { ## [R, R+, R+, R, R] [mu, delta, alpha, beta, lambda] (avec alpha^2 > beta^2)
alpha = matrix(0,1,5)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log(param[3])
alpha[4] = log( (param[4]+param[3]) / (param[3]-param[4]) )
alpha[5] = param[5]
} ,
"generalizedlambda" = { ## [R, R+, R, R]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
alpha[4] = param[4]
} ,
"generalizedt" = { ## [R, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2:4] = log(param[2:4])
} ,
"gompertz" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"gpd" = { ## [R, R+, R]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
} ,
"gumbel" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"gumbel2" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"halfcauchy" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"halflogistic" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"halfnormal" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"halft" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"hjorth" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"hblaplace" = { ## [01, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1]/(1-param[1]))
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"hyperbolic" = { ## [R, R+, R+, R]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log(param[3])
alpha[4] = log( (param[4]+param[3]) / (param[3]-param[4]) )
} ,
"hzeta" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"huber" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"inversebeta" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inverseburr" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"inversechisquared" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"inverseexponential" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"inverseexpexponential" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inversegamma" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inverselomax" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inverseparalogistic" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inversepareto" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inversetransformedgamma" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"inverseweibull" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"kumaraswamy" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"kumaraswamyexponential" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"kumaraswamygamma" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"kumaraswamygumbel" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"kumaraswamyhalfnormal" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"kumaraswamyloglogistic" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"kumaraswamynormal" = { ## [R, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2:4] = log(param[2:4])
} ,
"kumaraswamyweibull" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"laplace" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"levy" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"linearfailurerate" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"lindley" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"libbynovickbeta" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"logcauchy" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"loggamma" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"loggumbel" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"loglaplace" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"loglog" = { ## [R+, >1]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(exp(-param[2]) + 1)
} ,
"loglogistic" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"lognormal" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"lognormal3" = { ## [R, R+, R]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
} ,
"logistic" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"logisticexponential" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"logisticrayleigh" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"logseries" = { ## [01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"lomax" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"makeham" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"maxwell" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"mcgilllaplace" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"moexponential" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"moweibull" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"nakagami" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"ncchisquared" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"ncF" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"negativebinomial" = { ## [N+, 01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"normalinversegaussian" = { ## [R, R+, R+, R]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log(param[3])
alpha[4] = log( (param[4]+param[3]) / (param[3]-param[4]) )
} ,
"nsbeta" = { ## [R+, R+, R(min), R(maxi)]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3:4] = param[3:4]
} ,
"paralogistic" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"pareto" = { ## [R+, R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"paretopositivestable" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"pareto1" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"pareto2" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"pareto3" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"pareto4" = { ## [R, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2:4] = log(param[2:4])
} ,
"perks" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"pctalaplace" = { ## [R+, R]
alpha = matrix(0,1,2)
alpha[1] = log(param[1])
alpha[2] = param[2]
} ,
"poisson" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"power1" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"power2" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"powerdistribution" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"powerexponential" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"rayleigh" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"reflectedgamma" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"rice" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"scaledchisquared" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"schabe" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"simplex" = { ## [01, R+]
alpha = matrix(0,1,2)
alpha[1] = log(param[1]/ (1-param[1]))
alpha[2] = log(param[2])
} ,
"skewedlaplace" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"skewedt" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"skewedtfourparam" = { ## [R, R+, R, R+(<25)]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
alpha[4] = log(param[4]/(25-param[4]))
} ,
"skewednormal" = { ## [R, R+, R, R]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
} ,
"skewedgeneralizedt" = { ## [R, R+, -1+1, R+(>1), R+(>1)]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log( (param[3]+param[2]) / (param[2]-param[3]) )
alpha[4] = log(exp(-param[4]) + 1)
} ,
"skewedexponentialpower" = { ## [R, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"slash" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"stacy" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"t" = { ## [R, R+, R+ (<25)]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log(param[3]/(25-param[3]))
} ,
"tobit" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"topple" = { ## [01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"transformedbeta" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"transformedgamma" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"truncatednormal" = { ## [R, R+, R(min), R(max)]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3:4] = param[3:4]
} ,
"truncatedpareto" = { ## [R+(mini), R+(maxi), R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"twosidedpower" = { ## [01, R+]
alpha = matrix(0,1,2)
alpha[1] = log(param[1]/(1-param[1]))
alpha[2] = log(param[2])
} ,
"wald" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"weibull" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"xie" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"yules" = { ## [R+] >0.5
alpha = matrix(0,1,1)
alpha[1] = log(exp(-param[1]) + 0.5)
} ,
)
return(alpha)
}
Try the GenHMM1d package in your browser
Any scripts or data that you put into this service are public.
GenHMM1d documentation built on Jan. 21, 2021, 9:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions theta2alpha
Documented in theta2alpha
#'@title Transform constrained parameters to unconstrained parameters
#'
#'@description This function computes the unconstrained parameters alpha of a univariate distribution corresponding to constrainted parameters theta.
#'
#'@param family distribution name; run the command distributions() for help
#'@param param constrained parameters of the univariate distribution
#'
#'@return \item{alpha}{constrained parameters}
#'
#'
#'
#'@export
#'@keywords internal
theta2alpha<-function(family,param){
switch(family,
"asymexppower" = { ## [R+, R+, 01]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
alpha[3] = log(param[3]/(1-param[3]))
} ,
"asymlaplace" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"asympower" = { ## [01, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1]/(1-param[1]))
alpha[2:3] = log(param[2:3])
} ,
"asymt" = { ## [R+, R+, 01]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = log(param[3]/(1-param[3]))
alpha[4] = param[4]
} ,
"beard" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"benini" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"benford" = { ## [1 ou 2]
alpha = matrix(0,1,1)
if (param[1]>log(2)){
alpha[1] = log(2)
} else if (param[1]<0){
alpha[1] = 0
} else {
alpha[1] = log(param[1])
}
} ,
"bernoulli" = { ## [01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"beta" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"betabinomial" = { ## [N+, R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"betageometric" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"betanegativebinomial" = { ## [N+, R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"betaburr" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betaburr7" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betaexponential" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"betafrechet" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betagompertz" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betagumbel" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"betagumbel2" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betalognormal" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"betalomax" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"betanormal" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"betaprime" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"betaweibull" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"bhattacharjee" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"binomial" = { ## [N+, 01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"birnbaumsaunders" = { ## [R+, R+, R]
alpha = matrix(0,1,3)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
} ,
"boxcox" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"burr" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"burr2param" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"cauchy" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"chen" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"chi" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"chisquared" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"clg" = { ## [R+, R+, R]
alpha = matrix(0,1,3)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
} ,
"complementarybeta" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"dagum" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"diffzeta" = { ## [R+, >1]
alpha = matrix(0,1,2)
alpha[1] = log(param[1])
alpha[2] = log(exp(-param[2]) + 1)
} ,
"discretegamma" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"discretelaplace" = { ## [R, 01]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])/(1-param[2])
} ,
"discretenormal" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"discreteweibull" = { ## [01, R+]
alpha = matrix(0,1,2)
alpha[1] = log(param[1]/(1-param[1]))
alpha[2] = log(param[2])
} ,
"doubleweibull" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"ev" = {
## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"exponential" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"exponentialextension" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"exponentialgeometric" = { ## [R+, 01]
alpha = matrix(0,1,2)
alpha[1] = log(param[1])
alpha[2] = log(param[2]/(1-param[2]))
} ,
"exponentiallogarithmic" = { ## [R+, 01]
alpha = matrix(0,1,2)
alpha[1] = log(param[1])
alpha[2] = log(param[2]/(1-param[2]))
} ,
"exponentialpoisson" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"exponentialpower" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"exponentiatedexponential" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"exponentiatedlogistic" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"exponentiatedweibull" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"F" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"fellerpareto" = { ## [R(mini), R+, R+, R+, R+]
alpha = matrix(0,1,5)
alpha[1] = param[1]
alpha[2:5] = log(param[2:5])
} ,
"fisk" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"foldednormal" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"frechet" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"gamma" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"gammapoisson" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"gaussian" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"gev" = { ## [R, R+, R]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
} ,
"geninvbeta" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"genlogis" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"genlogis3" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"genlogis4" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"geometric" = { ## [01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"generalizedhyperbolic" = { ## [R, R+, R+, R, R] [mu, delta, alpha, beta, lambda] (avec alpha^2 > beta^2)
alpha = matrix(0,1,5)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log(param[3])
alpha[4] = log( (param[4]+param[3]) / (param[3]-param[4]) )
alpha[5] = param[5]
} ,
"generalizedlambda" = { ## [R, R+, R, R]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
alpha[4] = param[4]
} ,
"generalizedt" = { ## [R, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2:4] = log(param[2:4])
} ,
"gompertz" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"gpd" = { ## [R, R+, R]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
} ,
"gumbel" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"gumbel2" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"halfcauchy" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"halflogistic" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"halfnormal" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"halft" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"hjorth" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"hblaplace" = { ## [01, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1]/(1-param[1]))
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"hyperbolic" = { ## [R, R+, R+, R]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log(param[3])
alpha[4] = log( (param[4]+param[3]) / (param[3]-param[4]) )
} ,
"hzeta" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"huber" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"inversebeta" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inverseburr" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"inversechisquared" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"inverseexponential" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"inverseexpexponential" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inversegamma" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inverselomax" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inverseparalogistic" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inversepareto" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"inversetransformedgamma" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"inverseweibull" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"kumaraswamy" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"kumaraswamyexponential" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"kumaraswamygamma" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"kumaraswamygumbel" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"kumaraswamyhalfnormal" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"kumaraswamyloglogistic" = { ## [R+, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"kumaraswamynormal" = { ## [R, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2:4] = log(param[2:4])
} ,
"kumaraswamyweibull" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"laplace" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"levy" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"linearfailurerate" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"lindley" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"libbynovickbeta" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"logcauchy" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"loggamma" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"loggumbel" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"loglaplace" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"loglog" = { ## [R+, >1]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(exp(-param[2]) + 1)
} ,
"loglogistic" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"lognormal" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"lognormal3" = { ## [R, R+, R]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
} ,
"logistic" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"logisticexponential" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"logisticrayleigh" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"logseries" = { ## [01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"lomax" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"makeham" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"maxwell" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"mcgilllaplace" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"moexponential" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"moweibull" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"nakagami" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"ncchisquared" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"ncF" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"negativebinomial" = { ## [N+, 01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"normalinversegaussian" = { ## [R, R+, R+, R]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log(param[3])
alpha[4] = log( (param[4]+param[3]) / (param[3]-param[4]) )
} ,
"nsbeta" = { ## [R+, R+, R(min), R(maxi)]
alpha = matrix(0,1,4)
alpha[1:2] = log(param[1:2])
alpha[3:4] = param[3:4]
} ,
"paralogistic" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"pareto" = { ## [R+, R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"paretopositivestable" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"pareto1" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"pareto2" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"pareto3" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"pareto4" = { ## [R, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2:4] = log(param[2:4])
} ,
"perks" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"pctalaplace" = { ## [R+, R]
alpha = matrix(0,1,2)
alpha[1] = log(param[1])
alpha[2] = param[2]
} ,
"poisson" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"power1" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"power2" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"powerdistribution" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"powerexponential" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"rayleigh" = { ## [R+]
alpha = matrix(0,1,1)
alpha[1] = log(param[1])
} ,
"reflectedgamma" = { ## [R+, R, R+]
alpha = matrix(0,1,3)
alpha[1] = log(param[1])
alpha[2] = param[2]
alpha[3] = log(param[3])
} ,
"rice" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"scaledchisquared" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"schabe" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"simplex" = { ## [01, R+]
alpha = matrix(0,1,2)
alpha[1] = log(param[1]/ (1-param[1]))
alpha[2] = log(param[2])
} ,
"skewedlaplace" = { ## [R, R+, R+]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2:3] = log(param[2:3])
} ,
"skewedt" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"skewedtfourparam" = { ## [R, R+, R, R+(<25)]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
alpha[4] = log(param[4]/(25-param[4]))
} ,
"skewednormal" = { ## [R, R+, R, R]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
} ,
"skewedgeneralizedt" = { ## [R, R+, -1+1, R+(>1), R+(>1)]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log( (param[3]+param[2]) / (param[2]-param[3]) )
alpha[4] = log(exp(-param[4]) + 1)
} ,
"skewedexponentialpower" = { ## [R, R+, R, R+]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = param[3]
alpha[4] = log(param[4])
} ,
"slash" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"stacy" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"t" = { ## [R, R+, R+ (<25)]
alpha = matrix(0,1,3)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3] = log(param[3]/(25-param[3]))
} ,
"tobit" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"topple" = { ## [01]
alpha = matrix(0,1,1)
alpha[1] = log(param[1]/(1-param[1]))
} ,
"transformedbeta" = { ## [R+, R+, R+, R+]
alpha = matrix(0,1,4)
alpha[1:4] = log(param[1:4])
} ,
"transformedgamma" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"truncatednormal" = { ## [R, R+, R(min), R(max)]
alpha = matrix(0,1,4)
alpha[1] = param[1]
alpha[2] = log(param[2])
alpha[3:4] = param[3:4]
} ,
"truncatedpareto" = { ## [R+(mini), R+(maxi), R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"twosidedpower" = { ## [01, R+]
alpha = matrix(0,1,2)
alpha[1] = log(param[1]/(1-param[1]))
alpha[2] = log(param[2])
} ,
"wald" = { ## [R, R+]
alpha = matrix(0,1,2)
alpha[1] = param[1]
alpha[2] = log(param[2])
} ,
"weibull" = { ## [R+, R+]
alpha = matrix(0,1,2)
alpha[1:2] = log(param[1:2])
} ,
"xie" = { ## [R+, R+, R+]
alpha = matrix(0,1,3)
alpha[1:3] = log(param[1:3])
} ,
"yules" = { ## [R+] >0.5
alpha = matrix(0,1,1)
alpha[1] = log(exp(-param[1]) + 0.5)
} ,
)
return(alpha)
}
Try the GenHMM1d package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.