R/greybox-package.R

In config-i1/greybox: Toolbox for Model Building and Forecasting

#' Grey box
#'
#' Toolbox for working with univariate models for purposes of analysis and forecasting
#'
#' \tabular{ll}{ Package: \tab greybox\cr Type: \tab Package\cr Date: \tab
#' 2018-02-13 - Inf\cr License: \tab GPL-2 \cr } The following functions are
#' included in the package:
#' \itemize{
#' \item \link[greybox]{AICc} and \link[greybox]{BICc} - AIC / BIC corrected for the
#' sample size.
#' \item \link[greybox]{pointLik} - point likelihood of the function.
#' \item \link[greybox]{pAIC}, \link[greybox]{pAICc}, \link[greybox]{pBIC},
#' \link[greybox]{pBICc} - point versions of respective information criteria.
#' \item \link[greybox]{coefbootstrap} - Method that uses a simple implementation of the
#' case resampling to get bootstrapped estimates of parameters of the model.
#' \item \link[greybox]{dsrboot} - Bootstrap inspired by the meboot package, that creates
#' bootstraped series based on the provided one.
#' \item \link[greybox]{determination} - Coefficients of determination between different
#' exogenous variables.
#' \item \link[greybox]{temporaldummy} - Matrix with seasonal dummy variables.
#' \item \link[greybox]{outlierdummy} - Matrix with dummies for outliers.
#' \item \link[greybox]{alm} - Advanced Linear Model - regression, estimated using
#' likelihood with specified distribution (e.g. Laplace or Chi-Squared).
#' \item \link[greybox]{sm} - Scale Model - Regression model for scale of distribution
#' (e.g. for Variance of Normal distribution). Requires an \code{lm()} or \code{alm()}
#' model.
#' \item \link[greybox]{stepwise} - Stepwise based on information criteria and partial
#' correlations. Efficient and fast.
#' \item \link[greybox]{xregExpander} - Function that expands the provided data into
#' the data with lags and leads.
#' \item \link[greybox]{xregTransformer} - Function produces mathematical transformations
#' of the variables, such as taking logarithms, square roots etc.
#' \item \link[greybox]{xregMultiplier} - Function produces cross-products of the
#' matrix of the provided variables.
#' \item \link[greybox]{lmCombine} - Function combines lm models from the estimated
#' based on information criteria weights.
#' \item \link[greybox]{lmDynamic} - Dynamic regression based on point AIC.
#' \item \link[greybox]{ro} - Rolling origin evaluation.
#' \item \link[greybox]{Distributions} - document explaining the distribution functions
#' of the greybox package.
#' \item \link[greybox]{spread} - function that produces scatterplots / boxplots / tableplots,
#' depending on the types of variables.
#' \item \link[greybox]{assoc} - function that calculates measures of association,
#' depending on the types of variables.
#' }
#'
#' @name greybox
#' @aliases greybox-package
#' @template author
#'
#' @seealso \code{\link[greybox]{stepwise}, \link[greybox]{lmCombine}}
#'
#' @template keywords
#'
#' @examples
#'
#' \donttest{
#' xreg <- cbind(rnorm(100,10,3),rnorm(100,50,5))
#' xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rnorm(100,0,3),xreg,rnorm(100,300,10))
#' colnames(xreg) <- c("y","x1","x2","Noise")
#'
#' stepwise(xreg)
#'}
#'
#' @importFrom graphics abline layout legend lines par points polygon plot
#' @importFrom stats AIC BIC logLik cov deltat end frequency is.ts cor start time ts var lm as.formula residuals
#' @importFrom utils packageVersion
NULL