greybox (version 2.0.5)

greybox: Grey box

Description

Toolbox for working with univariate models for purposes of analysis and forecasting

Author

Ivan Svetunkov,

Details

Package:	greybox
Type:	Package
Date:	2018-02-13 - Inf
License:	GPL-2

The following functions are included in the package:

• AICc and BICc - AIC / BIC corrected for the sample size.

• pointLik - point likelihood of the function.

• pAIC, pAICc, pBIC, pBICc - point versions of respective information criteria.

• coefbootstrap - Method that uses a simple implementation of the case resampling to get bootstrapped estimates of parameters of the model.

• dsrboot - Bootstrap inspired by the meboot package, that creates bootstraped series based on the provided one.

• determination - Coefficients of determination between different exogenous variables.

• temporaldummy - Matrix with seasonal dummy variables.

• outlierdummy - Matrix with dummies for outliers.

• alm - Advanced Linear Model - regression, estimated using likelihood with specified distribution (e.g. Laplace or Chi-Squared).

• sm - Scale Model - Regression model for scale of distribution (e.g. for Variance of Normal distribution). Requires an lm() or alm() model.

• stepwise - Stepwise based on information criteria and partial correlations. Efficient and fast.

• xregExpander - Function that expands the provided data into the data with lags and leads.

• xregTransformer - Function produces mathematical transformations of the variables, such as taking logarithms, square roots etc.

• xregMultiplier - Function produces cross-products of the matrix of the provided variables.

• lmCombine - Function combines lm models from the estimated based on information criteria weights.

• lmDynamic - Dynamic regression based on point AIC.

• ro - Rolling origin evaluation.

• Distributions - document explaining the distribution functions of the greybox package.

• spread - function that produces scatterplots / boxplots / tableplots, depending on the types of variables.

• assoc - function that calculates measures of association, depending on the types of variables.

See Also

stepwise, lmCombine

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)
# }