proposed in the actuarial and demographic literature including the
It includes functions for fitting mortality models, analysing their
goodness-of-fit and performing mortality projections and simulations.
Suggests: demography, knitr, xtable, MTS

Description: With this package you can simulate several lines into the
QQnorm-Plot and the TA-plot under Gaussian distribution

QQ-Norm-Plot under the assumption of Gaussian distribution

Title: Plots a QQ-Norm Plot with several Gaussian simulations.
Description: Plots a QQ-Norm Plot with several Gaussian simulations.

Title: Quantile-Quantile Plot with Several Gaussian Simulations

Title: Plots a QQ-Norm Plot with Several Gaussian Simulations

Title: Student's t Vector Autoregression (StVAR).
Description: This package contains a function to estimate
heteroskedastic Student's t Vector Autoregression (StVAR) for a
given degrees of freedom and lag length.

Title: Student's t Vector Autoregression (StVAR)
multivariate Student's t dynamic regression models for a given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form.

Title: Stabilizing Regression and Variable Selection
person("Niklas", "Pfister", email="np@math.ku.dk", role=c("aut", "cre")),
Description: Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2019) <arXiv:1911.01850>. The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors.
Imports: MASS, R6, glmnet, corpcor, ggplot2, ggrepel

Description: Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) <arXiv:1911.01850>. The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors.