Package: porridge
Type: Package
Title: Ridge-Type Penalized Estimation of a Potpourri of Models
Version: 0.3.0
Date: 2022-01-07
Author: Wessel N. van Wieringen [aut, cre],
  Mehran Aflakparast [ctb] (part of the R-code of the mixture
    functionality)
Maintainer: Wessel N. van Wieringen <w.vanwieringen@vumc.nl>
Authors@R: c(person("Wessel N.", "van Wieringen", role = c("aut", "cre"),
	             email="w.vanwieringen@vumc.nl"),
	     person("Mehran", "Aflakparast", role = "ctb",
	            comment="part of the R-code of the mixture functionality"))
Description: The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021, <doi:10.1002/sim.9028>) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, <doi:10.1016/j.jmva.2020.104621>) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, <doi:10.1080/10618600.2019.1604374>). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018, <doi:10.1002/bimj.201700102>).
License: GPL (>= 2)
LazyLoad: yes
URL: https://www.few.vu.nl/~wvanwie/
Depends: R (>= 2.15.1)
Imports: MASS, Matrix, stats, mvtnorm, Rcpp, methods
Suggests:
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2022-01-08 20:54:57 UTC; wessel
Repository: CRAN
Date/Publication: 2022-01-08 21:12:41 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-21 05:59:00 UTC; windows
Archs: i386, x64
