Package: porridge 0.3.3

Wessel N. van Wieringen

porridge: Ridge-Type Penalized Estimation of a Potpourri of Models

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) 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). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, <doi:10.1080/10618600.2022.2035231>).

Authors:Wessel N. van Wieringen [aut, cre], Mehran Aflakparast [ctb]

porridge_0.3.3.tar.gz
porridge_0.3.3.tar.gz(r-4.5-noble)porridge_0.3.3.tar.gz(r-4.4-noble)
porridge_0.3.3.tgz(r-4.4-emscripten)porridge_0.3.3.tgz(r-4.3-emscripten)
porridge.pdf |porridge.html
porridge/json (API)
NEWS

# Install 'porridge' in R:
install.packages('porridge', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.00 score 224 downloads 21 exports 7 dependencies

Last updated 10 months agofrom:928649f5e7. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 01 2024
R-4.5-linux-x86_64OKDec 01 2024

Exports:genRidgePenaltyMatmakeFoldsGLMcvoptPenaltyGGMmixture.kCVautooptPenaltyGLM.kCVautooptPenaltyGLMmultiT.kCVautooptPenaltyPgen.kCVauto.bandedoptPenaltyPgen.kCVauto.groupsoptPenaltyPmultiT.kCVautooptPenaltyPrep.kCVautooptPenaltyPrepEdiag.kCVautoridgeGGMmixtureridgeGLMridgeGLMdofridgeGLMmultiTridgePgenridgePgen.kCVridgePgen.kCV.bandedridgePgen.kCV.groupsridgePmultiTridgePrepridgePrepEdiag

Dependencies:latticeMASSMatrixmvtnormpracmaRcppRcppArmadillo

Readme and manuals

Help Manual

Help pageTopics
Ridge-Type Penalized Estimation of a Potpourri of Models.porridge-package porridge
Penalty parameter matrix for generalized ridge regression.genRidgePenaltyMat
Generate folds for cross-validation of generalized linear models.makeFoldsGLMcv
Automatic search for optimal penalty parameter (mixture of GGMs).optPenaltyGGMmixture.kCVauto
Automatic search for optimal penalty parameters of the targeted ridge GLM estimator.optPenaltyGLM.kCVauto
Automatic search for optimal penalty parameters of the targeted ridge GLM estimator.optPenaltyGLMmultiT.kCVauto
Automatic search for optimal penalty parameter (generalized ridge precision).optPenaltyPgen.kCVauto.banded
Automatic search for optimal penalty parameter (generalized ridge precision).optPenaltyPgen.kCVauto.groups
Automatic search for optimal penalty parameter (ridge precision with multi-targets).optPenaltyPmultiT.kCVauto
Automatic search for optimal penalty parameters (for precision estimation of data with replicates).optPenaltyPrep.kCVauto
Automatic search for optimal penalty parameters (for precision estimation of data with replicates).optPenaltyPrepEdiag.kCVauto
Ridge penalized estimation of a mixture of GGMs.ridgeGGMmixture
Ridge estimation of generalized linear models.ridgeGLM
Degrees of freedom of the generalized ridge estimator.ridgeGLMdof
Multi-targeted ridge estimation of generalized linear models.ridgeGLMmultiT
Ridge estimation of the inverse covariance matrix with element-wise penalization and shrinkage.ridgePgen
K-fold cross-validated loglikelihood of ridge precision estimator.ridgePgen.kCV
K-fold cross-validated loglikelihood of ridge precision estimator for banded precisions.ridgePgen.kCV.banded
K-fold cross-validated loglikelihood of ridge precision estimator with group-wise penalized variates.ridgePgen.kCV.groups
Ridge estimation of the inverse covariance matrix with multi-target shrinkage.ridgePmultiT
Ridge penalized estimation of the precision matrix from data with replicates.ridgePrep
Ridge penalized estimation of the precision matrix from data with replicates.ridgePrepEdiag