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, ) 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, ). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018, ).


Reference manual

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0.3.0 by Wessel N. van Wieringen, 17 days ago


Browse source code at https://github.com/cran/porridge

Authors: Wessel N. van Wieringen [aut, cre] , Mehran Aflakparast [ctb] (part of the R-code of the mixture functionality)

Documentation:   PDF Manual  

GPL (>= 2) license

Imports MASS, Matrix, stats, mvtnorm, Rcpp, methods

Linking to Rcpp, RcppArmadillo

See at CRAN