Imports: stats, expm, reshape, ggplot2, igraph, Hmisc

Imports: igraph, stats, expm, reshape, ggplot2, Hmisc, fdrtool,

Author: Carel F.W. Peeters [cre, aut], Anders Ellern Bilgrau [aut], Wessel N. van Wieringen [aut]
Description: Proper L2-penalized ML estimators for the
precision matrix as well as supporting functions to employ these estimators
Imports: igraph, stats, methods, expm, reshape, ggplot2, Hmisc,
fdrtool, snowfall, sfsmisc, utils, grDevices, graphics

fdrtool, snowfall, sfsmisc, utils, grDevices, graphics, gRbase,

Description: Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details see van Wieringen & Peeters (2016) <doi:10.1016/j.csda.2016.05.012> and associated publications.

Maintainer: Carel F.W. Peeters <carel.peeters@wur.nl>
Description: Proper L2-penalized maximum likelihood estimators for precision
matrices and supporting functions to employ these estimators in a graphical
modeling setting. For details see van Wieringen & Peeters (2016)
Suggests: KEGGgraph, testthat, knitr, rmarkdown
Author: Carel F.W. Peeters [aut, cre, cph]
Wessel N. van Wieringen [aut] (<https://orcid.org/0000-0002-5100-9123>)

modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022)

Title: Ridge Estimation of the Vector Auto-Regressive (VAR) Processes
Author: Wessel N. van Wieringen <w.vanwieringen@vumc.nl>
Description: Ridge maximum likelihood estimation of vector auto-regressive processes and supporting functions for their exploitation.
Depends: R (>= 2.15.1), rags2ridges (>= 2.0), gRbase, igraph
Imports: expm, fdrtool, graph, graphics, grDevices, marray, Matrix,

Title: Ridge Estimation of Vector Auto-Regressive (VAR) Processes

Depends: R (>= 2.15.1), gRbase, igraph, rags2ridges,

Imports: abind, expm, fdrtool, graph, graphics, grDevices, marray,
MASS, Matrix, methods, mvtnorm, RBGL, stats, utils

Description: The ragt2ridges-package provides ridge maximum likelihood estimation of vector auto-regressive processes: the VAR(1), VAR(2) and VARX(1) model (more to be added). Prior knowledge may be incorporated in the estimation through a) specification of the edges believed to be absent in the time series chain graph, and b) a shrinkage target towards which the parameter estimate is shrunken for large penalty parameter values. Estimation functionality is accompanied by methodology for penalty parameter selection. In addition, the package offers supporting functionality for the exploitation of estimated models. Among others, i) a procedure to infer the support of the non-sparse ridge estimate (and thereby of the time series chain graph) is implemented, ii) a table of node-wise network summary statistics, iii) mutual information analysis, and iv) impulse response analysis. Cf. Miok et al. (2017) <DOI:10.1002/bimj.201500269> and follow-up work for details on the implemented methods.
Depends: R (>= 2.15.1), igraph, rags2ridges,
Imports: abind, expm, fdrtool, graphics, grDevices, marray, MASS,