Imports: corpcor, ggplot2, ggrepel, gridExtra, Matrix, methods,
R.utils, reticulate (>= 1.25), rstudioapi
version = "1.7.1"), list(package = "numpy", version =
"1.22.4"), list(package = "matplotlib"), list(package =
"scikit-learn"), list(package = "six"), list(package =
"pylearn-parsimony", version = "0.3.1", pip = TRUE) ) )

Title: Model Averaging for Multivariate GLM with Null Models
Author: Masatoshi Katabuchi and Akihiro Nakamura
Description: Tools for univariate and multivariate generalized linear models with model averaging and null model technique.

Title: Estimating Mixed Graphical Models
Description: Functions to estimate and sample from a mixed Graphical model.
Imports: Rcpp (>= 0.11.4), matrixcalc, glmnet

Description: Estimation of Mixed Graphical model using L1-regularized neighborhood regression.

Title: Estimating Time-Varying Mixed Graphical Models
Description: Estimation of time-varying Mixed Graphical model using L1-regularized neighborhood regression.

Description: Estimation of time-varying Mixed Graphical models and mixed VAR models using L1-regularized neighborhood regression.

Title: Estimating Time-Varying k-Order Mixed Graphical Models
Description: Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression.
Imports: matrixcalc, glmnet, stringr, Hmisc

Description: Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.
Imports: matrixcalc, glmnet, stringr, Hmisc, qgraph, gtools

Imports: glmnet, stringr, Hmisc, qgraph, gtools

Description: Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see linked paper.

Title: Functions for multivariate generalized Pareto distribution (MGPD
Description: Extends density functions to parametric multivariate
generalized Pareto distributions (MGPD of Type II), and
provides fitting functions which calculate maximum likelihood
estimates for bivariate models (BGPD). There are seven
"classical" parametric dependence models within BGPD available:
logistic, negative logistic, bilogistic, negative bilogistic,
Tajvidi's generalised symmetric logistic, Dirichlet and mixed.
Beyond these, two novel asymmetric approaches giving absolutely
continuous BGPD are implemented as psi-logistic and