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

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

Estimating Time-Varying k-Order Mixed Graphical Models
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression.

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>.
matrixcalc, glmnet, stringr, Hmisc, qgraph, gtools

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.

Functions for multivariate generalized Pareto distribution (MGPD
of Type II)
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
psi-negative logistic models.

mgpd: Functions for multivariate generalized Pareto distribution
(MGPD of Type II)
R (>= 2.10.1), evd, numDeriv, corpcor, fields
Extends distribution and density functions to parametric
multivariate generalized Pareto distributions (MGPD of Type
II), and provides fitting functions which calculate maximum
likelihood estimates for bivariate and trivariate models. (Help
is under progress)

Graphing map attributes and non-map variables in R
Each function in the package performs three main functions
i) it reads spatial data and produces basic graphs including
pie chart, bar chart, box plots, histogram, scatter plots, and
lines ii)it reads non-spatial data such as "csv", "txt", "dat"
data and produces basic graphs and iii) it plots map(s) of the
input attribute(s) of spatial data by setting "type" parameter
to "map"