c(
person("Sacha", "Epskamp", email = "mail@sachaepskamp.com",role = c("aut", "cre")),
person("Eiko I.", "Fried", role = c("ctb"))
)
methods, igraph, IsingFit, qgraph, dplyr (>= 0.3.0.2), tidyr,
gtools, corpcor, IsingSampler, mvtnorm, abind, Matrix,
parallel, huge, parcor, mgm (>= 1.2), relaimpo, NetworkToolbox
(>= 1.1.0), pbapply, graphicalVAR, BDgraph, psych,
networktools, lavaan, glasso
Sacha Epskamp [aut, cre],
Eiko I. Fried [ctb]

methods, igraph, IsingFit, qgraph, dplyr (>= 0.3.0.2), tidyr,
gtools, corpcor, IsingSampler, mvtnorm, abind, Matrix,
parallel, huge, parcor, mgm (>= 1.2), relaimpo, NetworkToolbox
(>= 1.1.0), pbapply, graphicalVAR, BDgraph, psychTools,
networktools, lavaan, glasso

methods, igraph, IsingFit, qgraph, dplyr (>= 0.3.0.2), tidyr,
gtools, corpcor, IsingSampler, mvtnorm, abind, Matrix,
parallel, huge, parcor, mgm (>= 1.2), relaimpo, NetworkToolbox
(>= 1.1.0), pbapply, graphicalVAR, BDgraph, psych,
networktools, lavaan

methods, igraph, IsingFit, qgraph, dplyr (>= 0.3.0.2), tidyr,
gtools, corpcor, IsingSampler, mvtnorm, abind, Matrix,
parallel, huge, mgm (>= 1.2), relaimpo, NetworkToolbox (>=
1.1.0), pbapply, graphicalVAR, BDgraph, psychTools,
networktools, lavaan, glasso
Bootstrap methods to assess accuracy and stability of estimated network structures
and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible
specification of any undirected network estimation procedure in R, and offers
default sets for various estimation routines.

methods, igraph, IsingFit, qgraph, dplyr (>= 0.3.0.2), tidyr,
gtools, corpcor, IsingSampler, mvtnorm, abind, Matrix, snow,
mgm (>= 1.2), NetworkToolbox (>= 1.1.0), pbapply, networktools,
rlang, tibble
glasso, GGMncv, BDgraph, graphicalVAR, relaimpo, lavaan,
psychTools, huge

A bootstrap test for the probability of ruin in the classical
risk process
This package provides a framework for testing the
probability of ruin in the classical (compound Poisson) risk
process. It also includes some procedures for assessing and
comparing the performance between the bootstrap test and the
test using asymptotic normality.