'sepindex.R' 'couples.R' 'blocks.R' 'covablocks.R'
'covastat.R' 'covaprop.R' 'covastatM.R' 'dataprep.R' 'vv_13.R'

utils, stats, graphics, methods, matrixcalc, lubridate, zoo,
gstat, sp (>= 0.9-72), spacetime (>= 1.0-0)
'sepindex.R' 'couples.R' 'blocks.R' 'covablocks.R'
'covastat.R' 'covaprop.R' 'covastatM.R' 'read.STdata.R'
'vv_13.R'

Tests on properties of space-time covariance functions.
Tests on symmetry, separability and for assessing
different forms of non-separability are available. Moreover tests on
some classes of covariance functions, such that the classes of
product-sum models, Gneiting models and integrated product models have
been provided.  It is the companion R package to the paper of
Cappello C., De Iaco S. and Posa D. <doi:10.1007/s00477-017-1472-2>.

Tests on properties of space-time covariance functions.
Tests on symmetry, separability and for assessing
different forms of non-separability are available. Moreover tests on
some classes of covariance functions, such that the classes of
product-sum models, Gneiting models and integrated product models have
been provided.  It is the companion R package to the papers of
Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability
and some classes of space-time covariance function models <doi:10.1007/s00477-017-1472-2>
and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for
selecting a class of space-time covariance functions <doi:10.18637/jss.v094.i01>.

utils, stats, graphics, methods, lubridate, zoo, gstat, sp (>=
0.9-72), spacetime (>= 1.0-0)

c(person("Jacob", "Helwig", email = "jacob.a.helwig@tamu.edu", role = c("cre", "aut")),
person("Sutanoy", "Dasgupta", email = "sutanoy@stat.tamu.edu", role = c("aut")),
person("Peng", "Zhao", email = "pzhao@stat.tamu.edu", role = c("aut")),
person("Bani", "Mallick", email = "bmallick@stat.tamu.edu", role = c("aut")),
person("Debdeep", "Pati", email = "debdeep@stat.tamu.edu", role = c("aut")))
A covariate-dependent approach to Gaussian graphical modeling as described in Dasgupta et al. (2022). Employs a novel weighted pseudo-likelihood approach to model the conditional dependence structure of data as a continuous function of an extraneous covariate. The main function, covdepGE::covdepGE(), estimates a graphical representation of the conditional dependence structure via a block mean-field variational approximation, while several auxiliary functions (inclusionCurve(), matViz(), and plot.covdepGE()) are included for visualizing the resulting estimates.
doParallel, foreach, ggplot2, glmnet, latex2exp, MASS,
parallel, Rcpp, reshape2, stats
Jacob Helwig [cre, aut],
Sutanoy Dasgupta [aut],
Peng Zhao [aut],
Bani Mallick [aut],
Debdeep Pati [aut]