Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.

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>.

Allows Brownian motion, fractional Brownian motion,
    and integrated Ornstein-Uhlenbeck process components to
    be added to linear and non-linear mixed effects models
    using the structures and methods of the 'nlme' package.

Combine partial covariance matrices using a Wishart-EM algorithm. 
    Methods are described in the November 2019 article by Akdemir et al. 
    <https://www.biorxiv.org/content/10.1101/857425v1>.
    It can be used to combine partially overlapping covariance matrices 
    from independent trials, partially overlapping multi-view relationship
    data from genomic experiments, partially overlapping Gaussian graphs
    described by their covariance structures. 
    High dimensional covariance estimation, 
    multi-view data integration.
    high dimensional covariance graph estimation.

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.