Description: The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated),
comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation.
The estimation is based on latent copula Gaussian models.

Imports: stats, pcaPP, fMultivar, mnormt, chebpol, Matrix, MASS,
Suggests: microbenchmark, rmarkdown, markdown, knitr, testthat (>=

Imports: stats, pcaPP, fMultivar, mnormt, Matrix, MASS, heatmaply,

For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017).
For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.
ggplot2, plotly, graphics, geometry, doFuture, foreach, future,
Suggests: rmarkdown, markdown, knitr, testthat (>= 3.0.0), lattice,

Title: Functions for Graphical Model with Latent Variables
Author: Yanxin Jin, Samantha Yang, Kean Ming Tan
Description: Three methods are provided to estimate graphical model with latent variables: Chandrasekaran, V., Parrilo, P. A. & Willsky, A. S. (2010) <arXiv:1008.1290>, Tan, K. M., Ning, Y., Witten, D. M. & Liu, H. (2016) <doi:10.1093/biomet/asw050> and Yanxin Jin, Yang Ning, and Kean Ming Tan (2020) (submitted).
Imports: Rcpp, pracma, glmnet, MASS, stats

Title: Graphical Models with Latent Variables
Description: Three methods are provided to estimate graphical models with latent variables: (1) Jin, Y., Ning, Y., and Tan, K. M. (2020) (preprint available); (2) Chandrasekaran, V., Parrilo, P. A. & Willsky, A. S. (2012) <doi:10.1214/11-AOS949>; (3) Tan, K. M., Ning, Y., Witten, D. M. & Liu, H. (2016) <doi:10.1093/biomet/asw050>.

Title:  Latent position and cluster models for statistical networks
Description: Latent position and cluster models for statistical networks.
These models are fit within the "ergm" framework of the "statnet" package.
Suggests: coda, mclust, shapes, KernSmooth
Packaged: Thu Aug 25 00:26:33 2005; handcock