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heteromixgm  

Copula Graphical Models for Heterogeneous Mixed Data
View on CRAN: Click here


Download and install heteromixgm package within the R console
Install from CRAN:
install.packages("heteromixgm")

Install from Github:
library("remotes")
install_github("cran/heteromixgm")

Install by package version:
library("remotes")
install_version("heteromixgm", "2.0.2")



Attach the package and use:
library("heteromixgm")
Maintained by
Sjoerd Hermes
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-02-27
Latest Update: 2023-06-29
Description:
A multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the 'heteromixgm' package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2022) .
How to cite:
Sjoerd Hermes (2023). heteromixgm: Copula Graphical Models for Heterogeneous Mixed Data. R package version 2.0.2, https://cran.r-project.org/web/packages/heteromixgm. Accessed 03 Dec. 2024.
Previous versions and publish date:
0.1.0 (2023-02-27 09:22), 1.0.0 (2023-06-29 19:40), 2.0.0 (2024-07-30 18:40), 2.0.1 (2024-08-01 13:00)
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Complete documentation for heteromixgm
Functions, R codes and Examples using the heteromixgm R package
Some associated functions: data_sim . heteromixgm . initialize . lower.upper . maize . modselect . 
Some associated R codes: Approx_method.R . FGL.R . Gibbs_method.R . data_sim.R . globals.R . heteromixgm.R . modselect.R .  Full heteromixgm package functions and examples
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