Package: gmgm 1.1.2

gmgm: Gaussian Mixture Graphical Model Learning and Inference

Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

Authors:Jérémy Roos [aut, cre, cph], RATP Group [fnd, cph]

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gmgm.pdf |gmgm.html✨
gmgm/json (API)
NEWS

# Install 'gmgm' in R:

install.packages('gmgm', repos = c('https://jeremyroos.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jeremyroos/gmgm/issues

Datasets:

data_air - Beijing air quality dataset
data_body - NHANES body composition dataset
gmbn_body - Gaussian mixture Bayesian network learned from the NHANES body composition dataset
gmdbn_air - Gaussian mixture dynamic Bayesian network learned from the Beijing air quality dataset
gmm_body - Gaussian mixture model learned from the NHANES body composition dataset

On CRAN:

bayesian-networks gaussian-mixture-models inference machine-learning probabilistic-graphical-models

3.40 score 5 stars 7 scripts 248 downloads 36 exports 59 dependencies

Last updated 2 years agofrom:c9ce0c8d35. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 08 2024
R-4.5-win	OK	Nov 08 2024
R-4.5-linux	OK	Nov 08 2024
R-4.4-win	OK	Nov 08 2024
R-4.4-mac	OK	Nov 08 2024
R-4.3-win	OK	Nov 08 2024
R-4.3-mac	OK	Nov 08 2024

Exports:add_arcs add_nodes add_var aggregation conditional density ellipses em expectation filtering gmbn gmdbn gmm inference merge_comp network param_em param_learn particles prediction propagation relevant remove_arcs remove_nodes remove_var rename_nodes rename_var reorder sampling smem smoothing split_comp stepwise struct_em struct_learn structure

Dependencies:base64enc bslib cachem cli colorspace cpp11 digest dplyr evaluate fansi farver fastmap fontawesome fs generics ggplot2 glue gtable highr htmltools htmlwidgets isoband jquerylib jsonlite knitr labeling lattice lifecycle magrittr MASS Matrix memoise mgcv mime munsell nlme pillar pkgconfig purrr R6 rappdirs RColorBrewer rlang rmarkdown sass scales stringi stringr tibble tidyr tidyselect tinytex utf8 vctrs viridisLite visNetwork withr xfun yaml

Help page	Topics
Gaussian mixture graphical model learning and inference	gmgm-package
Add arcs to a Gaussian mixture graphical model	add_arcs
Add nodes to a Gaussian mixture graphical model	add_nodes
Add variables to a Gaussian mixture model	add_var
Aggregate particles to obtain inferred values	aggregation
Compute the Akaike Information Criterion (AIC) of a Gaussian mixture model or graphical model	AIC AIC.gmbn AIC.gmdbn AIC.gmm
Compute the Bayesian Information Criterion (BIC) of a Gaussian mixture model or graphical model	BIC BIC.gmbn BIC.gmdbn BIC.gmm
Conditionalize a Gaussian mixture model	conditional
Beijing air quality dataset	data_air
NHANES body composition dataset	data_body
Compute densities of a Gaussian mixture model	density
Display the mixture components of a Gaussian mixture model	ellipses
Estimate the parameters of a Gaussian mixture model	em
Compute expectations of a Gaussian mixture model	expectation
Perform filtering inference in a Gaussian mixture dynamic Bayesian network	filtering
Create a Gaussian mixture Bayesian network	gmbn
Gaussian mixture Bayesian network learned from the NHANES body composition dataset	gmbn_body
Create a Gaussian mixture dynamic Bayesian network	gmdbn
Gaussian mixture dynamic Bayesian network learned from the Beijing air quality dataset	gmdbn_air
Create a Gaussian mixture model	gmm
Gaussian mixture model learned from the NHANES body composition dataset	gmm_body
Perform inference in a Gaussian mixture Bayesian network	inference
Compute the log-likelihood of a Gaussian mixture model or graphical model	logLik logLik.gmbn logLik.gmdbn logLik.gmm
Merge mixture components of a Gaussian mixture model	merge_comp
Display the graphical structure of a Gaussian mixture Bayesian network	network
Learn the parameters of a Gaussian mixture graphical model with incomplete data	param_em
Learn the parameters of a Gaussian mixture graphical model	param_learn
Initialize particles to perform inference in a Gaussian mixture graphical model	particles
Perform predictive inference in a Gaussian mixture dynamic Bayesian network	prediction
Propagate particles forward in time	propagation
Extract the minimal sub-Gaussian mixture graphical model required to infer a subset of nodes	relevant
Remove arcs from a Gaussian mixture graphical model	remove_arcs
Remove nodes from a Gaussian mixture graphical model	remove_nodes
Remove variables from a Gaussian mixture model	remove_var
Rename nodes of a Gaussian mixture graphical model	rename_nodes
Rename variables of a Gaussian mixture model	rename_var
Reorder the variables and the mixture components of a Gaussian mixture model	reorder
Sample a Gaussian mixture model	sampling
Select the number of mixture components and estimate the parameters of a Gaussian mixture model	smem
Perform smoothing inference in a Gaussian mixture dynamic Bayesian network	smoothing
Split a mixture component of a Gaussian mixture model	split_comp
Select the explanatory variables, the number of mixture components and estimate the parameters of a conditional Gaussian mixture model	stepwise
Learn the structure and the parameters of a Gaussian mixture graphical model with incomplete data	struct_em
Learn the structure and the parameters of a Gaussian mixture graphical model	struct_learn
Provide the graphical structure of a Gaussian mixture graphical model	structure
Summarize a Gaussian mixture model or graphical model	summary summary.gmbn summary.gmdbn summary.gmm

Package: gmgm 1.1.2

gmgm: Gaussian Mixture Graphical Model Learning and Inference

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