R: Gaussian Mixture Graphical Model Learning and Inference

# Gaussian Mixture Graphical Model Learning and Inference

## Documentation for package ‘gmgm’ version 1.1.3

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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