| 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 |
![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)