Package index • normalblockr

User-facing functions

Main entry points for fitting a Normal-Block model and simulating data

normal_block(): Normal-block model
normal_block_sequential(): Cluster variables in the mean, then in the residual covariance
NB_control(): NB_control
generate_normal_block_var_data(): Generate Normal Block Var Data
generate_normal_block_mean_data(): Generate Normal Block Mean Data

Model and data classes

R6 classes returned by normal_block(). NormalBlockData wraps the responses and design matrix; NormalBlockVarClusters (optionally ZI-prefixed) are single fitted models, known- or unknown-clustering; NormalBlockCollection are collections of models explored over a range of q and/or sparsity penalties.

NormalBlockData: Data Container for Normal-Block Models
NormalBlockVarKnownClusters: Normal-Block Model with Known Clustering
NormalBlockVarUnknownClusters: Normal-Block Model with Unknown Clustering
NormalBlockMeanKnownClusters: Mean-Block Model with Known Clustering
NormalBlockMeanUnknownClusters: Mean-Block Model with Unknown Clustering
ZINormalBlockVarKnownClusters: Zero-Inflated Normal-Block Model with Known Clustering
ZINormalBlockVarUnknownClusters: Zero-Inflated Normal-Block Model with Unknown Clustering
ZINormalBlockMeanKnownClusters: Zero-Inflated Mean-Block Model with Known Clustering
ZINormalBlockMeanUnknownClusters: Zero-Inflated Mean-Block Model with Unknown Clustering
NormalBlockVarCollectionClusters: Collection of Normal-Block Models over a Range of Cluster Counts
NormalBlockVarCollectionSparsity: Collection of Normal-Block Models over a Sparsity Path
NormalBlockVarCollectionClustersSparsity: Collection of Normal-Block Models over Cluster Counts and Sparsity Levels
NormalBlockMeanCollectionClusters: Collection of Mean-Block Models over a Range of Cluster Counts
NormalBlockMeanCollectionSparsity: Collection of Mean-Block Models over a Sparsity Path
NormalBlockMeanCollectionClustersSparsity: Collection of Mean-Block Models over Cluster Counts and Sparsity Levels

Data sets

brca_rppa: Breast cancer proteomics data (TCGA, RPPA)
onema: French stream fish community data (ONEMA / OFB electrofishing surveys)
university: University webpages text data (CMU "4 Universities" / WebKB)

S3 methods

Standard extractors and methods for a fitted model (any NormalBlockBase subclass) and for a collection of models (any NormalBlockCollection subclass)

coef(<NormalBlockBase>): Extract Model Coefficients
fitted(<NormalBlockBase>): Extract Fitted Values
predict(<NormalBlockBase>): Predict Method for Variance-Block Models
sigma(<NormalBlockBase>): Extract the Covariance Matrix
print(<NormalBlockBase>): Print a Normal-Block Model
summary(<NormalBlockBase>): Summarize a Normal-Block Model
print(<summary.NormalBlockBase>): Print a Normal-Block Model Summary
plot(<NormalBlockBase>): Plot a Normal-Block Model
logLik(<NormalBlockBase>): Extract Log-Likelihood of a Normal-Block Model
BIC(<NormalBlockBase>): Bayesian Information Criterion for a Normal-Block Model
print(<NormalBlockCollection>): Print a Collection of Normal-Block Models
summary(<NormalBlockCollection>): Summarize a Collection of Normal-Block Models
print(<summary.NormalBlockCollection>): Print a Collection Summary
logLik(<NormalBlockCollection>): Extract Log-Likelihood of a Collection of Normal-Block Models
BIC(<NormalBlockCollection>): Bayesian Information Criterion for a Collection of Normal-Block Models
print(<normal_block_sequential>): Print a Sequential Mean-then-Variance Fit

Internal

Abstract base classes, low-level helpers and exploratory code kept for reference; not part of the user-facing API

NormalBlockBase: Root Base Class for Normal-Block Models
NormalBlockVarBase: Base Class for Variance-Block Models
NormalBlockMeanBase: Base Class for Mean-Block Models
NormalBlockCollection: Base Class for a Collection of Normal-Block Models
NormalBlockCollectionClusters: Base Class for a Collection of Models over a Range of Cluster Counts
NormalBlockCollectionSparsity: Base Class for a Collection of Models over a Sparsity Path
NormalBlockCollectionClustersSparsity: Base Class for a Collection over Cluster Counts and Sparsity Levels
get_model(): Create a Normal-Block Model Object
isNB(): Check if an Object is a Normal-Block Model