Class Hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 12]

CStochTree::ColumnMatrix	Internal wrapper around Eigen::MatrixXd interface for multidimensional floating point data
CStochTree::ColumnVector	Internal wrapper around Eigen::VectorXd interface for univariate floating point data. The (frequently updated) full / partial residual used in sampling forests is stored internally as a ColumnVector by the sampling functions (see Forest Sampler API)
CStochTree::CutpointGridContainer	Container class for FeatureCutpointGrid objects stored for every feature in a dataset
CStochTree::FeatureCutpointGrid	Computing and tracking cutpoints available for a given feature at a given node Store cutpoint bins in 0-indexed fashion, so that if a given node has
CStochTree::FeaturePresortPartition	Data structure that tracks pre-sorted feature values through a tree's split lifecycle
CStochTree::FeaturePresortRoot	Data structure for presorting a feature by its values
CStochTree::FeaturePresortRootContainer	Container class for FeaturePresortRoot objects stored for every feature in a dataset
CStochTree::FeatureUnsortedPartition	Mapping nodes to the indices they contain
CStochTree::ForestContainer	Container of TreeEnsemble forest objects. This is the primary (in-memory) storage interface for multiple "samples" of a decision tree ensemble in stochtree
CStochTree::ForestDataset	API for loading and accessing data used to sample tree ensembles The covariates / bases / weights used in sampling forests are stored internally as a ForestDataset by the sampling functions (see Forest Sampler API)
CStochTree::ForestTracker	"Superclass" wrapper around tracking data structures for forest sampling algorithms
CStochTree::GammaSampler
CStochTree::GaussianConstantLeafModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CStochTree::GaussianConstantSuffStat	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model
CStochTree::GaussianMultivariateRegressionLeafModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CStochTree::GaussianMultivariateRegressionSuffStat	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model
CStochTree::GaussianUnivariateRegressionLeafModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CStochTree::GaussianUnivariateRegressionSuffStat	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model
CStochTree::GlobalHomoskedasticVarianceModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CStochTree::IGVariancePrior
CStochTree::InverseGammaSampler
CStochTree::LabelMapper	Standalone container for the map from category IDs to 0-based indices
CStochTree::LeafNodeHomoskedasticVarianceModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CStochTree::LogLinearVarianceLeafModel	Marginal likelihood and posterior computation for heteroskedastic log-linear variance model
CStochTree::LogLinearVarianceSuffStat	Sufficient statistic and associated operations for heteroskedastic log-linear variance model
CStochTree::MultivariateNormalSampler
CStochTree::MultivariateRegressionRandomEffectsModel	Posterior computation and sampling and state storage for random effects model with a group-level multivariate basis regression
CStochTree::NodeCutpointTracker	Computing and tracking cutpoints available for a given feature at a given node
CStochTree::NodeOffsetSize	Tracking cutpoints available at a given node
CStochTree::RandomEffectsContainer
CStochTree::RandomEffectsDataset	API for loading and accessing data used to sample (additive) random effects
▼CStochTree::RandomEffectsGaussianPrior
CStochTree::RandomEffectsRegressionGaussianPrior
CStochTree::RandomEffectsTracker	Wrapper around data structures for random effects sampling algorithms
CStochTree::SampleCategoryMapper	Class storing sample-node map for each tree in an ensemble TODO: Add run-time checks for categories with a few observations
CStochTree::SampleNodeMapper	Class storing sample-node map for each tree in an ensemble
CStochTree::SamplePredMapper	Class storing sample-prediction map for each tree in an ensemble
CStochTree::SortedNodeSampleTracker	Data structure for tracking observations through a tree partition with each feature pre-sorted
CStochTree::Tree	Decision tree data structure
CStochTree::TreeEnsemble	Class storing a "forest," or an ensemble of decision trees
CStochTree::TreePrior
CStochTree::TreeSplit	Representation of arbitrary tree split rules, including numeric split rules (X[,i] <= c) and categorical split rules (X[,i] in {2,4,6,7})
CStochTree::UnivariateNormalSampler
CStochTree::UnsortedNodeSampleTracker	Mapping nodes to the indices they contain