Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 12]

▼NStochTree
CColumnMatrix	Internal wrapper around Eigen::MatrixXd interface for multidimensional floating point data
CColumnVector	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)
CCutpointGridContainer	Container class for FeatureCutpointGrid objects stored for every feature in a dataset
CFeatureCutpointGrid	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
CFeaturePresortPartition	Data structure that tracks pre-sorted feature values through a tree's split lifecycle
CFeaturePresortRoot	Data structure for presorting a feature by its values
CFeaturePresortRootContainer	Container class for FeaturePresortRoot objects stored for every feature in a dataset
CFeatureUnsortedPartition	Mapping nodes to the indices they contain
CForestContainer	Container of TreeEnsemble forest objects. This is the primary (in-memory) storage interface for multiple "samples" of a decision tree ensemble in stochtree
CForestDataset	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)
CForestTracker	"Superclass" wrapper around tracking data structures for forest sampling algorithms
CGammaSampler
CGaussianConstantLeafModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CGaussianConstantSuffStat	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model
CGaussianMultivariateRegressionLeafModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CGaussianMultivariateRegressionSuffStat	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model
CGaussianUnivariateRegressionLeafModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CGaussianUnivariateRegressionSuffStat	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model
CGlobalHomoskedasticVarianceModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CIGVariancePrior
CInverseGammaSampler
CLabelMapper	Standalone container for the map from category IDs to 0-based indices
CLeafNodeHomoskedasticVarianceModel	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model
CLogLinearVarianceLeafModel	Marginal likelihood and posterior computation for heteroskedastic log-linear variance model
CLogLinearVarianceSuffStat	Sufficient statistic and associated operations for heteroskedastic log-linear variance model
CMultivariateNormalSampler
CMultivariateRegressionRandomEffectsModel	Posterior computation and sampling and state storage for random effects model with a group-level multivariate basis regression
CNodeCutpointTracker	Computing and tracking cutpoints available for a given feature at a given node
CNodeOffsetSize	Tracking cutpoints available at a given node
CRandomEffectsContainer
CRandomEffectsDataset	API for loading and accessing data used to sample (additive) random effects
CRandomEffectsGaussianPrior
CRandomEffectsRegressionGaussianPrior
CRandomEffectsTracker	Wrapper around data structures for random effects sampling algorithms
CSampleCategoryMapper	Class storing sample-node map for each tree in an ensemble TODO: Add run-time checks for categories with a few observations
CSampleNodeMapper	Class storing sample-node map for each tree in an ensemble
CSamplePredMapper	Class storing sample-prediction map for each tree in an ensemble
CSortedNodeSampleTracker	Data structure for tracking observations through a tree partition with each feature pre-sorted
CTree	Decision tree data structure
CTreeEnsemble	Class storing a "forest," or an ensemble of decision trees
CTreePrior
CTreeSplit	Representation of arbitrary tree split rules, including numeric split rules (X[,i] <= c) and categorical split rules (X[,i] in {2,4,6,7})
CUnivariateNormalSampler
CUnsortedNodeSampleTracker	Mapping nodes to the indices they contain