A collection of random number generation utilities. More...

Classes
struct	BARTConfig

struct	BARTData

struct	BARTPredictionMetadata
	Metadata for the BART prediction routine. More...

struct	BARTPredictionResult
	Struct returning BART model predictions. More...

struct	BARTPredTerms
	Selector for model terms that should be predicted. More...

class	BARTSampler

struct	BARTSamples

struct	BCFConfig

struct	BCFData

struct	BCFPredictionMetadata
	Metadata for the BCF prediction routine. More...

struct	BCFPredictionResult
	Struct returning BCF model predictions. More...

struct	BCFPredTerms
	Selector for model terms that should be predicted. More...

class	BCFSampler

struct	BCFSamples

class	CategorySampleTracker
	Mapping categories to the indices they contain TODO: Add run-time checks for categories with a few observations.

class	CloglogOrdinalLeafModel
	Marginal likelihood and posterior computation for complementary log-log ordinal BART model. More...

class	CloglogOrdinalSuffStat
	Sufficient statistic and associated operations for complementary log-log ordinal BART model. More...

class	ColumnMatrix
	Internal wrapper around `Eigen::MatrixXd` interface for multidimensional floating point data. More...

class	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). More...

class	CutpointGridContainer
	Container class for FeatureCutpointGrid objects stored for every feature in a dataset. More...

class	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. More...

class	FeaturePresortPartition
	Data structure that tracks pre-sorted feature values through a tree's split lifecycle. More...

class	FeaturePresortRoot
	Data structure for presorting a feature by its values. More...

class	FeaturePresortRootContainer
	Container class for FeaturePresortRoot objects stored for every feature in a dataset. More...

class	FeatureUnsortedPartition
	Mapping nodes to the indices they contain. More...

class	ForestContainer
	Container of `TreeEnsemble` forest objects. This is the primary (in-memory) storage interface for multiple "samples" of a decision tree ensemble in `stochtree`. More...

class	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). More...

class	ForestTracker
	"Superclass" wrapper around tracking data structures for forest sampling algorithms More...

class	gamma_sampler

class	GammaSampler

class	GaussianConstantLeafModel
	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model. More...

class	GaussianConstantSuffStat
	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model. More...

class	GaussianMultivariateRegressionLeafModel
	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model. More...

class	GaussianMultivariateRegressionSuffStat
	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model. More...

class	GaussianUnivariateRegressionLeafModel
	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model. More...

class	GaussianUnivariateRegressionSuffStat
	Sufficient statistic and associated operations for gaussian homoskedastic constant leaf outcome model. More...

class	GlobalHomoskedasticVarianceModel
	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model. More...

class	IGVariancePrior

class	InverseGammaSampler

class	LabelMapper
	Standalone container for the map from category IDs to 0-based indices. More...

class	LeafNodeHomoskedasticVarianceModel
	Marginal likelihood and posterior computation for gaussian homoskedastic constant leaf outcome model. More...

class	LogLinearVarianceLeafModel
	Marginal likelihood and posterior computation for heteroskedastic log-linear variance model. More...

class	LogLinearVarianceSuffStat
	Sufficient statistic and associated operations for heteroskedastic log-linear variance model. More...

class	MultivariateNormalSampler

class	MultivariateRegressionRandomEffectsModel
	Posterior computation and sampling and state storage for random effects model with a group-level multivariate basis regression. More...

class	NodeCutpointTracker
	Computing and tracking cutpoints available for a given feature at a given node. More...

class	NodeOffsetSize
	Tracking cutpoints available at a given node. More...

class	OrdinalSampler
	Sampler for ordinal model hyperparameters. More...

class	RandomEffectsContainer

class	RandomEffectsDataset
	API for loading and accessing data used to sample (additive) random effects. More...

class	RandomEffectsGaussianPrior

class	RandomEffectsRegressionGaussianPrior

class	RandomEffectsTracker
	Wrapper around data structures for random effects sampling algorithms. More...

class	SampleCategoryMapper
	Class storing sample-node map for each tree in an ensemble TODO: Add run-time checks for categories with a few observations. More...

class	SampleNodeMapper
	Class storing sample-node map for each tree in an ensemble. More...

class	SamplePredMapper
	Class storing sample-prediction map for each tree in an ensemble. More...

class	SortedNodeSampleTracker
	Data structure for tracking observations through a tree partition with each feature pre-sorted. More...

class	standard_normal

class	Tree
	Decision tree data structure. More...

class	TreeEnsemble
	Class storing a "forest," or an ensemble of decision trees. More...

class	TreePrior

class	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}`) More...

class	UnivariateNormalSampler

class	UnsortedNodeSampleTracker
	Mapping nodes to the indices they contain. More...

class	walker_vose

Typedefs
using	SuffStatVariant = std::variant< GaussianConstantSuffStat, GaussianUnivariateRegressionSuffStat, GaussianMultivariateRegressionSuffStat, LogLinearVarianceSuffStat, CloglogOrdinalSuffStat >
	Unifying layer for disparate sufficient statistic class types.

using	LeafModelVariant = std::variant< GaussianConstantLeafModel, GaussianUnivariateRegressionLeafModel, GaussianMultivariateRegressionLeafModel, LogLinearVarianceLeafModel, CloglogOrdinalLeafModel >
	Unifying layer for disparate leaf model class types.

Enumerations
enum	ModelType
	Leaf models for the forest sampler: More...

enum class	PredType
	Determines whether posterior predictions are returned as-is or pre-aggregated.

enum class	PredScale
	Determines the scale of predictions (i.e. whether a probability / class transformation is applied) More...

enum	TreeNodeType
	Tree node type.

Functions
void	AppendForestContainerSamples (std::unique_ptr< ForestContainer > &dst, const std::unique_ptr< ForestContainer > &src, const char *name)
	Append every retained forest sample from `src` onto the end of `dst` (deep copy). Used by BARTSamples::Merge / BCFSamples::Merge to combine independently-sampled chains. The forest must be present in both samples objects or neither; `name` labels the forest in the mismatch error.

static void	ExtractMultipleFeaturesFromMemory (std::vector< std::string > text_data, const Parser parser, std::vector< int > &column_indices, Eigen::MatrixXd &data, data_size_t num_rows)
	Extract multiple features from the raw data loaded from a file into an `Eigen::MatrixXd`. Lightly modified from LightGBM's datasetloader interface to support `stochtree`'s use cases.

static void	ExtractSingleFeatureFromMemory (std::vector< std::string > text_data, const Parser parser, int column_index, Eigen::VectorXd &data, data_size_t num_rows)
	Extract a single feature from the raw data loaded from a file into an `Eigen::VectorXd`. Lightly modified from LightGBM's datasetloader interface to support `stochtree`'s use cases.

template<typename container_type , typename prob_type >
void	sample_without_replacement (container_type output, prob_type p, container_type *a, int population_size, int sample_size, std::mt19937 &gen)
	Sample without replacement according to a set of probability weights. This template function is a C++ variant of numpy's implementation: https://github.com/numpy/numpy/blob/031f44252d613f4524ad181e3eb2ae2791e22187/numpy/random/_generator.pyx#L925.

double	norm_cdf (double x)

double	norm_inv_cdf (double p)

double	standard_uniform_draw_53bit (std::mt19937 &gen)

double	standard_uniform_draw_32bit (std::mt19937 &gen)

double	sample_standard_normal (double mean, double sd, std::mt19937 &gen)

double	sample_gamma (std::mt19937 &gen, double shape, double scale)

double	sample_std_truncnorm_upper (std::mt19937 &gen)

double	sample_std_truncnorm_lower (std::mt19937 &gen)

static SuffStatVariant	suffStatFactory (ModelType model_type, int basis_dim=0)
	Factory function that creates a new `SuffStat` object for the specified model type.

static LeafModelVariant	leafModelFactory (ModelType model_type, double tau, Eigen::MatrixXd &Sigma0, double a, double b)
	Factory function that creates a new `LeafModel` object for the specified model type.

static double	sample_univariate_gaussian_regression_coefficient (double y, double x, double error_variance, double prior_variance, int n, std::mt19937 &gen)
	Sample a regression coefficient from the posterior distribution of a univariate Gaussian regression model with known error variance and known prior variance.

static void	sample_general_bivariate_gaussian_regression_coefficients (double output, double y, double x1, double x2, double error_variance, double prior_variance_11, double prior_variance_12, double prior_variance_22, int n, std::mt19937 &gen)
	Sample regression coefficients from the posterior distribution of a bivariate Gaussian regression model with known error variance and known prior variance.

static void	sample_diagonal_bivariate_gaussian_regression_coefficients (double output, double y, double x1, double x2, double error_variance, double prior_variance_11, double prior_variance_22, int n, std::mt19937 &gen)
	Sample regression coefficients from the posterior distribution of a bivariate Gaussian regression model with known error variance and known diagonal prior variance.

static Eigen::VectorXd	sample_general_gaussian_regression_coefficients (const Eigen::Ref< const Eigen::VectorXd > &y, const Eigen::Ref< const Eigen::MatrixXd > &X, double error_variance, const Eigen::Ref< const Eigen::MatrixXd > &prior_variance, int n, std::mt19937 &gen)
	Sample regression coefficients from the posterior distribution of a bivariate Gaussian regression model with known error variance and known diagonal prior variance.

BARTPredictionResult	predict_bart_model (BARTData &data, BARTSamples &samples, BARTPredictionMetadata &metadata)
	BART prediction function.

BCFPredictionResult	predict_bcf_model (BCFData &data, BCFSamples &samples, BCFPredictionMetadata &metadata)
	BCF prediction function.

void	AppendRandomEffectsContainerSamples (std::unique_ptr< RandomEffectsContainer > &dst, const std::unique_ptr< RandomEffectsContainer > &src)
	Append every retained random effects sample from `src` onto the end of `dst` (deep copy). Used by BARTSamples::Merge / BCFSamples::Merge to combine independently-sampled chains. The random effects must be present in both samples objects or neither.

std::string	TreeNodeTypeToString (TreeNodeType type)
	Get string representation of TreeNodeType.

TreeNodeType	TreeNodeTypeFromString (std::string const &name)
	Get NodeType from string.

bool	operator== (const Tree &lhs, const Tree &rhs)
	Comparison operator for trees.

bool	SplitTrueNumeric (double fvalue, double threshold)
	Determine whether an observation produces a "true" value in a numeric split node.

bool	SplitTrueCategorical (double fvalue, std::vector< std::uint32_t > const &category_list)
	Determine whether an observation produces a "true" value in a categorical split node.

int	NextNodeNumeric (double fvalue, double threshold, int left_child, int right_child)
	Return left or right node id based on a numeric split.

int	NextNodeCategorical (double fvalue, std::vector< std::uint32_t > const &category_list, int left_child, int right_child)
	Return left or right node id based on a categorical split.

int	EvaluateTree (Tree const &tree, Eigen::MatrixXd &data, int row)

int	EvaluateTree (Tree const &tree, Eigen::Map< Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor > > &data, int row)

bool	RowSplitLeft (Eigen::MatrixXd &covariates, int row, int split_index, double split_value)
	Determine whether a given observation is "true" at a split proposed by split_index and split_value.

bool	RowSplitLeft (Eigen::MatrixXd &covariates, int row, int split_index, std::vector< std::uint32_t > const &category_list)
	Determine whether a given observation is "true" at a split proposed by split_index and split_value.

static void	VarSplitRange (ForestTracker &tracker, ForestDataset &dataset, int tree_num, int leaf_split, int feature_split, double &var_min, double &var_max)
	Computer the range of available split values for a continuous variable, given the current structure of a tree.

static bool	NodesNonConstantAfterSplit (ForestDataset &dataset, ForestTracker &tracker, TreeSplit &split, int tree_num, int leaf_split, int feature_split)
	Determines whether a proposed split creates two leaf nodes with constant values for every feature (thus ensuring that the tree cannot split further).

template<typename LeafModel , typename LeafSuffStat , typename... LeafSuffStatConstructorArgs>
static void	GFRSampleOneIter (TreeEnsemble &active_forest, ForestTracker &tracker, ForestContainer &forests, LeafModel &leaf_model, ForestDataset &dataset, ColumnVector &residual, TreePrior &tree_prior, std::mt19937 &gen, std::vector< double > &variable_weights, std::vector< int > &sweep_update_indices, double global_variance, std::vector< FeatureType > &feature_types, int cutpoint_grid_size, bool keep_forest, bool pre_initialized, bool backfitting, int num_features_subsample, int num_threads, LeafSuffStatConstructorArgs &... leaf_suff_stat_args)

template<typename LeafModel , typename LeafSuffStat , typename... LeafSuffStatConstructorArgs>
static void	MCMCSampleOneIter (TreeEnsemble &active_forest, ForestTracker &tracker, ForestContainer &forests, LeafModel &leaf_model, ForestDataset &dataset, ColumnVector &residual, TreePrior &tree_prior, std::mt19937 &gen, std::vector< double > &variable_weights, std::vector< int > &sweep_update_indices, double global_variance, bool keep_forest, bool pre_initialized, bool backfitting, int num_threads, LeafSuffStatConstructorArgs &... leaf_suff_stat_args)
	Runs one iteration of the MCMC sampler for a tree ensemble model, which consists of two steps for every tree in a forest:

Variables
static constexpr double	Phi_0 = 0.5

Detailed Description

A collection of random number generation utilities.

General-purpose data structures used for keeping track of categories in a training dataset.

SampleCategoryMapper is a simplified version of SampleNodeMapper, which is not tree-specific as it tracks categories loaded into a training dataset, and we do not expect to modify it during training.

SampleCategoryMapper is used in two places:

Group random effects: mapping observations to group IDs for the purpose of computing random effects
Heteroskedasticity based on fixed categories (as opposed to partitions as in HBART by Pratola et al 2018)
- One example of this would be binary treatment causal inference with separate outcome variances for the treated and control groups (as in Krantsevich et al 2023)

CategorySampleTracker is a simplified version of FeatureUnsortedPartition, which as above does not vary based on tree / partition and is not expected to change during training.

SampleNodeMapper is inspired by the design of the DataPartition class in LightGBM, released under the MIT license with the following copyright:

Simple container-like interfaces for samples of common models.

Data structures for enumerating potential cutpoint candidates.

This is used in the XBART family of algorithms, which samples split rules based on the log marginal likelihood of every potential cutpoint. For numeric variables with large sample sizes, it is often unnecessary to consider every unique value, so we allow for an adaptive "grid" of potential cutpoint values.

Algorithms for enumerating cutpoints take Dataset and SortedNodeSampleTracker objects as inputs, so that each feature is "pre-sorted" according to its value within a given node. The size of the adaptive cutpoint grid is set by the cutpoint_grid_size configuration parameter.

Numeric Features

When a node has fewer available observations than cutpoint_grid_size, full enumeration of unique available cutpoints is done via the EnumerateNumericCutpointsDeduplication function

When a node has more available observations than cutpoint_grid_size, potential cutpoints are "thinned out" by considering every k-th observation, where k is implied by the number of observations and the target cutpoint_grid_size.

Ordered Categorical Features

In this case, the grid is every unique value of the ordered categorical feature in ascending order.

Unordered Categorical Features

In this case, the grid is every unique value of the unordered categorical feature, arranged in an outcome-dependent order, as described in Fisher (1958)

This file is vendored from a broader C++ / R distribution library, where the distributions are subject to rigorous testing. https://github.com/andrewherren/cpp11_r_rng

Data structures used for tracking dataset through the tree building process.

The first category of data structure tracks observations available in nodes of a tree. a. UnsortedNodeSampleTracker tracks the observations available in every leaf of every tree in an ensemble, in no feature-specific sort order. It is primarily designed for use in BART-based algorithms. b. SortedNodeSampleTracker tracks the observations available in a every leaf of a tree, pre-sorted separately for each feature. It is primarily designed for use in XBART-based algorithms.

The second category, SampleNodeMapper, maps observations from a dataset to leaf nodes.

SampleNodeMapper is inspired by the design of the DataPartition class in LightGBM, released under the MIT license with the following copyright:

SortedNodeSampleTracker is inspired by the "approximate" split finding method in xgboost, released under the Apache license with the following copyright:

Enumeration Type Documentation

◆ PredScale

enum class StochTree::PredScale

strong

Determines the scale of predictions (i.e. whether a probability / class transformation is applied)

Options are

linear (i.e. raw) scale,
probability scale,
class predictions.

Only valid for binary / ordinal outcome models.

Function Documentation

◆ norm_cdf()

double StochTree::norm_cdf ( double x )

inline

Standard normal cumulative distribution function, implemented via the complementary error function.

◆ norm_inv_cdf()

double StochTree::norm_inv_cdf ( double p )

inline

Standard normal quantile function (inverse CDF), implemented via the inverse complementary error function.

◆ standard_uniform_draw_53bit()

double StochTree::standard_uniform_draw_53bit ( std::mt19937 & gen )

inline

Generate a standard uniform random variate to 53 bits of precision via two mersenne twisters, see: https://github.com/numpy/numpy/blob/0d7986494b39ace565afda3de68be528ddade602/numpy/random/src/mt19937/mt19937.h#L56

◆ standard_uniform_draw_32bit()

double StochTree::standard_uniform_draw_32bit ( std::mt19937 & gen )

inline

Generate a standard uniform random variate to 32 bits of precision via a single mersenne twister.

◆ sample_standard_normal()

double StochTree::sample_standard_normal	(	double	mean,
		double	sd,
		std::mt19937 &	gen
	)

inline

Stateless standard normal sampler implementing Marsaglia's polar method. Without caching, this is half as fast as other methods for repeated normal sampling, but this might be acceptable in cases where a relatively small number of normal draws is desired.

Reference: https://en.wikipedia.org/wiki/Marsaglia_polar_method

◆ sample_gamma()

double StochTree::sample_gamma	(	std::mt19937 &	gen,
		double	shape,
		double	scale
	)

inline

Generate a single sample from a gamma distribution using a combination of algorithms When shape < 1.0, use the Ahrens-Dieter method When shape > 1.0, use the Marsaglia-Tsang method When shape == 1.0, sample an exponential via inverse transform method When shape == 0.0, return 0.0 https://en.wikipedia.org/wiki/Gamma_distribution#Random_variate_generation

◆ sample_std_truncnorm_upper()

double StochTree::sample_std_truncnorm_upper ( std::mt19937 & gen )

inline

Generate a single sample from a truncated standard normal distribution, bounded above by 0.

◆ sample_std_truncnorm_lower()

double StochTree::sample_std_truncnorm_lower ( std::mt19937 & gen )

inline

Generate a single sample from a truncated standard normal distribution, bounded below by 0.

◆ sample_univariate_gaussian_regression_coefficient()

static double StochTree::sample_univariate_gaussian_regression_coefficient	(	double *	y,
		double *	x,
		double	error_variance,
		double	prior_variance,
		int	n,
		std::mt19937 &	gen
	)

static

Sample a regression coefficient from the posterior distribution of a univariate Gaussian regression model with known error variance and known prior variance.

Parameters

y	Pointer to outcome array of length n
x	Pointer to covariate array of length n
error_variance	Known error variance (sigma^2)
prior_variance	Known prior variance (tau^2)
n	Number of observations
gen	Random number generator

Returns: double

◆ sample_general_bivariate_gaussian_regression_coefficients()

static void StochTree::sample_general_bivariate_gaussian_regression_coefficients	(	double *	output,
		double *	y,
		double *	x1,
		double *	x2,
		double	error_variance,
		double	prior_variance_11,
		double	prior_variance_12,
		double	prior_variance_22,
		int	n,
		std::mt19937 &	gen
	)

static

Sample regression coefficients from the posterior distribution of a bivariate Gaussian regression model with known error variance and known prior variance.

Parameters

output	Pointer to sampled coefficient array of length 2, where the sampled coefficients will be stored
y	Pointer to outcome array of length n
x1	Pointer to first covariate array of length n
x2	Pointer to second covariate array of length n
error_variance	Known error variance (sigma^2)
prior_variance_11	First diagonal element of the known prior covariance matrix (tau^2 for the first coefficient)
prior_variance_12	Off-diagonal element of the known prior covariance matrix (covariance between the two coefficients) – note that this is the same as prior_variance_21 so only one off-diagonal element is needed
prior_variance_22	Second diagonal element of the known prior covariance matrix (tau^2 for the second coefficient)
n	Number of observations
gen	Random number generator

◆ sample_diagonal_bivariate_gaussian_regression_coefficients()

static void StochTree::sample_diagonal_bivariate_gaussian_regression_coefficients	(	double *	output,
		double *	y,
		double *	x1,
		double *	x2,
		double	error_variance,
		double	prior_variance_11,
		double	prior_variance_22,
		int	n,
		std::mt19937 &	gen
	)

static

Sample regression coefficients from the posterior distribution of a bivariate Gaussian regression model with known error variance and known diagonal prior variance.

Parameters

output	Pointer to sampled coefficient array of length 2, where the sampled coefficients will be stored
y	Pointer to outcome array of length n
x1	Pointer to first covariate array of length n
x2	Pointer to second covariate array of length n
error_variance	Known error variance (sigma^2)
prior_variance_11	First diagonal element of the known prior covariance matrix (tau^2 for the first coefficient)
prior_variance_22	Second diagonal element of the known prior covariance matrix (tau^2 for the second coefficient)
n	Number of observations
gen	Random number generator

◆ sample_general_gaussian_regression_coefficients()

static Eigen::VectorXd StochTree::sample_general_gaussian_regression_coefficients	(	const Eigen::Ref< const Eigen::VectorXd > &	y,
		const Eigen::Ref< const Eigen::MatrixXd > &	X,
		double	error_variance,
		const Eigen::Ref< const Eigen::MatrixXd > &	prior_variance,
		int	n,
		std::mt19937 &	gen
	)

static

Sample regression coefficients from the posterior distribution of a bivariate Gaussian regression model with known error variance and known diagonal prior variance.

Parameters

y	Eigen::VectorXd of outcomes of length n
X	Eigen::MatrixXd of covariates with n rows and p columns
error_variance	Known error variance (sigma^2)
prior_variance	Eigen::MatrixXd of known prior covariance matrix (tau^2 for the coefficients) of dimension p x p
n	Number of observations
gen	Random number generator

◆ predict_bart_model()

BARTPredictionResult StochTree::predict_bart_model	(	BARTData &	data,
		BARTSamples &	samples,
		BARTPredictionMetadata &	metadata
	)

BART prediction function.

Accepts BARTData, BARTSamples, and a struct of metadata, which dictates the model terms for which predictions are computed / returned and any transformations done before returning (i.e. pre-aggregation, probit function transformation).

Parameters

data	Struct wrapping pointers to prediction data from R / Python
samples	Object storing BART samples
metadata	Struct containing prediction metadata

Returns: BARTPredictionResult struct containing prediction vectors

◆ predict_bcf_model()

BCFPredictionResult StochTree::predict_bcf_model	(	BCFData &	data,
		BCFSamples &	samples,
		BCFPredictionMetadata &	metadata
	)

BCF prediction function.

Accepts BCFData, BCFSamples, and a struct of metadata, which dictates the model terms for which predictions are computed / returned and any transformations done before returning (i.e. pre-aggregation, probit function transformation).

Parameters

data	Struct wrapping pointers to prediction data from R / Python
samples	Object storing BCF samples
metadata	Struct containing prediction metadata

Returns: BCFPredictionResult struct containing prediction vectors

Variable Documentation

◆ Phi_0

constexpr double StochTree::Phi_0 = 0.5

staticconstexpr

Precomputed standard normal CDF at 0

Classes

Typedefs

Enumerations

Functions

Variables

Detailed Description

Numeric Features

Ordered Categorical Features

Unordered Categorical Features

Enumeration Type Documentation

◆ PredScale

Function Documentation

◆ norm_cdf()

◆ norm_inv_cdf()

◆ standard_uniform_draw_53bit()

◆ standard_uniform_draw_32bit()

◆ sample_standard_normal()

◆ sample_gamma()

◆ sample_std_truncnorm_upper()

◆ sample_std_truncnorm_lower()

◆ sample_univariate_gaussian_regression_coefficient()

◆ sample_general_bivariate_gaussian_regression_coefficients()

◆ sample_diagonal_bivariate_gaussian_regression_coefficients()

◆ sample_general_gaussian_regression_coefficients()

◆ predict_bart_model()

◆ predict_bcf_model()

Variable Documentation

◆ Phi_0