stochtree 0.3.1

New Features

Replaced C++ standard library distributions (discrete_distribution, uniform_real_distribution, normal_distribution, and gamma_distribution) with custom implementations for cross-platform reproducibility.
Substituted custom implementations for base R mean(), var(), and sd() in the preprocessing logic of the R bart() and bcf() functions for enhanced numeric stability across platforms.

Added print, summary, plot, and extract_parameter generic functions in R for the bartmodel and bcfmodel classes (#271)

Fix R bug where our approach to temporarily modifying users’ RNG state failed if .Random.seed did not exist (i.e. if the R RNG hadn’t yet been accessed by an R session) (#258)
Fix prediction bug for R BART models with random effects with labels that aren’t straightforward 1:num_groups integers when only y_hat is requested (#256)
Fix issue with C++ standard specification in Windows R package config (#276)

Fix prediction bug for univariate random effects models in R (#248)
Fix prediction bug for Python BART and BCF models with random effects with labels that aren’t straightforward 0:(num_groups-1) integers (#256)

Encode expectations about which combinations of BART / BCF features work together and ensure warning (#250)

Support for multithreading in various elements of the GFR and MCMC algorithms (#182)
Support for binary outcomes in BART and BCF with a probit link (#164)
Enable “restricted sweep” of tree algorithms over a handful of trees (#173)
Support for multivariate treatment in R (#183)
Enable modification of dataset variables (weights, etc…) via low-level interface (#194)

Fixed indexing bug in cleanup of grow-from-root (GFR) samples in BART and BCF models
Avoid using covariate preprocessor in computeForestLeafIndices function when a ForestSamples object is provided (rather than a bartmodel or bcfmodel object)
Correctly compute feature-specific split counts in R and Python (#220)
Avoid override of user-specified num_burnin parameter in BCF models with an internal propensity score (#222)
Outcome predictions correctly incorporate adaptive coding of untreated observations in BCF with binary treatment (#231)

Clarify structure / layout of samples when users request multiple chains in BART and BCF models (#220)

Standardized naming conventions for data elements of BART and BCF models across R and Python interfaces
- Covariates / features are always referred to as “X”
- Treatment is always referred to as “Z”
- Propensity scores are referred to as “propensity” (rather than “pi”)
- Outcomes are referred to as “y”
- Basis vectors for leaf-wise regression models in forest terms are referred to as “leaf_basis”
- Group labels for additive random effects models are referred to as “rfx_group_ids”
- Basis vectors for additive random effects models are referred to as “rfx_basis”
Run-time checks for variables that are treated as continuous but have many “ties” (which presents issues with the current GFR algorithm) when only GFR samples are requested (#243)

Fixed initialization bug in several R package code examples for random effects models

Initial release on CRAN.
Support for sampling stochastic tree ensembles using two algorithms: MCMC and Grow-From-Root (GFR)
High-level model types supported:
- Supervised learning with constant leaves or user-specified leaf regression models
- Causal effect estimation with binary or continuous treatments
Additional high-level modeling features:
- Forest-based variance function estimation (heteroskedasticity)
- Additive (univariate or multivariate) group random effects
- Multi-chain sampling and support for parallelism
- “Warm-start” initialization of MCMC forest samplers via the Grow-From-Root (GFR) algorithm
- Automated preprocessing / handling of categorical variables
Low-level interface:
- Ability to combine a forest sampler with other (additive) model terms, without using C++
- Combine and sample an arbitrary number of forests or random effects terms