NEWS

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stochtree 0.4.6.9000

Bug Fixes

• Fix the covariate range used to propose MCMC split points, which was computed incorrectly when all of a node's values for a feature were negative or when the node's first observation held its largest value, causing grow proposals to be rejected or drawn from the wrong range #425
• Fix the move-type and node-selection probabilities in the MCMC grow / prune Metropolis-Hastings acceptance ratios, so that the tree sampler targets the correct posterior over trees #426
• Fix ordering of missing (NaN) covariate values when presorting features for the grow-from-root (GFR) sampler, which now sorts NaNs last, consistent with how numeric splits route them #427
• Fix the conditional posterior of the random effects variance components, which was missing factors of one half in its inverse gamma shape and scale #428
• Fix an uninitialized starting value when initializing cloglog forests in R, which could crash or corrupt the outcome data for binary and ordinal models with a cloglog link #420

Fixes in #425, #426, and #428 change sampler output, as does #427 for covariates with missing values, so results for a given random seed will differ from previous versions.

Documentation and Other Maintenance

• Remove unused variables flagged by compiler warnings #417
• Mark Log::Fatal as [[noreturn]], which silences "may be used uninitialized" compiler warnings #418
• Explicitly initialize variables in the R forest initialization and sampling code #421
• Use shallow clones and canonical .git URLs for git submodules #422
• Updated Eigen version to 5.0.1, which also fixes builds on PowerPC without VSX support #424
• Add absolute tolerance to several python floating point comparison tests #430.
• Add exact-posterior tests of the MCMC tree sampler in R and Python, which check that a long chain on a small problem reproduces the analytically enumerated posterior over trees #431

stochtree 0.4.5 (2026-06-30)

Bug Fixes

• Fix IndexError when sampling a mean-only Python BART model with categorical covariates; the excluded-variable weight zero-out is now guarded by the forest-inclusion flags, matching the R implementation #411
• Fix ordinal class prediction bug in R BART #399

stochtree 0.4.4 (2026-06-08)

Documentation and Other Maintenance

• Switch sampling default to single-threaded execution (num_threads = 1) in both R and Python #396. This prevents CRAN pre-checks from failing due to limitations on core usage and also avoid unintentional default interaction with users' own multi-core setups.

stochtree 0.4.3

Bug Fixes

• Fix serialization / deserialization roundtrip bugs in BART and BCF #393
• Safely handle / cast float32 inputs in python BART / BCF #389
• Fix ordinal cloglog probability transform for K>=4 categories #382
• Fixed bug that separated homogeneous parametric treatment effect term (tau_0) from treatment effect forest predictions (tau(x)), only including the latter in tau for BCF predictions when a parametric term is included in the model #377
• Fixed BCF prediction bug for prognostic function and mu(X) when adaptive coding is used and the tau(x) forest is not also requested by a prediction call #377
• Fixed R BCF prediction bug when covariates are passed as dataframes and an internal propensity is sampled #374

stochtree 0.4.2 (2026-04-28)

New Features

• Added support for parametric treatment effect term in BCF #309
• Added support for observation-level weights passed as data arguments to BART and BCF #333

Bug Fixes

• Include boost headers in sdist for Python package #362
• Fixed prediction bug for cloglog BART in R #361
• Fixed assignment bug in RFX category tracker data structure #360
• Fixed prediction bugs in binary classification sklearn wrapper #359
• Fixed several initialization, sampling and prediction bugs for cloglog #357
• Fixed num_threads pass-through bug in R BART #339
• Fixed probit offset bug in R and Python #337
• Standardized multi-chain BCF handling of internal propensity models #334
• Fixed multi-chain BCF bugs with the parametric intercept term in R and Python #326
• Fixed indexing bugs for multivariate treatment BCF in Python #326
• Convert binary factor-valued treatments to 0/1 binary numeric treatment in bcf() R function #332

Documentation and Other Maintenance

• Add support for python 3.14 and drop 3.8 and 3.9 #363

stochtree 0.4.1

Documentation and Other Maintenance

• Converted all R man page titles to title case #310
• Updated Python summary() methods to print directly to console rather than return a summary string #310
• Updated vendored pybind11 dependency to version 3.0.2 #310

stochtree 0.4.0 (2026-03-06)

New Features

• Added support for ordinal outcome modeling through complementary log-log link function in the BART models (#196)
• Added __str__, __repr__, summary, and extract_parameter methods in Python for the BARTModel and BCFModel classes (#298)
• Added plotting utility function (plot_parameter_trace) in Python that operates on both the BARTModel and BCFModel classes (#298)
• Added vignettes for summary / model inspection utilities in both R and Python (#298)
• Added print methods in R and __str__ methods in Python for the forest container and random effects container objects (#298)
• Updated R documentation to group related functions into topics (#302):
  • BARTSerialization
  • BCFSerialization
  • ForestSamplesSerialization
  • RandomEffectSamplesSerialization
  • DataPreprocessing
  • ForestKernelComputation
  • ForestStateManagement
  • RandomEffectStateManagement
• Converted the following R function names from snake case to camel case (#302):
  • compute_bart_posterior_interval -> computeBARTPosteriorInterval
  • compute_bcf_posterior_interval -> computeBCFPosteriorInterval
  • compute_contrast_bart_model -> computeContrastBARTModel
  • compute_contrast_bcf_model -> computeContrastBCFModel
  • extract_parameter -> extractParameter
  • sample_bart_posterior_predictive -> sampleBARTPosteriorPredictive
  • sample_bcf_posterior_predictive -> sampleBCFPosteriorPredictive

Bug Fixes

• Fixed status logging bugs for multi-chain R MCMC loops (#298)

stochtree 0.3.1 (2026-02-12)

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.

stochtree 0.3.0 (2026-01-27)

New Features

• Added print, summary, plot, and extract_parameter generic functions in R for the bartmodel and bcfmodel classes (#271)
• Added sklearn-compatible estimator wrapper for BARTModel in Python (#270)

Bug Fixes

• 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)

stochtree 0.2.1 (2025-12-11)

Bug Fixes

• 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)

Other Changes

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

stochtree 0.2.0 (2025-11-22)

New Features

• 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)

Computational Improvements

• Modified default random effects initialization (#190)
• Avoid double prediction on training set (#178)

Bug Fixes

• 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)

Documentation Improvements

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

Other Changes

• 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)

stochtree 0.1.1 (2025-02-08)

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

stochtree 0.1.0 (2025-02-07)

• 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