Changes in version 0.4.4                        

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.

                        Changes in version 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

                 Changes in version 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

                        Changes in version 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

                 Changes in version 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)

                 Changes in version 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.

                 Changes in version 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)

                 Changes in version 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)

                 Changes in version 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)

                 Changes in version 0.1.1 (2025-02-08)                  

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

                 Changes in version 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