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
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
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 status logging bugs for multi-chain R MCMC loops (#298)
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.
stochtree 0.3.0
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
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
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)
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
Fixed initialization bug in several R package code examples for
random effects models
stochtree 0.1.0
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