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)
• 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

• 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