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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.