- 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