stochtree R 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

stochtree Python 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

stochtree R 0.4.0

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)

stochtree Python 0.4.0

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)

stochtree Python 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 R 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 Python 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 R 0.3.0

New Features

• Added print, summary, plot, and extract_parameter generic functions in R for the bartmodel and bcfmodel classes (#271)

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 Python 0.2.1

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 R 0.2.1

stochtree 0.2.1

Bug Fixes

• Fix prediction bug for univariate random effects models in R (#248)

Other Changes

• Encode expectations about which combinations of BART / BCF features work together and ensure warning (#250)