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_pkgdown.yml
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url: https://stochastictree.github.io/stochtree-r/
template:
bootstrap: 5
reference:
- title: Supervised learning
desc: >
High-level functionality for training supervised Bayesian tree ensembles (BART, XBART)
contents:
- bart
- predict.bartmodel
- title: Causal inference
desc: >
High-level functionality for estimating causal effects using Bayesian tree ensembles (BCF, XBCF)
contents:
- bcf
- predict.bcfmodel
- title: Low-level functionality
- subtitle: Serialization
desc: >
Classes and functions for converting sampling artifacts to JSON and saving to disk
contents:
- CppJson
- createCppJson
- createCppJsonFile
- createCppJsonString
- loadForestContainerJson
- loadForestContainerCombinedJson
- loadForestContainerCombinedJsonString
- loadVectorJson
- loadScalarJson
- loadRandomEffectSamplesJson
- loadRandomEffectSamplesCombinedJson
- loadRandomEffectSamplesCombinedJsonString
- saveBARTModelToJson
- saveBARTModelToJsonFile
- saveBARTModelToJsonString
- createBARTModelFromJson
- createBARTModelFromJsonFile
- createBARTModelFromJsonString
- createBARTModelFromCombinedJson
- createBARTModelFromCombinedJsonString
- saveBCFModelToJson
- saveBCFModelToJsonFile
- saveBCFModelToJsonString
- createBCFModelFromJsonFile
- createBCFModelFromJsonString
- createBCFModelFromJson
- createBCFModelFromCombinedJson
- createBCFModelFromCombinedJsonString
- subtitle: Data
desc: >
Classes and functions for preparing data for sampling algorithms
contents:
- ForestDataset
- createForestDataset
- Outcome
- createOutcome
- RandomEffectsDataset
- createRandomEffectsDataset
- preprocessTrainData
- preprocessPredictionData
- convertPreprocessorToJson
- savePreprocessorToJsonString
- createPreprocessorFromJson
- createPreprocessorFromJsonString
- subtitle: Forest
desc: >
Classes and functions for constructing and persisting forests
contents:
- Forest
- createForest
- ForestModel
- createForestModel
- ForestSamples
- createForestSamples
- ForestModelConfig
- createForestModelConfig
- GlobalModelConfig
- createGlobalModelConfig
- CppRNG
- createCppRNG
- calibrateInverseGammaErrorVariance
- computeForestMaxLeafIndex
- computeForestLeafIndices
- computeForestLeafVariances
- resetActiveForest
- resetForestModel
- subtitle: Random Effects
desc: >
Classes and functions for constructing and persisting random effects terms
contents:
- RandomEffectSamples
- createRandomEffectSamples
- RandomEffectsModel
- createRandomEffectsModel
- RandomEffectsTracker
- createRandomEffectsTracker
- getRandomEffectSamples
- getRandomEffectSamples.bartmodel
- getRandomEffectSamples.bcfmodel
- sampleGlobalErrorVarianceOneIteration
- sampleLeafVarianceOneIteration
- resetRandomEffectsModel
- resetRandomEffectsTracker
- rootResetRandomEffectsModel
- rootResetRandomEffectsTracker
- title: Package info
desc: >
High-level package details
contents:
- stochtree-package
articles:
- title: High-Level Model Fitting
navbar: High-Level Model Fitting
contents:
- BayesianSupervisedLearning
- CausalInference
- Heteroskedasticity
- title: Advanced Model Interface
navbar: Advanced Model Interface
contents:
- MultiChain
- ModelSerialization
- PriorCalibration
- EnsembleKernel
- TreeInspection
- title: Prototype Interface
navbar: Prototype Interface
contents:
- CustomSamplingRoutine