Package: BayesTreePrior 1.0.1

BayesTreePrior: Bayesian Tree Prior Simulation

Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <doi:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <doi:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).

Authors:Alexia Jolicoeur-Martineau <[email protected]>

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
BayesTreePrior/json (API)

# Install 'BayesTreePrior' in R:

install.packages('BayesTreePrior', repos = c('https://alexiajm.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alexiajm/bayestreeprior/issues

On CRAN:

bayesian-trees cart prior-distribution simulation tree

3.59 score 7 stars 11 scripts 275 downloads 11 exports 0 dependencies

Last updated from:0ba57f15d2. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	NOTE	129
source / vignettes	OK	160
linux-release	NOTE	101
macos-release	NOTE	173
macos-oldrel	NOTE	177
windows-devel	NOTE	67
windows-release	NOTE	67
windows-oldrel	NOTE	70
wasm-release	OK	99

Exports:BayesTreePrior BayesTreePriorNotOrthogonal BayesTreePriorOrthogonal BayesTreePriorOrthogonalInf E_alpha GetListUniqueSplits NumBotMaxDepth NumBotMaxDepth_inf NumBotMaxDepthX p_split Var_alpha

Dependencies:

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Simulation of the tree prior.	BayesTreePrior
Simulation of the tree prior in the general case (Case #4).	BayesTreePriorNotOrthogonal
Simulation of the tree prior in the case where we have one single variable (Case #3).	BayesTreePriorOrthogonal
Simulation of the tree prior in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) (Case #2).	BayesTreePriorOrthogonalInf
Expected value of the number of bottom nodes in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) and beta=0 (Case #1).	E_alpha
Unique splits that leads to children with more than minpart nodes.	GetListUniqueSplits
Number of bottom nodes and depth in the case where we have one single variable (Case #3).	NumBotMaxDepth
Number of bottom nodes and depth in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) (Case #2).	NumBotMaxDepth_inf
Number of bottom nodes and depth in the general case (Case #4).	NumBotMaxDepthX
Probability of split of the tree prior.	p_split
Variance of the number of bottom nodes in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) and beta=0 (Case #1).	Var_alpha