Bayesian Additive Regression Trees

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch .


News

Changes and New Features in 2.4 (2019-04-10): * change: per CRAN policy, dynamic libraries are no longer "stripped" on Linux

Changes and New Features in 2.3 (2019-03-27): * new feature: adding arguments to surv.pre.bart, surv.bart and mc.surv.bart to fine-tune grid of time points and automate creation of time dependent covariates. These are convenience features to make multi-state models easier to handle; see new demo leuk

* change: arguments to rtnorm and rtgamma more user friendly

* new feature: re-organized vignettes into a single vignette

* new feature: gbart now calculates log pseudo-marginal
likelihood (LPML) for computing pseudo-Bayes factors

* new feature: new Generalized BART Mixed Models, see the
function gbmm

Changes and New Features in 2.2 (2019-01-22):

* bug fix: fix typo in size of theta grid for sparse prior

* new feature: Multinomial BART, mbart2, (suitable for
cases with more categories) based on the original mbart
implementation but inspired by the logit transformation;
nevertheless, both logit and probit are available and, 
of course, probit is much faster

Changes and New Features in 2.1 (2018-11-28):

* to meet current CRAN guidelines, replaced CXX1X and CXX1XSTD 
configure/autoconf macros with CXX11 and CXX11STD respectively

Changes and New Features in 2.0 (2018-11-12):

* new feature: Multinomial BART, mbart, (suitable for cases
with relatively fewer categories) replaced with a new
conditional probability implementation which allows the user
to choose probit or logit BART; of course, probit BART is 
much faster

* new feature: if lambda is specified as 0, then sigma is
considered to be fixed and known at the value sigest 
    and, therefore, not sampled

* bug fix: fixed single column x.test bug

Changes and New Features in 1.9 (2018-08-17):

* bug fix: off by one error fixed in robust Gamma 
generator for sparse Dirichlet prior

    * new feature: abart/mc.abart computes a variant
of the Accelerated Failue Time model based on BART

* new feature: for x.train/x.test with missing data elements, 
gbart will singly impute them with hot decking.  
Since mc.gbart runs multiple gbart threads in parallel, 
mc.gbart performs multiple imputation with hot decking, 
i.e., a separate imputation for each thread.

Changes and New Features in 1.8 (2018-06-30):

* bug fix: fix typo in the recur.pwbart() which
prevented predict() from working when OpenMP
was not available

Changes and New Features in 1.7 (2018-06-08):

* enhancement: generalized, or generic, BART: gbart/mc.gbart
unites continuous and binary BART in one function call
re-based time-to-event BARTs on gbart as well

* enhancement: binaryOffset=NULL specifies 
binaryOffset=qXXXX(mean(y.train)) for pbart/mc.pbart,
lbart/mc.lbart, mbart/mc.mbart; offset=NULL does the
same for gbart/mc.gbart, surv.bart/mc.surv.bart, 
recur.bart/mc.recur.bart, crisk.bart/mc.crisk.bart
and crisk2.bart/mc.crisk2.bart (note: competing 
cause 2 is handled analogously for offset2=NULL)

* enhancement: multinomial BART rebased on probit BART for 
computational efficiency

* bug fix: several corrections in probit and logit BART.
Note that this may change your results for binary and
time-to-event outcomes.  For probit BART, the correction
generally leads to a small change in the results.  However,
the logit BART correction may lead to more substantial
changes.

* doc fix: correct docs for the binary case in pbart/mc.pbart, 
lbart and mbart; and correct docs for the numeric case in
wbart/mc.wbart

* enhancement: robust Gamma generation for small scale parameter

* enhancement: more robust sparse Dirichlet prior implementation

Changes and New Features in 1.6 (2018-03-19):

* for binary outcomes, new default for ntree=50
  (change inadvertently omitted from v1.4 below)

    * enhancement: recur.pre.bart, recur.bart and mc.recur.bart
  can now handle NA entries in the times and delta matrices

* enhancement: for time-to-event outcomes, new optional 
  K parameter which coarsens time per the quantiles
      1/K, 2/K, ..., K/K.

* bug fix: x.test/x.test2 now properly transposed if needed
  for post-processing

* bug fix: sparse Dirichlet prior now corrected for 
  random theta update.  Thanks to Antonio Linero for
  the detailed bug report.

Changes and New Features in 1.5 (2018-02-08):

    * bug fix: ambiguous call of floor surrounding integer division

* bug fix: x.test is not an argument of recur.pre.bart

Changes and New Features in 1.4 (2018-02-02):

* for binary outcomes, new default for ntree=50

    * fixed library bloat on Linux with strip

* x.train and x.test can be supplied as data.frames
  which contain factors as stated in the documentation

* cutpoints now based on data itself, i.e., binary or
  ordinal covariates.  Similarly, you can request 
  quantiles via the usequants setting. 

* sparse variable selection now available with the
  sparse=TRUE argument; see the documentation

* new vignettes

* new function, mc.lbart, for logit BART in parallel

* mbart updated to equivalent functionality as other functions

* new function, mc.mbart, for Multinomial BART in parallel

Changes and New Features in 1.3 (2017-09-18):

    * new examples in demo directory

* return ndpost values rather ndpost/keepevery

* for calling BART directly from C++, you can
  now use the RNG provided by Rmath or the STL random class
  see the improved example in cxx-ex

* new predict S3 methods, see predict.wbart and other
  predict variants

* Added Geweke diagnostics for pbart, surv.bart, etc.
  See gewekediag which is adapted from the coda package

* logit BART added for binary outcomes; see lbart

* Multinomial BART added for categorical outcomes; see mbart

Changes and New Features in 1.2 (2017-04-30):

* you can now call BART directly from C++ with the Rmath library 
  see new header rn.h and the example in cxx-ex

Changes and New Features in 1.1 (2017-04-13):

* No user visible changes: bug-fix release

Changes and New Features in 1.0 (2017-04-07):

* First release on CRAN

Reference manual

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install.packages("BART")

2.9 by Rodney Sparapani, 9 months ago


Browse source code at https://github.com/cran/BART


Authors: Robert McCulloch [aut] , Rodney Sparapani [aut, cre] , Charles Spanbauer [aut] , Robert Gramacy [aut] , Matthew Pratola [aut] , Martyn Plummer [ctb] , Nicky Best [ctb] , Kate Cowles [ctb] , Karen Vines [ctb]


Documentation:   PDF Manual  


Task views: Machine Learning & Statistical Learning, Bayesian Inference


GPL (>= 2) license


Imports Rcpp, parallel, tools

Depends on nlme, nnet, survival

Suggests MASS, knitr, rmarkdown

Linking to Rcpp


Imported by CIMTx, SAMTx, borrowr, paths.

Depended on by cjbart.

Suggested by MachineShop, StratifiedMedicine, tidytreatment.


See at CRAN