Changes and New Features in 2.9 (2020-12-21): * update CITATION and DOI along with help file references for publication of our article in JSS * from here on: bug-fixes only package frozen in order to be in synch with article * for new developments, see BART3 on our github site at https://github.com/rsparapa/bnptools Changes and New Features in 2.8 (2020-11-27): * fix bugs in mc.pwbart * gbart: return ndpost as an item like mc.gbart Changes and New Features in 2.7 (2019-12-04): * addressing new behavior of class function Changes and New Features in 2.6 (2019-07-24): * updated vignette with typo corrections and additional info Changes and New Features in 2.5 (2019-06-10): * new feature: stratrs function now handles continuous data as well categorical * bug fix: bartModelMatrix now correctly handles all-missing columns with numcut>0 and rm.const=T * bug fix: fix gbart syntax error in LPML feature * bug fix: fix a mc.pbart error which failed to recalculate prob.train.mean and prob.test.mean based on all chains 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