Changes and New Features in 2.9.6 (2024-01-09): * resolving CRAN checks for printf and Rd Changes and New Features in 2.9.4 (2023-03-25): * C++11 is now the CRAN standard and C++17 is not needed * casting signed int to unsigned for comparisons with unsigned Changes and New Features in 2.9.3 (2023-02-04): * resolving CRAN checks for mc_cores_openmp and recur.pwbart * src/Makevars is "hard-wired" for OpenMP on Windows * surv.pre.bart: times beyond the events grid are necessarily censored Changes and New Features in 2.9.1, 2.9.2: version numbers skipped 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 * N.B. the warnings "unused variable 'grp'" are purposely kept, i.e., if you want the Dirichlet sparse prior to recognize factors as grouped variables, then you need to use BART3 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