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
