Package: randomForestSRC
Version: 2.8.0 (bld20190102)

---------------------------------------------------------------------------------
FOR BETA BUILDS, DOCUMENTATION, and dual compilation for R and
  Spark/Java (experimental) please see:
  https://github.com/kogalur/randomForestSRC
---------------------------------------------------------------------------------


---------------------------------------------------------------------------------
CHANGES TO RELEASE 2.8.0
---------------------------------------------------------------------------------
Ensembles in regression now support Greenwald-Khanna approximate
quantile queries via rfsrc(), predict.rfsrc() and the new wrapper
quantileReg.rfsrc(). Related to this, a new split rule "quantile.regr"
has been added.  Specifications will be added to the GitHub page,
shortly.  Another new wrapper, imbalanced.rfsrc(), implements various
solutions to the two-class imbalanced problem, including the newly
proposed quantile-classifier approach of O'Brien and Ishwaran (2017).
Also includes Breiman's balanced random forests undersampling of the
majority class.  Performance is assesssed using the G-mean, but
misclassification error can be requested. Also, the new parameter
get.tree in predict.rfsrc() allows users to extract the ensembles for
a single tree or subset of trees over the forest. Finally, the default
nodesize for survival and competing risk has been changed to 15.
    
---------------------------------------------------------------------------------
RELEASE 2.7.0
---------------------------------------------------------------------------------
Three primary additions:
  (1) Subsample Forests for VIMP Confidence Intervals:
  Uses subsampling to calculate confidence intervals and standard
  errors for VIMP (variable importance). Applies to all families.
  (2) Tune Random Forest for the optimal mtry and nodesize parameters:
  Finds the optimal mtry and nodesize tuning parameter for a random
  forest using out-of-bag (OOB) error.  Applies to all families. 
  (3) Fast approximate random forests:  Uses subsampling with forest options
  set to encourage computational speed.  Applies to all families.

---------------------------------------------------------------------------------
RELEASE 2.6.1
---------------------------------------------------------------------------------
Fix to predict() call not returning ensembles when y-vars not present.  Sorry
about that.
  
---------------------------------------------------------------------------------
RELEASE 2.6.0
---------------------------------------------------------------------------------
Serious improvements to OpenMP performance after addressing issues
related to the blocking of threads during a number of calculations
involving ensembles, importance, forest weights, and partial plots
mostly in big-n data sets.
  
---------------------------------------------------------------------------------
RELEASE 2.5.1
---------------------------------------------------------------------------------
Addition of configure file to source package allowing more accessible OpenMP
parallel execution on systems that support it.
  
---------------------------------------------------------------------------------
RELEASE 2.5.0
---------------------------------------------------------------------------------
Introduction of conditional quantiles for a regression forest.
Applies to both univariate and multivariate forests.  Can be used for
both training and testing purposes and returns the conditional
quantiles for the target outcomes, and conditional density, which can
be used to calculate conditional moments, such as the mean and
standard deviation.

---------------------------------------------------------------------------------
RELEASE 2.4.2
---------------------------------------------------------------------------------
Bug fixes to partial.rfsrc() on R and C side.  Allowance of second
order variable specification in this analysis.  Conditional importance
values in classification adjusted by a factor of exp(1).  Bug fix to unsafe
threading in LB-VIMP calculations.

---------------------------------------------------------------------------------
RELEASE 2.4.1
---------------------------------------------------------------------------------
Fix to typedef that breaks Linux.  Sorry about that.
  
---------------------------------------------------------------------------------
RELEASE 2.4.0
---------------------------------------------------------------------------------
Fix to custom splitting family verification and registration harness.
Introduction of bootstrap="by.user".  Fix to incorrect mapping of user
specified time points to event times when ntime option is used.  It is
recommended that the use of this option be avoided.  The effect of
discretizing the time values compromises the ensembles. For best
results, all event times must be used.  This was and is the default
behaviour.  Fix to incorrect passing of time option parameter in
plot.variable().  Introduction of partial.rfsrc() to allow direct
access to partial ensembles.  Added support for long vectors on native
code side.

  
RELEASE 2.3.0
---------------------------------------------------------------------------------  
Fix to levels.count when ntree=1.  Some n-based loop optimization.
Significant improvements in CPU times for restore-predict modes, and
plot.variable().  Consequent changes to forest object, and
incompatibility with objects created with previous versions of the
package.  Default is now importance=FALSE in predict.rfsrc().

RELEASE 2.2.0
---------------------------------------------------------------------------------
Bug fix to coerce.factor option via get.xvar.nlevels() and
get.yvar.nlevels() resolved by sending in max instead of number of
levels.  Bug fix to VIMP that potentially occurs in OpenMP mode
causing non-zero LB-VIMP. Methodological fix to in-node imputation.
and removal of na.random.  Consequent incompatibility with objects
created with previous versions of the package. Fix to rfsrcSyn() bug
pertaining to colnames of test set synthetic features. Introduction of
sampsize, samptype, and case.wt to address imbalanced data sets.
Continued improvements to CPU and memory performance in big-n, big-p,
and big-ntree scenarios.
  
RELEASE 2.1.0
---------------------------------------------------------------------------------
Change to GROW mode default importance=none and to allow importance=TRUE.
Addition of user trace with time estimates.
CPU usage - code optimization of ensemble calculations.
CPU usage - code optimization of imputation. 
Fix to R-side parsing of ensembles in multivariate classification.
Change to treat ordered factors under classification setting
instead of regression.
    
---------------------------------------------------------------------------------
RELEASE 2.0.7
Fix to factor coercion option in responses.  Fix to R-side processing
of err.rate and importance in multivariate families with
classification. Update of OPENMP protocols per CRAN recommendation.
Expansion of fast.restore option to omit performance on every tree,
and update to associated Rd file.

---------------------------------------------------------------------------------
RELEASE 2.0.5  
Fix to bug in dimensioning of predict object in survival families.
Added documentation for custom splitting.

---------------------------------------------------------------------------------
RELEASE 2.0.0
Multivariate capabilities added. Custom splitting harness
modifications.  Redefinition of nodesize to allow terminal nodes less
than said size, subject to the initial test for 2 x nodesize before
the split, maximum depth, and purity.  Various bug fixes.

RELEASE 1.6.1
Fix to donttest example in rfsrc.Rd, and other adjustments per
CRAN packaging protocols.

---------------------------------------------------------------------------------
RELEASE 1.6.0
Bug fix to duplicating missingness protocol when restoring a forest.
Added fast.restore option to grow call.  Change to pass through
xvar.wt as entered by user. RAM profile reduction in vimp().  Added
versioning checks of forest object, thanks to suggestions by John
Ehrlinger.  Bug fix to allow logical responses, treated as reals. User
trace functionality restored.

---------------------------------------------------------------------------------
RELEASE 1.5.5
Bug fix to daughter assignment in classification.  Significant RAM
optimization in all modes.

---------------------------------------------------------------------------------
RELEASE 1.5.4
Addition of new function stat.split() for extracting information from
tree node splitting-statistics.  Added more functionality to
rfsrcSyn() for fitting synthetic random forests.

---------------------------------------------------------------------------------
RELEASE 1.5.3
Addition of rfsrcSyn() function to grow a synthetic random forest (RF)
using RF machines as synthetic features.  Applies only to regression
and classification settings.  Used for prediction only.

---------------------------------------------------------------------------------
RELEASE 1.5.2
Fix to non-standard GCC errors and warnings.  Fix to bug in split
rules related to omission of missing individuals in the split
statistic.  Minor R-side fixes.

---------------------------------------------------------------------------------
RELEASE 1.5.1
Fix to UBSAN warnings.  Implementation of new RG protocols.

---------------------------------------------------------------------------------
RELEASE 1.5.0
Significant improvements to CPU and RAM usage profiles in serial and
OpenMP modes of execution.  Proximity options allow inbag, OOB and all.
VIMP implements sub-setting and conditional variable importance. NA
options allow the split statistic to be based on non-missing values
only.  In addition it allows random assignment of missing values.

---------------------------------------------------------------------------------
RELEASE 1.4.0
Modification of terminal node imputation protocol.  We now assign all
individuals the same value rather than sampling from the distribution.
Implementation of split.null option.  Implementation of unsupervised
splitting for missing data in impute.rfsrc().  Modification of nimpute
> 1 protocols.  In-bag, OOB, and all now depend on the mode.
Reduction in impute memory footprint.  Modification of proximity
option to allow in-bag, OOB, and all.  Fixed bug in predict involving
manual formula calls.  Fixed bug in find.interaction involving
specifying covariate names.  Changes relating to Undefined Behaviour
Sanitizer.

---------------------------------------------------------------------------------
RELEASE 1.3.0

Initial re-engineering of memory footprint for imputation.
Performance enhancements to split rules.

---------------------------------------------------------------------------------
RELEASE 1.2.0

Competing risks now implements two distinct splitting rules for
identifying short term risks affecting the cause-specific hazard or
long term predictions affecting the cumulative incidence function. The
plot.variable function now returns, and can reuse, a plot.variable
data structure object for user convenience. Thanks to John Ehrlinger
for this improvement. Other minor bug fixes, and enhancements.

---------------------------------------------------------------------------------
RELEASE 1.1.0
OpenMP performance enhancements to ensemble and variable importance calculations.

---------------------------------------------------------------------------------
RELEASE 1.0.2
Fix to [S] missingness check when all status are non-censored.
Fix to [S] summary imputation of time.
Fix to variables used all.trees output.
Fix to manual formula interface.
Removed big.data option.
Added ntime option for survival families.

---------------------------------------------------------------------------------
RELEASE 1.0.1
Replaced 'suggests multicore' with 'depends parallel'.  Followed
protocol in parallel package for controlling number of cores, via
options(), and environment variables.  Reduced [S] memory footprint by
prematurely de-allocating terminal node information.

---------------------------------------------------------------------------------
RELEASE 1.0.0 represents the first release of the
package.  

