No longer depends on gfortran.
Uses SIMD instructions on M1 Macs.
callback functionality to
Custom loss functiosn for
xbart now require an additional
Fixed a multithreaded issue leading to inconsistent results
rbart_vi should now correctly use default arguments.
rbart_vi now works with
keepTrainingFits as false.
Weighted binary responses sample latent variables from the correct distribution.
Extracting the values from the posterior predictive distribution for models with weights now incorporates them into the variance.
Weighted values are considered in loss functions for crossvalidation.
extract now accepts as a type
allows for easier inspection of models fit with
fits; implementation thanks to Emil Hvitfeldt.
xbart now accepts a
seed argument to enhance
dbarts through its
tree.prior argument) accept
split.probs which controls the prior probability that any
variable is used when splitting observations.
rbart_vi models now uses a C++
implementation for the expected value that uses less memory
and is faster.
xbart for binary outcomes with log loss no longer returns
NaN when some subset of the response is perfectly predicted by the
covariates. Bug report thanks to Marcela Veselkova.
dbarts now exposes access to the underlying proposal
rules and their probabilities through its
bart2 response to the same argument, while
rbart_vi accept a
seed argument that will yield reproducible results, even
when running with multiple threads and multiple chains.
The interface registered under
changed to reflect proper fixed hyperpriors for
Samples of the end-node sensitivity parameter,
are returned by
rbart_vi when it modeled.
Burn-in samples of the end-node sensitivity parameter,
k, are included in the results of
rbart_vi will now look for
group.by.test in the
before looking in the
formula or calling environments.
k mixing across chains when running multithreaded
k being modeled. Bug report thanks to Noah
method = "k-fold" when data
not evenly divided by number of folds. Rug report thanks to Jesse
(@ALEXLANGLANG on Github).
getLatents and corresponding C function now
add user supplied offset to result.
Saved, flattened trees now correctly partition observations on left and right.
Samplers now have method
When used in conjunction with
the model to fully make predictions from the prior distribution.
dbartsControl (and now
...) now accept
rngSeed argument. This can be used
to generate reproducible results with multiple threads. It should
only be used for testing, as the thread-specific pRNGs are seeded
using sequential draws from a pRNG created with the user-supplied
C interface supports
dbarts_initializeState which can be used to re-create
samplers that were allocated using forked multithreading.
C interface also supports
makeTestModelMatrix to allow package authors to
create test data at a later point from training data.
bart fits now has dimnames set.
residuals generic added to
rbart now creates the correct
number of chains.
Should now compile on non-x86 architectures. Report thanks to Lars Viklund.
Fixed hang when
verbose = TRUE for multiple threads and
multiple chains. Report thanks to Noah Greifer.
Fixed potential memory access errors when recreating sample from saved state.
Correctly de-serializes saved tree structure.
Sampler now explicitly supports
setSigma for use in
setOffset has an additional argument
updateScale. When the response is continuous and
TRUE, the implicit scaling,
effecting the node parameters' variance, is adjusted to match
the range of the new data. This optionally reverts the change
of version 0.9-13 with the intention of being used only during
warmup when using an offset that is itself being sampled.
Extraneous print line from debugging 0.9-17.
Eliminated two race conditions from multithreaded crossvalidation. Report thanks to Ignacio Martinez.
Eliminated garbage read on construction of crossvalidation sampler, removing inconsistencies across multiple runs with the same starting seed.
makeModelMatrixFromDataFrame now converts character vectors
to factors instead of dropping them. Report thanks to Colin Carlson.
Memory leak for
fitted generics for
"test" sets of
observations while returning
"ev" - samples from the
posterior of the individual level expected value,
- the sum of trees component; same as
"ev" for linear
models but on the probit scale for binary ones, and
- samples from the posterior predictive distribution. To synergize
"response" can be used as a synonym
"link" can be used as a synonym for
bart models with binary outcomes returns
a result on the probability scale, not probit. The argument
value is deprecated - use
predict further conforms to the same system of arguments as
xbart with a k-hyperprior should no longer crash. Report thanks
to Colin Carlson.
rbart_vi now work with generics
samples from the posterior distribution for the training and test
fitted applies averages across those samples,
predict can be used to obtain values for completely
rbart_vi takes value "ev" instead of "post-mean"
to clarify what is being returned, i.e. samples from the posterior
distribution of the observation-level expected values.
load should work correctly. Report thanks to Jeremy Coyle.
predict now works when trees aren't saved, for use in testing
offset slot no longer changes the relative scaling of the
response. This stabilizes predictions across iterations. For a semantic
where the scaling does change, use
Varying intercepts model for probit regression.
A hyperpriors for
k has now been implemented. Passing
k = chi(degreesOfFreedom, scale) now penalizes small values of
k, encouraging more shrinkage.
chi(1.25, Inf) is now default for
with binary outcomes. The default accuracy should improve substantially.
xbart divides data correctly with random subsampling.
More control over cut points has been added. It is now possible to specify
the cut points for a variable once and subsequently change that predictor
without also modifying the cuts using
sampler$getTrees implemented to get a flattened, depth-first down
left traversal of the trees.
sampler$setPredictor, an argument specifies whether or not to
rollback or force the change if the new data would result in a leaf
having 0 observations.
pd2bart now work with formula/data specifications,
as well as taking models or samplers that have previously stored trees.
x as integer matrix of the max of which cut point an observation is
to the left of, by default using 16 bit integers. Limited to 65535 cut points.
That can be increased with some special compilation instructions.
Uses CPU dispatch and SIMD instructions for some operations. This and the integer
x make BART about 30% faster on datasets of around 10k observations.
Saved trees are stored using significantly less memory.
plot now works for fits from
rbart_vi new reports
bart2 now defaults to not storing trees due to the memory cost.
bart2 now defaults to using quantile rules to decide splits.
predict for binary outcomes now correct.
Fix for verbose multithreading on Linux, reported by @ignacio82 on github.
General improvements to slice sampler in
rbart_vi thanks to reports from Yutao Liu.
sampler$plotTree now handles multiple chains correctly.
Negative log loss for
xbart with binary outcomes should now be computed correctly.
rbart_vi fits a simple varying intercept, random effects model.
Now natively supports multiple chains running in parallel.
Objects fit by
bart can be used with the predict generic
when instructed to save the trees.
bart2 introduced, similar to
bart but with
more efficient default parameters.
dbartsControl has had two parameters renamed:
numBurnIn is now
dbartsControl supports parameters
In the C interface, a new function (
setRNGState) has been
added to specify the states of the random number generators, of which there
is now one for every chain.
State objects saved by the handles no longer contain the total fits, since they can be rebuild from the tree fits. States are also lists of objects now, with one corresponding to each chain. Tree fits and strings are matrices corresponding to the number of trees and saved samples.
random subsampling crossvalidation (
xbart) has been implemented
in C++. Refits model using current set of trees for changes in
Rudimentary tree plotting added to sampler (
dbartsData as a way of constructing data objects
and setting the data seen by the sampler all at once. Sampler now supports
keepevery argument to
bart now has argument
keepcall to suppress
storing the call object.
bart now accepts a
MakeModelMatrixFromDataFrame now implemented in C, supports
an argument for tracking/keeping dropped values from factors.
Usage of weights was causing incorrect updates to posterior for
Should now JIT byte compile correctly.
Cuts derived from quantiles should now be valid.
Uses a rejection sampler to simulated binary latent variables (CP Robert 2009, http://arxiv.org/pdf/0907.4010.pdf). Code thanks to Jared Murray.
Now encapsulates its own random number generator, so that the C++ objects can safely be used in parallel. Shouldn't affect pure-R users unless their RNG has non-exported state (i.e. Box-Muller normal kind).
offset.test vector that can be controlled
independently of the
offset vector, but in general inherits
behavior from it. Set at creation with
dbarts() or after
By default, no longer attempts to obtain identical results as
BayesTree. To recover this behavior, compile from source with
configure.args = "--enable-match-bayes-tree".
Changing the entirety of the test matrix using
no longer allowed. Use
Changing the predictor can now result in failure if the covariates
would leave an end-node empty.
setPredictor returns a logical
as to success.
dbarts objects may not be compatible and should be
re-created to be sure of valdity.
Now requires R versions >= 3.1.0.
Corrected binary latent variable sampler and no longer multiply adds offset (reported by Jared Murray).
Relatively embarassing bug related to loop-unrolling when
n mod 5 != 0
Correct aggregation of results for multithreaded variance calculations.
More equitably distributed tasks across multiple threads.
Makevars tweaked to allow compilation on Ubuntu.
Initial public release.