Fixed WARNING under fedora-clang-devel. Added climate.dat file to package for building vignette so that package does not violate CRAN’s policy for accessing internet resources and is more permament if file location/url changes locally.
Fixed testthat errors under Solaris. Default settings for
force.heredity is set back to FALSE in
bas.glm so that methods work on all platforms. For Solaris, users who wish to impose the
force.heredity constraint may use the post-processing function.
Fixed valgrind error in src/ZS_approx_null_np.c for invalid write noted in CRAN checks
fixed function declaration type-mismatch and argument errors identified by LTO noted in CRAN checks
Fixed errors identified on cran checks https://cran.r-project.org/web/checks/check_results_BAS.html
initialize R2_m = 0.0 in lm_mcmcbas.c (lead to NA’s with clang on debian and fedora )
switch to default of
pivot = TRUE in
tol as an argument to control tolerance in
cholregpovot for improved stability across platforms with singular or nearly singular designs.
valgrind messages: Conditional jump or move depends on uninitialised value(s). Initialize vectors allocated via R_alloc in lm_deterministic.c and glm_deterministic.c.
Included an option
bas.lm to fit the models using a pivoted Cholesky decomposition to allow models that are rank-deficient. Enhancement #24 and Bug #21. Currently coefficients that are not-estimable are set to zero so that
predict and other methods will work as before. The vector
rank is added to the output (see documentation for
bas.lm) and the degrees of freedom methods that assume a uniform prior for obtaining estimates (AIC and BIC) are adjusted to use
rank rather than
force.heredity=TRUEto force lower order terms to be included if higher order terms are present (hierarchical constraint) for
bas.glm. Updated Vignette to illustrate. enhancement #19. Checks to see if parents are included using
include.always pass issue #26.
plot.bas so that variables that are always included may be excluded from the plot showing the marginal posterior inclusion probabilities (
which=4). By default all are shown enhancement #23
fitted.bas to use predict so that code covers both GLM and LM cases with
Updates to package for CII Best Practices Badge certification
Added Code Coverage support and more extensive tests using
include.always to include the intercept issue #26 always so that
drop.always.included = TRUE drops the intercept and any other variables that are forced in.
force.heredity=TRUE can now be used together with
added warning if marginal likelihoods/posterior probabilities are NA with default model fitting method with suggestion that models be rerun with
pivot = TRUE. This uses a modified Cholesky decomposition with pivoting so that if the model is rank deficient or nearly singular the dimensionality is reduced. Bug #21.
corrected count for first model with
method='MCMC' which lead to potential model with 0 probability and errors in
coerced predicted values to be a vector under BMA (was a matrix)
size with using
bas.glm (was not updated)
fixed problem in
horizontal=TRUE when intervals are point mass at zero.
force.heredity.bas to renormalize the prior probabilities rather than to use a new prior probability based on heredity constraints. For future, add new priors for models based on heredity. See comment on issue #26.
Changed License to GPL 3.0
variable.namesto extract variable names in the highest probability model, median probability model, and best probability model for objects created by
include.always as new argument to
bas.lm. This allows a formula to specify which terms should always be included in all models. By default the intercept is always included.
added a section to the vignette to illustrate weighted regression and the
force.heredity.bas function to group levels of a factor so that they enter or leave the model together.
confint.coef.baswhen parm is a character string
fixed issue with scoping in eval of data in
predict.bas if dataname is defined in local env.
fixed issue 10 in github (predict for estimator=‘BPM’ failed if there were NA’s in the X data. Delete NA’s before finding the closest model.
fixed bug in ‘JZS’ prior - merged pull request #12 from vandenman/master
fixed bug in bas.glm when default betaprior (CCH) is used and inputs were INTEGER instead of REAL
removed warning with use of ‘ZS-null’ for backwards compatibility
Added new method for
bas.lm to obtain marginal likelihoods with the Zellner-Siow Priors for "prior= ‘JZS’ using QUADMATH routines for numerical integration. The optional hyperparameter alpha may now be used to adjust the scaling of the ZS prior where g ~ G(1/2, alpha*n/2) as in the
BayesFactor package of Morey, with a default of alpha=1 corresponding to the ZS prior used in Liang et al (2008). This also uses more stable evaluations of log(1 + x) to prevent underflow/overflow.
ZS-full for bas.lm is planned to be deprecated.
replaced math functions to use portable C code from Rmath and consolidated header files
Extract coefficient summaries, credible intervals and plots for the
MPM in addition to the default
BMA by adding a new
estimator argument to the
coef function. The new
n.models argument to
coef provides summaries based on the top
n.models highest probability models to reduce computation time. ‘n.models = 1’ is equivalent to the highest probability model.
use of newdata that is a vector is now deprecated for predict.bas; newdata must be a dataframe or missing, in which case fitted values based on the dataframe used in fitting is used
factor levels are handled as in
glm for prediction when there may be only level of a factor in the newdata
bas.lmto agree with documentation
renormalizethat selects whether the Monte Carlo frequencies are used to estimate posterior model and marginal inclusion probabilities (default
renormalize = FALSE) or that marginal likelihoods time prior probabilities that are renormalized to sum to 1 are used. (the latter is the only option for the other methods); new slots for probne0.MCMC, probne0.RN, postprobs.RN and postprobs.MCMC.
bas.glmto omit missing data.
confint.coef.bas. See the help files for an example or the vignette.
bas.glmto implement Bayes Factors based on the likelihood ratio statistic’s distribution for GLMs.
A vignette has been added at long last! This illustrates several of the new features in
BAS such as
diagnostic()function for checking convergence of
basobjects created with
method = "MCMC""
- added phi1 function from Gordy (1998) confluent hypergeometric function of two variables also known as one of the Horn hypergeometric functions or Humbert's phi1 - added Jeffrey's prior on g - added the general tCCH prior and special cases of the hyper-g/n. - TODO check shrinkage functions for all
- new improved Laplace approximation for hypergeometric1F1 - added class basglm for predict - predict function now handles glm output - added dataframe option for newdata in predict.bas and predict.basglm - renamed coefficients in output to be 'mle' in bas.lm to be consistent across lm and glm versions so that predict methods can handle both cases. (This may lead to errors in other external code that expects object$ols or object$coefficients) - fixed bug with initprobs that did not include an intercept for bas.lm
- added thinning option for MCMC method for bas.lm - returned posterior expected shrinkage for bas.glm - added option for initprobs = "marg-eplogp" for using marginal SLR models to create starting probabilities or order variables especially for p > n case - added standalone function for hypergeometric1F1 using Cephes library and a Laplace approximation -Added class "BAS" so that predict and fitted functions (S3 methods) are not masked by functions in the BVS package: to do modify the rest of the S3 methods.
correct fitted values (broken in version 0.80)
shrinkage - changed
predict.bma to center newdata using the mean(X) - Added new Adaptive MCMC option (method = “AMCMC”) (this is not stable at this point)
allocated within code
column of ones for the intercept optionally included. - fixed help file for predict - added modelprior argument to bas.lm so that users may now use the beta-binomial prior distribution on model size in addition to the default uniform distribution - added functions uniform(), beta-binomial() and Bernoulli() to create model prior objects - added a vector of user specified initial probabilities as an option for argument initprobs in bas.lm and removed the separate argument user.prob