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.lm and 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
Added contrast=NULL argument to bas.lm and bas.glm so that non-NULL contrasts do not trigger warning in model.matrix as of R 3.6.0. Bug #44
Added check for sample size equal to zero due to subsetting or missing data Bug #37
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 bas.lm, adding 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 pivot=TRUE in 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 size.
Added option force.heredity=TRUEto force lower order terms to be included if higher order terms are present (hierarchical constraint) for method='MCMC' and method='BAS' with bas.lm and bas.glm. Updated Vignette to illustrate. enhancement #19. Checks to see if parents are included using include.always pass issue #26.
Added option drop.always.included to image.bas so that variables that are always included may be excluded from the image. By default all are shown enhancement #23
Added option drop.always.included and subset to 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
update fitted.bas to use predict so that code covers both GLM and LM cases with type='link' or type='response'
Updates to package for CII Best Practices Badge certification
Added Code Coverage support and more extensive tests using test_that.
fixed issue #36 Errors in prior = “ZS-null” when R2 is not finite or out of range due to model being not full rank. Change in gexpectations function in file bayesreg.c
fixed issue #35 for method="MCMC+BAS" in bas.glm in glm_mcmcbas.c when no values are provided for MCMC.iterations or n.models and defaults are used. Added unit test in test-bas-glm.R
fixed issue #34 for bas.glm where variables in include.always had marginal inclusion probabilities that were incorrect. Added unit test in test-bas-glm.R
fixed issue #33 for Jeffreys prior where marginal inclusion probabilities were not renormalized after dropping intercept model
fixed issue #32 to allow vectorization for phi1 function in R/cch.R and added unit test to “tests/testthat/test-special-functions.R”
fixed issue #31 to coerce g to be a REAL for g.prior prior and IC.prior in bas.glm; added unit-test “tests/testthat/test-bas-glm.R”
fixed issue #30 added n as hyperparameter if NULL and coerced to be a REAL for intrinsic prior in bas.glm; added unit-test
fixed issue #29 added n as hyperparameter if NULL and coerced to be a REAL for beta.prime prior in bas.glm; added unit-test
fixed issue #28 fixed length of MCMC estimates of marginal inclusion probabilities; added unit-test
fixed issue #27 where expected shrinkage with the JZS prior was greater than 1. Added unit test.
fixed output 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. include.always and force.heredity=TRUE can now be used together with method="BAS".
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 image.
coerced predicted values to be a vector under BMA (was a matrix)
fixed size with using method=deterministic in bas.glm (was not updated)
fixed problem in confint with horizontal=TRUE when intervals are point mass at zero.
suppress warning when sampling probabilities are 1 or 0 and the number of models is decremented
Issue #25
changed 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.names to extract variable names in the highest probability model, median probability model, and best probability model for objects created by predict.predict.basglm which had that type = "link" was the default for prediction issue #18add na.action for handling NA’s for predict methods issue #10
added 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.
fixed problem if there is only one model for image function;
github issue #11
fixed error in bas.lm with non-equal weights where R2 was incorrect. issue #17 ## Deprecated
deprecate the predict argument in predict.bas, predict.basglm and internal functions as it is not utilized
confint.coef.bas when parm is a character stringBayes.outlier if prior probability of no outliers is providedfixed 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
updated print.bas to reflect changes in print.lm
Added Bayes.outlier function to calculate posterior probabilities of outliers using the method from Chaloner & Brant for linear models.
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.
Priors ZS-full for bas.lm is planned to be deprecated.
replaced math functions to use portable C code from Rmath and consolidated header files
Fixed unprotected ANS in C code in glm_sampleworep.c and sampleworep.c after call to PutRNGstate and possible stack imbalance in glm_mcmc.
Fixed problem with predict for estimator=BPM when newdata was one row
Fixed non-conformable error with predict when new data was from a dataframe with one row.
Fixed problem with missing weights for prediction using the median probability model with no new data.
Extract coefficient summaries, credible intervals and plots for the HPM and 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 lm or glm for prediction when there may be only level of a factor in the newdata
fixed issue for prediction when newdata has just one row
fixed missing id in plot.bas for which=3
bas.lm to agree with documentationrenormalize that 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.coefficients function.bas.lm and bas.glmna.action for bas.lm and bas.glm to omit missing data.confint.pred.bas or confint.coef.bas. See the help files for an example or the vignette.se.fit option in predict.basglm.testBF as a betaprior option for bas.glm to 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
confint.pred.bas()confint.coef.bas()predict.bas()type to specify estimator in fitted.bas and replaced with estimator so that predict() and fitted() are compatible with other S3 methods.bas to avoid NAMESPACE conflicts with other librariesfitted.bas or predict.basbas.glmdiagnostic() function for checking convergence of bas objects created with method = "MCMC""plot.bas that appears with Sweavecoef.bma when there is just one predictorbas rather than bma to avoid name conflicts with other packages- added weights for linear models
- switched LINPACK calls in bayesreg to LAPACK finally should be
faster
- fixed bug in intercept calculation for glms
- fixed inclusion probabilities to be a vector in the global EB
methods for linear models- added intrinsic prior for GLMs
- fixed problems for linear models for p > n and R2 not correct- 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.- added bas.glm for model averaging/section using mixture of g-priors for
GLMs. Currently limited to Logistic Regression
- added Poisson family for glm.fit- cleaned up MCMC method code- removed internal print statements in bayesglm.c
- Bug fixes in AMCMC algorithm- fixed glm-fit.R so that hyperparameter for BIC is numeric- added new AMCMC algorithm- bug fix in bayes.glm- added C routines for fitting glms- fixed problem with duplicate models if n.models was > 2^(p-1) byrestricting n.models
- save original X as part of object so that fitted.bma gives thecorrect fitted values (broken in version 0.80)
- Added `hypergeometric2F1` function that is callable by R
- centered X's in bas.lm so that the intercept has the correctshrinkage - changed predict.bma to center newdata using the mean(X) - Added new Adaptive MCMC option (method = “AMCMC”) (this is not stable at this point)
-Allowed pruning of model tree to eliminate rejected models- Added MCMC option to create starting values for BAS (`method = "MCMC+BAS"`)-Cleaned up all .Call routines so that all objects are duplicated orallocated within code
- fixed ch2inv that prevented building on Windows in bayes glm_fit- fixed FORTRAN calls to use F77_NAME macro
- changed allocation of objects for .Call to prevent some objects from being overwritten. - fixed EB.global function to include prior probabilities on models
- fixed update function - fixed predict.bma to allow newdata to be a matrix or vector with thecolumn 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