BAS 1.7.3

BAS 1.7.3 introduces a new Independent Adaptive MCMC algorithm for bas.lm that can be used a proposal distribution for sampling models with replacement and estimation of posterior model probabilities via Importance sampling and Horvitz-Thomposon estimators and their Bayesian Finite Population estimators. See details in bas.lm with `method="AMCMC".

BAS_1.7.3.tar.gz

BAS 1.7.2

Updated package provides a new adaptive independent MCMC sampler that allows more accurate estimates of model probabilities and other quantities using the Horvitz-Thompson estimator and Bayesian analogs for finite population sampling

Full Changelog: v1.7.1...v1.7.2

BAS 1.7.1

Minor Improvements and Fixes

• Initialized vector se via memset and disp = 1.0 in fit_glm.c (issue #72)

• Initialized variables in hyp1f1.c from testthat (issue #75)

• Removed models that have zero prior probability in bas.lm and bas.glm (issue #74)

• Fixed error in bayesglm.fit to check arguments x or y for correct type before calling C and added unit test (issue #67)

BAS 1.6.6

New Features

• Added support for Gamma regression for bas.glm, with unit tests and
  example (Code contributed by @betsyberrson)

Minor Improvements and Fixes

• added error if supplied initial model for the bas.lm sampling methods "MCMC" and "MCMC+BAS" had prior probability zero.

• fixed printing problems as identified via checks

• fixed indexing error for bas.lm and method = "MCMC+BAS" as bas.lm using method = "MCMC+BAS" crashed with a segmentation fault if bestmodel is not NULL or the null model. GitHub issue #69

• fixed error in predict.bas with se.fit=TRUE if there is only one predictor. GitHub issue #68 reported by @AleCarminati
  added unit test to test-predict.R

• Fixed error in coef for bas.glm objects when using a betaprior of class
  IC, including AIC and BIC Github issue #65

• Fixed error when using Jeffreys prior in bas.glm with the
  include.always option and added unit test in test-bas-glm.R.
  Github issue #61

• Fixed error for extracting coefficients from the median probability model
  when a formula is passed as an object rather than a literal, and added
  a unit test to test-coefficients.R Github issues #39 and #56

v1.6.4

Latest release for CRAN

BAS version 1.6.2

Release for updates with R 4.2.0

Major change is improved behavior for CCH and related priors in bas.glm that use the phi1 function. Alternative formulations for computing the marginal likelihoods show add improved stability and eliminate/reduce NA and Inf in computations as reported in Issue #55

BAS version 1.6.0

Changes

• update Fortran code to be compliant with USE_FC_LEN_T for character strings

Bug Fixes

• fixed warning in src code for log_laplace_F21 which had an uninitialized variable
  leading to NaN being returned from R function hypergeometric2F1

BAS Version 1.5.5

This version of the package provides some minor bug fixes and addresses some memory issues identified in CRAN checks.

• Changed the default in bas.lm and bas.glm to force.heredity=FALSE (triggered by errors with Solaris).

• Modified prior probabilities to adjust for the number of variables always
  included when using include.always. Pull request #41 by Don van de Bergh. Issue #40

• 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

BAS Version 1.5.3

This release fixes errors identified on CRAN for fedora with clang (there are still remaining problems with debian/clang and solaris)

Bug Fixes

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.

BAS Version 1.5.2

BAS 1.5.2

Features

• Included an option pivot=TRUE in bas.lm to fit the models using a pivoted Cholesky decomposition to allow models that are rank-deficient. Enhancment #24 and Bug #21. Currently coefficients that are not-estimable are set to zero so that predict and other methods will work as before. With more testing and timing this may become the default; otherwise the default method without pivoting issues a warning if log marginals are NA. The vector rank is added to the output (see documenation 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 certification

• Added Code Coverage support and more extensive tests using test_that.

Bugs

• 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 renomalized 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 probabiliy 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.

Other

• 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