merliseclyde/BAS: BAS Version 1.5.2

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.

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.

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