version 0.7.7 - fixed a bug introduced in mcmcsae 0.7.5 that affected the outcomes of the blocked Gibbs sampler for non-gaussian models with random effects defined at the data level - user-defined equality constraints on regression coefficients or random effects now work again for blocked Gibbs sampler - default prior for shape of gamma family has been changed from gamma(1, 1) to gamma(0.1, 0.1) - more sensible default start values for gamma shape parameter - renamed model component name bart to brt, to avoid name clash with main fitting function of package dbarts - prediction now also works for models with a BART model component by specifying keepTrees=TRUE in brt() - corrected and updated the still somewhat experimental SBC_test function for simulation-based calibration; it now also supports parallel computation version 0.7.6 - blocked Gibbs sampler now also works with nonzero prior means of regression effects - fully-blocked Gibbs sampler is now the default, and argument block has been moved to sampler_control - model components for fixed and random effects now also usable to model log-variance of gaussian multilevel models - added support for random effects for log mean of gamma sampling distribution - shape parameter now given default gamma(1, 1) prior in gamma multilevel models version 0.7.5 - added gamma regression (family = "gamma") - several improvements to the "softTMVN" truncated multivariate normal sampler - added a few more methods to class tabMatrix to prepare for Matrix 1.6.2 (thanks to Mikael Jagan) - removed a few obsolete arguments from exported functions - more consistent prior specification; normal priors can now be specified using function pr_normal; arguments b0, Q0 for prior mean and precision in several model components have been deprecated - function pr_fixed for specifying a degenerate prior can now be used in more places - global option setting function set_opts has been replaced by several control functions sampler_control and chol_control that can be used to pass computational options to various functions version 0.7.4 - replaced maptools in Suggests by sf for reading shape files; now both SpatialPolygonsDataframe (for backward compatibility) and sf spatial data frames are supported - updated documentation of spatial() (in help topic 'correlation') and added an example of a CAR spatial random effects model - fixed a bug so that conjugate gradients algorithm works again - added control functions to set computational options for create_sampler and setup_CG_sampler - updated a few unit tests to be compatible with upcoming Matrix 1.6.0 - small documentation and code improvements version 0.7.3 - improved handling of out-of-sample categories by predict method - further improvements to prepare for upcoming version of Matrix package (thanks to Mikael Jagan) - clean-up of create_TMVN_sampler, in which now the method for truncated multivariate normal sampling can be specified by means of a method function that allows to pass method-specific options - added HMC ZigZag TMVN sampler - fixed a bug in soft-TMVN sampler, which did not work in case of a sparse equalities constraint matrix - option to add a Bayesian Additive Regression Trees model component to the linear predictor through package dbarts version 0.7.2 - prediction for new data now handles out-of-sample random effects (at least for iid random effect terms), so that it becomes easier to account for cluster effects from cluster samples, say - several other small improvements to predict method - small fix in preparation for upcoming Matrix 1.5-4 (thanks to Mikael Jagan) - model_matrix: allow single-level factor/character variables if no contrasts are applied - bug fix: inequality constraints did not work in combination with blocked Gibbs sampler - some parts of truncated multivariate normal samplers have been converted to C++ (using Rcpp and RcppEigen) for better performance - argument sampler of computeDesignMatrix has been removed - to_draws_array can now also convert an mcdraws object (or a subset of components from it) to a draws_array object for further analysis using R package posterior version 0.7.1 - compute_WAIC can now run using multiple cores - predict method with option ppcheck=TRUE now also works in parallel - prepare for coercion deprecations in upcoming version of Matrix package version 0.7.0 - renamed class 'draws' to 'mcdraws' to avoid name clash with R package posterior - added function to_draws_array to convert a draws component to an object of class draws_array, as defined in R package posterior - support for multinomial family - support for Poisson family, approximately, in terms of negative binomial - it is now possible to use weights to specify irregularly spaced AR1 or RW1 correlation structures - initial support for conjugate gradient coefficient sampler - experimental function for simulation-based calibration version 0.6.0 - measurement in covariates model component mec() added - new function pr_gig to specify a Generalized Inverse Gaussian prior - new argument logJacobian for create_sampler to allow comparisons of information criteria between model fits based on different transformations - added function to set labels of draws component object - data is now second argument of create_sampler and generate_data functions, in line with many model fitting functions in R - generate_data gains argument linpred, which is convenient for generating both data and latent quantities of interest for area-level models - solved a bug in function split_iters - print.dc_summary now correctly handles max.lines argument - adapted to new version of Matrix package - more input checks and small code improvements version 0.5.0 - initial CRAN release