NEWS

mcmcsae 0.7.8 (2024-12-11)

added bym2 model component
in spatial random effect factor, argument poly.df has been replaced by graph which now also accepts neighbours lists, and argument detect.constraints has been deprecated as it is no longer needed
AR1 random effect factors used to be limited to fixed autoregressive parameter values, whereas now uniform or truncated normal priors can be assigned
prediction for models with offsets did not always work correctly, especially for Poisson family
bug fix in blocked Gibbs sampler for models including a measurement model component
more control over Metropolis-Hastings proposal distributions for some parameters through new control arguments
small documentation updates
some code restructuring to facilitate future extensions
more tests

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

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

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

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

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

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

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

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