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- global option setting function set_opts has been replaced by several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