
version 0.7.8
- 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- in spatial random effect factor, argument poly.df has been replaced by graph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


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
