NEWS
mcmcsae 0.8.0 (2025-09-12)
- support added for smooth term specification using mgcv::s()
- measurement error component mec() now also works for non-gaussian families
- fixed a bug introduced in v0.7.9 in internal dgC_is_tabMatrix function that
could affect results from models with RW2 factors
- fixed several other smaller bugs
- more input checks
- many tests added
mcmcsae 0.7.9 (2025-06-04)
- predict now also works for models with bym2 components
- improvements to conjugate gradients and constrained multivariate normal
sampling methods, which now also work in more cases (e.g. in case of
inferred AR1 parameter, local scale parameters or bym2 components)
- new argument add.eps.I of sampler_control to add a small multiple of the
identity matrix to the coefficients' posterior precision matrix to avoid
singularities. Both this option and the constrained multivariate normal
(cMVN) sampler allow faster sampling for large structured multilevel models
with many identifiability or other linear equality constraints.
- linear equality and inequality constraints should now be specified using
function set_constraints, which returns an environment including functions
to check whether a numeric vector obeys all constraints
- now imports R package collapse instead of matrixStats
- new argument weights of predict method for mcdraws object, which can be used
to pass population sizes of poststratification cells for the P-step of MRP
(Multilevel Regression and Poststratification)
- family-specific arguments should now be passed to thw family argument of
create_sampler of generate_data by means of the family functions f_gaussian,
f_binomial, f_negbinomial, f_poisson, f_multinomial, f_gamma
- fixed a bug in prediction with type="response" for multinomial family (thanks
to Sumonkanti Das for reporting)
- fixed a bug where generating data for a model with only random iid effects at
the data level failed
- spline factor component renamed to splines to avoid naming conflict with
stats::spline
- further (small) efficiency improvements
- more tests, documentation updates and further small bug fixes
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- in spatial random effect factor, argument poly.df has been replaced by graph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 derive.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 these parameters can be inferred by
assigning them uniform or truncated normal priors
- prediction for models with offsets did not always work correctly, especially
for Poisson family
- fixed a bug 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
mcmcsae 0.7.7 (2024-02-27)
- 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
mcmcsae 0.7.6 (2023-12-02)
- 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
mcmcsae 0.7.5 (2023-10-10)
- 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
mcmcsae 0.7.4 (2023-06-29)
- 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
mcmcsae 0.7.3 (2023-06-02)
- 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
mcmcsae 0.7.2 (2023-02-04)
- 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
mcmcsae 0.7.1 (2022-09-02)
- 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
mcmcsae 0.7.0 (2021-10-10)
- 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
mcmcsae 0.6.0 (2021-01-20)
- 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
mcmcsae 0.5.0 (2020-09-01)