bssm 2.0.1 (Release date: 2022-05-02)
==============
* Fixed weights to one in case of non-linear model with mcmc_type="approx".
* Adjusted tolerance of some testthat tests to comply with CRAN's MKL checks.
bssm 2.0.0 (Release date: 2021-11-26)
==============
* Added a progress bar for run_mcmc.
* Added a fitted method for extraction of summary statistics of posterior
predictive distribution p(y_t | y_1, ..., y_n) for t = 1, ..., n.
* Rewrote the summary method completely, which now returns data.frame. This
also resulted in some changes in order of the function arguments.
* The output of predict method is now a data frame with column weight
corresponding to the IS-weights in case of IS-MCMC. Previously resampling
was done internally, but now this is left for the user if needed
(i.e. for drawing state trajectories).
* The asymptotic_var and iact functions are now exported to users, and they
also contain alternative methods based on the posterior package.
* New function estimate_ess can be used to compute effective sample size
from weighted MCMC.
* Added compatibility with the posterior package by defining as_draws
method for converting run_mcmc output to draws_df object.
* New function check_diagnostics for quick glance of ESS and Rhat values.
* Large number of new tests, and improved documentation with added examples.
* Large number of internal tweaks so that the package complies with
goodpractices package and Ropensci statistical software standards.
bssm 1.1.7-1 (Release date: 2021-09-21)
==============
* Fixed an error in automatic tests due to lack of fixed RNG seed.
bssm 1.1.7 (Release date: 2021-09-20)
==============
* Added a function cpp_example_model which can be used to extract and
compile some non-linear and SDE models used in the examples and vignettes.
* Added as_draws method for run_mcmc output so samples can be analysed using
the posterior package.
* Added more examples.
* Fixed a tolerance of one MCMC test to pass the test on OSX as well.
* Fixed a bug in iterated extended Kalman smoothing which resulted incorrect
estimates.
bssm 1.1.6 (Release date: 2021-09-06)
==============
* Cleaned some codes and added lots of tests in line with pkgcheck tests.
* Fixed a bug in EKF-based particle filter which returned filtered estimates
also in place of one-step ahead predictions.
* Fixed a bug which caused an error in suggest_N for nlg_ssm.
* Fixed a bug which caused incorrect sampling of smoothing distribution for
ar1_lg model when predicting past or when using simulation smoother.
* Fixed a bug which caused an error when predicting past values in
multivariate time series case.
* Fixed log-likelihood computation for gamma model with non-constant shape
parameter when using (intermediate) Gaussian approximation.
* Fixed sampling of negative binomial distribution in predict method, which
used std::negative_binomial which converts non-integer phi to integer.
Sampling now uses Gamma-Poisson mixture for simulation.
bssm 1.1.5 (Release date: 2021-06-14)
==============
* Added explicit check for nsim > 0 in predict method as sample function
works with missing argument causing crypting warnings later.
* Updated drownings data until 2019 and changed the temperature variable
to an average over three stations.
* Improved checks for observations and distributions in model building.
bssm 1.1.4 (Release date: 2021-04-13)
==============
* Better documentation for SV model, and changed ordering of arguments to
emphasise the recommended parameterization.
* Fixed predict method for SV model.
* Removed parallelization in one example which failed on Solaris for some
unknown reason.
bssm 1.1.3-2 (Release date: 2021-02-24)
==============
* Fixed missing parenthesis causing compilation fail in case of no OpenMP
support.
* Added pandoc version >= 1.12.3 to system requirements.
* Restructured C++ classes so no R structures are present in OpenMP regions.
bssm 1.1.3-1 (Release date: 2021-02-22)
==============
* Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`.
* Fixed the state covariance estimates of IS-MCMC, approx-MCMC, and
Gaussian MCMC when output_type = "summary".
* Fixed memory leaks due to uninitialized variables due to aborted particle
filter.
* Fixed numerical issues of multivariate normal density for nonlinear
models.
* Removed dependency on R::lchoose for safer parallel code.
* Added vignette for SDE models.
* Updated citation information and streamlined the main vignette.
bssm 1.1.2 (Release date: 2021-02-08)
==============
* Changed the definition of D in ssm_ulg and ssm_ung, functions now accept
D as scalar or vector as
was originally intended.
* Fixed a segfault issue with parallel state sampling in general
ssm_ulg/mlg/ung/mng models caused by calls to R function inside parallel
region.
* Fixed a bug from version 1.0.0 in IS1 type sampling which actually lead
to IS2 type sampling.
* Fixed out-of-bounds error in IS3 sampling.
* Fixed weight computations for multivariate nonlinear models in case of
psi-APF in some border cases with non-standard H.
* Removed Armadillo bound checks for efficiency gains.
bssm 1.1.1 (Release date: 2021-01-22)
==============
* Added missing scaling for Gamma distribution in importance sampling
weights for added numerical robustness.
* Fixed sequential importance sampling for multivariate non-gaussian models.
* Fixed simulation smoother for multivariate Gaussian models.
bssm 1.1.0 (Release date: 2021-01-19)
==============
* Added function `suggest_N` which can be used to choose
suitable number of particles for IS-MCMC.
* Added function `post_correct` which can be used to update
previous approximate MCMC with IS-weights.
* Gamma priors are now supported in easy-to-use models such as `bsm_lg`.
* The adaptation of the proposal distribution now continues also after the
burn-in by default.
* Changed default MCMC type to typically most efficient and robust IS2.
* Renamed `nsim` argument to `particles` in most of the R functions (`nsim`
also works with a warning).
* Fixed a bug with bsm models with covariates, where all standard deviation
parameters were fixed. This resulted error within MCMC algorithms.
* Fixed a dimension drop bug in the predict method which caused error for
univariate models.
* Fixed some docs and added more examples.
* Fixed few typos in vignette (thanks Kyle Hussman)
* Reduced runtime of MCMC in growth model vignette as requested by CRAN.
bssm 1.0.1-1 (Release date: 2020-11-12)
==============
* Added an argument `future` for predict method which allows
predictions for current time points by supplying the original model
(e.g., for posterior predictive checks).
At the same time the argument name `future_model` was changed to `model`.
* Fixed a bug in summary.mcmc_run which resulted error when
trying to obtain summary for states only.
* Added a check for Kalman filter for a degenerate case where all
observational level and state level variances are zero.
* Renamed argument `n_threads` to `threads` for consistency
with `iter` and `burnin` arguments.
* Improved documentation, added examples.
* Added a vignette regarding psi-APF for non-linear models.
bssm 1.0.0 (Release date: 2020-06-09)
==============
Major update
* Major changes for model definitions, now model updating and priors
can be defined via R functions (non-linear and SDE models still rely on
C++ snippets).
* Added support for multivariate non-Gaussian models.
* Added support for gamma distributions.
* Added the function as.data.frame for mcmc output which converts the MCMC
samples to data.frame format for easier post-processing.
* Added truncated normal prior.
* Many argument names and model building functions have been changed for
clarity and consistency.
* Major overhaul of C++ internals which can bring minor efficiency gains
and smaller installation size.
* Allow zero as initial value for positive-constrained parameters of bsm
models.
* Small changes to summary method which can now return also only summaries
of the states.
* Fixed a bug in initializing run_mcmc for negative binomial model.
* Fixed a bug in phi-APF for non-linear models.
* Reimplemented predict method which now always produces data frame of
samples.
bssm 0.1.11 (Release date: 2020-02-25)
==============
* Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI,
as it seems to work better with noisy likelihood estimates.
* Print and summary methods for MCMC output are now coherent in their output.
bssm 0.1.10 (Release date: 2020-02-04)
==============
* Fixed missing weight update for IS-SPDK without OPENMP flag.
* Removed unused usage argument ... from expand_sample.
bssm 0.1.9 (Release date: 2020-01-27)
==============
* Fixed state sampling for PM-MCMC with SPDK.
* Added ts attribute for svm model.
* Corrected asymptotic variance for summary methods.
bssm 0.1.8-1 (Release date: 2019-12-20)
==============
* Tweaked tests in order to pass MKL case at CRAN.
bssm 0.1.8 (Release date: 2019-09-23)
==============
* Fixed a bug in predict method which prevented the method working in case
of ngssm models.
* Fixed a bug in predict method which threw an error due to dimension drop of
models with single state.
* Fixed issues with the vignette.
bssm 0.1.7 (Release date: 2019-03-19)
==============
* Fixed a bug in EKF smoother which resulted wrong smoothed state estimates
in case of partially missing multivariate observations. Thanks for Santeri
Karppinen for spotting the bug.
* Added twisted SMC based simulation smoothing algorithm for Gaussian models,
as an alternative to Kalman smoother based simulation.
bssm 0.1.6-1 (Release date: 2018-11-20)
==============
* Fixed wrong dimension declarations in pseudo-marginal MCMC and logLik
methods for SDE and ng_ar1 models.
* Added a missing Jacobian for ng_bsm and bsm models using IS-correction.
* Changed internal parameterization of ng_bsm and bsm models from
log(1+theta) to log(theta).
bssm 0.1.5 (Release date: 2018-05-23)
==============
* Fixed the Cholesky decomposition in filtering recursions of multivariate
models.
* as_gssm now works for multivariate Gaussian models of KFAS as well.
* Fixed several issues regarding partially missing observations in
multivariate models.
* Added the MASS package to Suggests as it is used in some unit tests.
* Added missing type argument to SDE MCMC call with delayed acceptance.
bssm 0.1.4-1 (Release date: 2018-02-04)
==============
* Fixed the use of uninitialized values in psi-filter from version 0.1.3.
bssm 0.1.4 (Release date: 2018-02-04)
==============
* MCMC output can now be defined with argument `type`. Instead of returning
joint posterior samples, run_mcmc can now return only marginal samples of
theta, or summary statistics of the states.
* Due to the above change, argument `sim_states` was removed from the
Gaussian MCMC methods.
* MCMC functions are now less memory intensive, especially with
`type="theta"`.
bssm 0.1.3 (Release date: 2018-01-07)
==============
* Streamlined the output of the print method for MCMC results.
* Fixed major bugs in predict method which caused wrong values for the
prediction intervals.
* Fixed some package dependencies.
* Sampling for standard deviation parameters of BSM and their non-Gaussian
counterparts is now done in logarithmic scale for slightly increased
efficiency.
* Added a new model class ar1 for univariate (possibly noisy) Gaussian AR(1)
processes.
* MCMC output now includes posterior predictive distribution of states for
one step ahead to the future.
bssm 0.1.2 (Release date: 2017-11-21)
==============
* API change for run_mcmc: All MCMC methods are now under the argument
method, instead of having separate arguments for delayed acceptance and IS
schemes.
* summary method for MCMC output now omits the computation of SE and ESS in
order to speed up the function.
* Added new model class lgg_ssm, which is a linear-Gaussian model defined
directly via C++ like non-linear ssm_nlg models. This allows more flexible
prior definitions and complex system matrix constructions.
* Added another new model class, ssm_sde, which is a model with continuous
state dynamics defined as SDE. These too are defined via couple
simple C++ functions.
* Added non-gaussian AR(1) model class.
* Added argument nsim for predict method, which allows multiple draws per
MCMC iteration.
* The noise multiplier matrices H and R in ssm_nlg models can now depend on
states.
bssm 0.1.1-1 (Release date: 2017-06-27)
==============
* Use byte compiler.
* Skip tests relying in certain numerical precision on CRAN.
bssm 0.1.1 (Release date: 2017-06-27)
==============
* Switched from C++11 PRNGs to sitmo.
* Fixed some portability issues in C++ codes.
bssm 0.1.0 (Release date: 2017-06-24)
==============
* Initial release.