bssm 2.0.2 (Release date: 2023-10-18)

• Switched to markdown NEWS with a plan to be more clear about the future changes in the package.
• Added more details to the ?bssm help page.
• Added more details to the ?bssm_prior help page.
• Added option to extract only hyperparameters in as_draws method. Also fixed a bug in as_draws which caused the it to ignore states argument.
• Added a default plot method for the run_mcmc output.
• Fixed the aliases of the main help page to accomodate changes in roxygen2.
• Removed explicit C++ version requirement as required by new CRAN policies.
• Removed magrittr dependency and switched to native pipe, leading to requirement for R 4.1.0+.
• Added Sys.setenv(“OMP_NUM_THREADS” = 2) to (partially) fix CRAN issues with parallelisation on Debian.

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.