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)
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* 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.