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)