bssm 1.0.0 (Release date: -)
==============
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.