Changes in version 0.9.13 - Particle filters now work with irregular and non-unit spaced time series data Changes in version 0.9.11 - Continuous time (ODE) models can now use workers for running chains in parallel with pmcmc Changes in version 0.9.9 - Basic inference now working with continuous time (ODE) models, via particle_filter and pmcmc Changes in version 0.9.2 - Support for adaptive proposals for deterministic models Changes in version 0.9.1 - Allow running a particle filter with multiple parameter sets and a single data set. - The nested field on the particle filter class has been split into two logical fields: has_multiple_parameters and has_multiple_data Changes in version 0.9.0 - Deprecated 'discrete' argument to parameters in favour of 'integer' - affects if2_parameter, pmcmc_parameter, pmcmc_varied_parameter, smc2_parameter Changes in version 0.8.4 - Compiled compare functions now supported in more places - particle_deterministic and multistage models (#177) Changes in version 0.8.3 - Overhaul mcstate::pmcmc_chains_* to always use a file for communication, making them easier to understand and more robust (#179) - New functions mcstate::pmcmc_chains_cleanup for removing files created by the above, and mcstate::pmcmc_chains_collect for automating collecting samples - New, simpler, approach to pmcmc parallelisation which shares as much code with the above. Changes in version 0.8.2 - Allow filtering of the pmcmc chains during running (dropping burnin and filtering) to reduce memory usage when collectin large trajectories - pmcmc no longer retains the initial parameter values Changes in version 0.8.1 - New argument to mcstate::particle_filter and mcstate::particle_deterministic, constant_log_likelihood which can be used to compute the probabilities of non-time series data (#185) Changes in version 0.8.0 - Rework the "nested" support; this now returns output in a different dimension order. Primarily this is an internal refactoring. - Allow use of multistage parameters with deterministic models, and with nested parameters. - Transform functions for multistage parameters now take info and not model as an argument, more in keeping with other functions. Changes in version 0.7.3 - Allow multistage parameters to work with the "deterministic" particle - New mcstate::particle_deterministic_state object for advanced use of the deterministic particle - Deterministic particle loses the run_many method Changes in version 0.7.2 - Multistage particle filters now cope with running data covering a subset of their stages - Drop support for chnging initial step via particle filter initial function for deterministic and nested filters Changes in version 0.7.1 - New helper function mcstate::particle_filter_initial for creating particle filter initial state functions from restart data. - Drop support for changing initial step via the particle filter initial function Changes in version 0.7.0 - Multi-stage particle filter implemented, allowing arbitrary changes to model structure during a particle filter run (#159) Changes in version 0.6.13 - Allow saving restart from the deterministic filter (#153) Changes in version 0.6.5 - Reduced overhead in parallel pmcmc with workers, and faster/less memory-hungry chain combination (#142) Changes in version 0.6.4 - Allow the particle filter to terminate early if we would not be interested in the result. This is useful for mcstate::pmcmc which can use it to stop calculating a likelood that would be rejected. Primarily useful when running with relatively low numbers of particles and a high variance in the estimator (#138) Changes in version 0.6.3 - Add support for running in "deterministic" mode with recent dust (#139) Changes in version 0.6.0 - Add an iterated filtering method via mcstate::if2 (#123) Changes in version 0.5.13 - New functions pmcmc_chains_prepare and pmcmc_chains_run which can be used to manually schedule chains over different computing resourcess (#129) Changes in version 0.5.12 - When rerun_every is specified, a new control parameter rerun_control can be used to make this stochastic rerun Changes in version 0.5.11 - The particle filter can now run entirely in compiled code if supported by the model. This may give a small performance gain, particularly on very simple models, or of the model has an expensive compare function (#118) Changes in version 0.5.9 - Add nested_step_ratio parameter to pmcmc_control for controlling the ratio of fixed:varied steps for nested pMCMC Changes in version 0.5.5 - New array helper mcstate::array_flatten for unshaping an array Changes in version 0.5.4 - Remove deprecated arguments to pmcmc (these were deprecated in 0.3.0) (#114) Changes in version 0.5.3 - Bugfix in index for nested particle filters. Changes in version 0.5.2 - Extend support of pmcmc to pmcmc_parameters_nested objects. Changes in version 0.5.1 - Added particle_filter_state_nested and extended particle_filter to handle pmcmc_parameters_nested objects. Changes in version 0.5.0 - Basic SMC^2 implementation (smc2()) as an alternative to pmcmc. This is very embryonic and the interface will change over future versions to support things like restarting and saving trajectories (#13) Changes in version 0.4.8 - Bug fixes in $proposal method of pmcmc_parameters_nested for discrete and bounded parameters. Changes in version 0.4.7 - Added helper methods mcstate::array_bind, mcstate::array_reshape and mcstate::array_drop to simplify some common array operations (#106) Changes in version 0.4.6 - Added pmcmc_varied_parameter for parameters that can vary between different populations. - Added pmcmc_parameters_nested to hold parameters that vary between populations (pmcmc_varied_parameter) and parameters that are the same (fixed) between populations (pmcmc_parameter). Changes in version 0.4.4 - Fix performance regression added in 0.4.3 Changes in version 0.4.3 - Support for incrementally running a particle filter (up to some point in the time series) and forking these partial runs; see the $begin_run method on the particle filter (#78) Changes in version 0.4.2 - Fix typo in sir_models.Rmd Changes in version 0.4.1 - New $fix() method on pmcmc_parameters objects for fixing the value for a subset of parameters before running with pmcmc (#98) Changes in version 0.4.0 - Compare functions no longer use (or accept) the prev_state argument and now use just the current model state. This requires that models compute things like "daily incidence" within model code but simplifies use with irregular time series (#94) Changes in version 0.3.4 - Support for "compiled compare functions", introduced in dust 0.6.1 (#92) Changes in version 0.3.1 - The particle filter can now return the entire model state at points during the run, with argument save_restart to $run() and method $restart_state() (#86) - The pmcmc can returned sample restart state using the save_restart argument to mcstate::pmcmc_control which can be used to restart the pMCMC part way along the time series (see vignette("restart")) Changes in version 0.3.0 - pmcmc is now controllable via a new mcstate::pmcmc_control object - pmcmc can run chains in parallel using callr, by specifying n_workers = n for n greater than 1. Changes in version 0.2.16 - pmcmc adds new rerun_every argument to rerun the particle filter unconditionally.