NEWS.md
particle_deterministic
and multistage models (#177)mcstate::pmcmc_chains_*
to always use a file for communication, making them easier to understand and more robust (#179)mcstate::pmcmc_chains_cleanup
for removing files created by the above, and mcstate::pmcmc_chains_collect
for automating collecting samplesmcstate::particle_filter
and mcstate::particle_deterministic
, constant_log_likelihood
which can be used to compute the probabilities of non-time series data (#185)info
and not model
as an argument, more in keeping with other functions.mcstate::particle_deterministic_state
object for advanced use of the deterministic particlerun_many
methodmcstate::particle_filter_initial
for creating particle filter initial state functions from restart data.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)mcstate::if2
(#123)pmcmc_chains_prepare
and pmcmc_chains_run
which can be used to manually schedule chains over different computing resourcess (#129)rerun_every
is specified, a new control parameter rerun_control
can be used to make this stochastic rerunnested_step_ratio
parameter to pmcmc_control
for controlling the ratio of fixed:varied steps for nested pMCMCmcstate::array_flatten
for unshaping an arraypmcmc
(these were deprecated in 0.3.0) (#114)particle_filter_state_nested
and extended particle_filter
to handle pmcmc_parameters_nested
objects.$proposal
method of pmcmc_parameters_nested
for discrete and bounded parameters.mcstate::array_bind
, mcstate::array_reshape
and mcstate::array_drop
to simplify some common array operations (#106)pmcmc_varied_parameter
for parameters that can vary between different populations.pmcmc_parameters_nested
to hold parameters that vary between populations (pmcmc_varied_parameter
) and parameters that are the same (fixed) between populations (pmcmc_parameter
).$begin_run
method on the particle filter (#78)$fix()
method on pmcmc_parameters
objects for fixing the value for a subset of parameters before running with pmcmc
(#98)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)dust
0.6.1 (#92)save_restart
to $run()
and method $restart_state()
(#86)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")
)pmcmc
is now controllable via a new mcstate::pmcmc_control
objectpmcmc
can run chains in parallel using callr
, by specifying n_workers = n
for n
greater than 1.