Package: mcstate 0.9.22

Rich FitzJohn

mcstate: Monte Carlo Methods for State Space Models

Implements Monte Carlo methods for state-space models such as 'SIR' models in epidemiology. Particle MCMC (pmcmc) and SMC2 methods are planned. This package is particularly designed to work with odin/dust models, but we will see how general it becomes.

Authors:Rich FitzJohn [aut, cre], Marc Baguelin [aut], Edward Knock [aut], Lilith Whittles [aut], John Lees [aut], Raphael Sonabend [aut], Imperial College of Science, Technology and Medicine [cph]

mcstate_0.9.22.tar.gz
mcstate_0.9.22.zip(r-4.7)mcstate_0.9.22.zip(r-4.6)mcstate_0.9.22.zip(r-4.5)
mcstate_0.9.22.tgz(r-4.6-any)mcstate_0.9.22.tgz(r-4.5-any)
mcstate_0.9.22.tar.gz(r-4.7-any)mcstate_0.9.22.tar.gz(r-4.6-any)
mcstate_0.9.22.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mcstate/json (API)

# Install 'mcstate' in R:
install.packages('mcstate', repos = c('https://mrc-ide.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mrc-ide/mcstate/issues

On CRAN:

Conda:

7.43 score 19 stars 1 packages 132 scripts 34 exports 23 dependencies

Last updated from:3549d64ff9 (on master). Checks:5 ERROR, 2 OK, 2 NOTE. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR235
source / vignettesOK301
linux-release-x86_64ERROR258
macos-release-arm64ERROR167
macos-oldrel-arm64NOTE158
windows-develERROR194
windows-releaseERROR255
windows-oldrelNOTE235
wasm-releaseOK109

Exports:adaptive_proposal_controlarray_bindarray_droparray_flattenarray_reshapeif2if2_controlif2_parameterif2_parametersif2_samplemultistage_epochmultistage_parametersparticle_deterministicparticle_filterparticle_filter_dataparticle_filter_initialpmcmcpmcmc_chains_cleanuppmcmc_chains_collectpmcmc_chains_preparepmcmc_chains_runpmcmc_combinepmcmc_controlpmcmc_parameterpmcmc_parameterspmcmc_parameters_nestedpmcmc_predictpmcmc_samplepmcmc_thinpmcmc_varied_parametersmc2smc2_controlsmc2_parametersmc2_parameters

Dependencies:callrclicpp11crayondescdustfsgluehmslifecycleotelpkgbuildpkgconfigpkgloadprettyunitsprocessxprogresspsR6rlangrprojrootvctrswithr

Fitting a continuous-time model
Using MCMC to infer parameters
Rendered fromcontinuous.Rmdusingknitr::rmarkdown

Last update: 2023-09-12
Started: 2023-09-12

Restarting pMCMC
Setup | All-in-one | Restarting
Rendered fromrestart.Rmdusingknitr::rmarkdown

Last update: 2022-11-10
Started: 2021-01-08

SIR models with odin, dust and mcstate
Stochastic SIR model definition | Inferring parameters with mcstate | Model data | Defining the comparison function | Inferring parameters | Using MCMC to infer parameters | Tuning the pMCMC | Running predictions | Fitting to multiple datastreams
Rendered fromsir_models.Rmdusingknitr::rmarkdown

Last update: 2022-11-10
Started: 2020-10-26

Validation of SMC using a Kalman filter
Rendered fromkalman.Rmdusingknitr::rmarkdown

Last update: 2022-11-10
Started: 2020-10-26

Deterministic models
Rendered fromdeterministic.Rmdusingknitr::rmarkdown

Last update: 2022-01-28
Started: 2021-08-09

Inference with iterated filtering
Setting up an IF2 run | Running the IF2 algorithm
Rendered fromif2.Rmdusingknitr::rmarkdown

Last update: 2022-01-28
Started: 2021-05-19

Nested SIR Models
Model data | Comparison, model and particle filter | Nested parameters | pMCMC and Visualisations
Rendered fromnested_sir_models.Rmdusingknitr::rmarkdown

Last update: 2022-01-28
Started: 2021-02-24

Parallelisation of inference
Within-model parallelism | Between-chain parallelism | Considerations
Rendered fromparallelisation.Rmdusingknitr::rmarkdown

Last update: 2022-01-28
Started: 2020-10-26

Readme and manuals

Help Manual

Help pageTopics
Adaptive proposal controladaptive_proposal_control
Bind arraysarray_bind
Drop specific array dimensionsarray_drop
Flatten array dimensionsarray_flatten
Rehape an array dimensionarray_reshape
Run iterated filtering (IF2 algorithm)if2 if2_sample
Control for IF2if2_control
Describe single IF2 parameterif2_parameter
if2_parametersif2_parameters
Multistage filter epochmultistage_epoch
Multistage filter parametersmultistage_parameters
Deterministic particle likelihoodparticle_deterministic
Deterministic particle stateparticle_deterministic_state
Particle filterparticle_filter
Prepare data for use with particle filterparticle_filter_data
Create restart initial stateparticle_filter_initial
Particle filter stateparticle_filter_state
Run a pmcmc samplerpmcmc
pMCMC with manual chain schedulingpmcmc_chains_cleanup pmcmc_chains_collect pmcmc_chains_prepare pmcmc_chains_run
Combine pmcmc samplespmcmc_combine
Control for the pmcmcpmcmc_control
Describe single pmcmc parameterpmcmc_parameter
pmcmc_parameterspmcmc_parameters
pmcmc_parameters_nestedpmcmc_parameters_nested
Run predictions from PMCMCpmcmc_predict
Thin a pmcmc chainpmcmc_sample pmcmc_thin
Describe varying pmcmc parameterpmcmc_varied_parameter
Run SMC^2smc2
Control for SMC2smc2_control
Describe single pmcmc parametersmc2_parameter
smc2_parameterssmc2_parameters