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:
mcstate_0.9.22.tar.gz
mcstate_0.9.22.zip(r-4.5)mcstate_0.9.22.zip(r-4.4)mcstate_0.9.22.zip(r-4.3)
mcstate_0.9.22.tgz(r-4.4-any)mcstate_0.9.22.tgz(r-4.3-any)
mcstate_0.9.22.tar.gz(r-4.5-noble)mcstate_0.9.22.tar.gz(r-4.4-noble)
mcstate_0.9.22.tgz(r-4.4-emscripten)mcstate_0.9.22.tgz(r-4.3-emscripten)
mcstate.pdf |mcstate.html✨
mcstate/json (API)
NEWS
| # 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
Last updated 4 months agofrom:3549d64ff9 (on master). Checks:OK: 1 NOTE: 5 ERROR: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 29 2024 |
| R-4.5-win | ERROR | Oct 29 2024 |
| R-4.5-linux | NOTE | Oct 29 2024 |
| R-4.4-win | NOTE | Oct 29 2024 |
| R-4.4-mac | NOTE | Oct 29 2024 |
| R-4.3-win | NOTE | Oct 29 2024 |
| R-4.3-mac | NOTE | Oct 29 2024 |
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:callrclicpp11crayondescdustfsgluehmslifecyclepkgbuildpkgconfigpkgloadprettyunitsprocessxprogresspsR6rlangrprojrootvctrswithr
Deterministic models
Rendered fromdeterministic.Rmdusingknitr::rmarkdownon Oct 29 2024.Last update: 2022-01-28
Started: 2021-08-09
Fitting a continuous-time model
Rendered fromcontinuous.Rmdusingknitr::rmarkdownon Oct 29 2024.Last update: 2023-09-12
Started: 2023-09-12
Inference with iterated filtering
Rendered fromif2.Rmdusingknitr::rmarkdownon Oct 29 2024.Last update: 2022-01-28
Started: 2021-05-19
Nested SIR Models
Rendered fromnested_sir_models.Rmdusingknitr::rmarkdownon Oct 29 2024.Last update: 2022-01-28
Started: 2021-02-24
Parallelisation of inference
Rendered fromparallelisation.Rmdusingknitr::rmarkdownon Oct 29 2024.Last update: 2022-01-28
Started: 2020-10-26
Restarting pMCMC
Rendered fromrestart.Rmdusingknitr::rmarkdownon Oct 29 2024.Last update: 2022-11-10
Started: 2021-01-08
SIR models with odin, dust and mcstate
Rendered fromsir_models.Rmdusingknitr::rmarkdownon Oct 29 2024.Last update: 2022-11-10
Started: 2020-10-26
Validation of SMC using a Kalman filter
Rendered fromkalman.Rmdusingknitr::rmarkdownon Oct 29 2024.Last update: 2022-11-10
Started: 2020-10-26
