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panelPomp
View on CRAN: Click
here
Download and install panelPomp package within the R console
Install from CRAN:
install.packages("panelPomp")
Install from Github:
library("remotes")
install_github("cran/panelPomp") Install by package version:
library("remotes")
install_version("panelPomp", "1.7.0.0") Attach the package and use:
library("panelPomp")
Maintained by
Jesse Wheeler
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-03-29
Latest Update: 2025-05-09
Description:
Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" .
How to cite:
Jesse Wheeler (2023). panelPomp: Inference for Panel Partially Observed Markov Processes. R package version 1.7.0.0, https://cran.r-project.org/web/packages/panelPomp. Accessed 26 Aug. 2026.
Previous versions and publish date:
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Other R packages that panelPomp depends,
imports, suggests or enhances
Complete documentation for panelPomp
Functions, R codes and Examples using
the panelPomp R package
Some associated functions: as . contacts . get_dim . mif2 . panelGompertz . panelGompertzLikelihood . panelPomp-package . panelPomp . panelPomp_methods . panelRandomWalk . panel_loglik . panel_logmeanexp . params . pfilter . plot . pparams . simulate . unitlogLik . unitobjects . wQuotes .
Some associated R codes: aaa.R . contacts.R . generics.R . get_col_row.R . mif2.R . mif2_methods.R . package.R . panelGompertz.R . panelGompertzLikelihood.R . panelPomp.R . panelPomp_methods.R . panelRandomWalk.R . panel_loglik.R . panel_logmeanexp.R . params.R . pfilter.R . pfilter_methods.R . plot.R . simulate.R . Full panelPomp package functions and examples
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