Package: MultipleSystemsEstimation 1.3.2

MultipleSystemsEstimation: Methods for Multiple Systems Estimation

Methods for multiple systems estimation using Poisson log-linear models, with particular attention to sparse capture data, model selection and its associated uncertainty, and Bayesian thresholding of interaction effects. The package implements and extends methodology from Silverman (2020) <doi:10.1111/rssa.12505>, Chan, Silverman and Vincent (2021) <doi:10.1080/01621459.2019.1708748>, and Silverman, Chan and Vincent (2024) <doi:10.1007/s11222-023-10346-9>.

Authors:Bernard W. Silverman [aut, cre], Lax Chan [ctb], Kyle Vincent [ctb]

MultipleSystemsEstimation_1.3.2.tar.gz
MultipleSystemsEstimation_1.3.2.zip(r-4.7-any)MultipleSystemsEstimation_1.3.2.zip(r-4.6-any)MultipleSystemsEstimation_1.3.2.zip(r-4.5-any)
MultipleSystemsEstimation_1.3.2.tgz(r-4.6-any)MultipleSystemsEstimation_1.3.2.tgz(r-4.5-any)
MultipleSystemsEstimation_1.3.2.tar.gz(r-4.7-any)MultipleSystemsEstimation_1.3.2.tar.gz(r-4.6-any)
MultipleSystemsEstimation_1.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MultipleSystemsEstimation/json (API)

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

Bug tracker:https://github.com/bernardsilverman/multiplesystemsestimation/issues

Pkgdown/docs site:https://bernardsilverman.github.io

Datasets:
  • Artificial_3 - Artificial three-list data
  • Korea - Korean military-sexual-slavery data
  • Kosovo - Kosovo conflict data
  • Ned - Netherlands human-trafficking data
  • Ned_5 - Five-list version of the Netherlands data
  • NewOrl - Greater New Orleans human-trafficking data
  • NewOrl_5 - Five-list version of the Greater New Orleans data
  • UKdat - United Kingdom modern-slavery data
  • UKdat_5 - Five-list version of the United Kingdom data
  • Western - Western-site sex-trafficking data

On CRAN:

Conda:

5.18 score 9 exports 11 dependencies

Last updated from:0171b6377a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK162
source / vignettesOK349
linux-releaseOK148
macos-releaseOK187
macos-oldrelOK191
windows-develOK115
windows-releaseOK127
windows-oldrelOK137
wasm-releaseOK89

Exports:check_extended_MLEestimate_populationestimate_population_bayesthreshestimate_population_bicestimate_population_fixedestimate_population_stepwisefind_neighbour_hierarchiesget_hierarchical_modelstidy_lists

Dependencies:codalatticelpSolveMASSMatrixMatrixModelsmcmcMCMCpackquantregSparseMsurvival

Capture histories and hierarchical models
Capture histories | Encoding capture histories | Parents, children, ancestors and descendants | Hierarchical log-linear models | Describing a hierarchical model | Neighbouring hierarchies | Generating hierarchical models | Sparse and masked interactions | Implications for the log-linear model | Appendix: The number of hierarchical models | Reference
Rendered fromcaptures-and-models.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2026-10-13

A User Guide to MultipleSystemsEstimation
Introduction | Data format | Start with estimate_population() | Specifying log-linear models | BIC model selection | Which models are considered? | Bootstrap inference after BIC selection | Restricting model selection within the bootstrap | Sparse capture data | The stepwise method | Bootstrap inference after stepwise selection | Fitting a specified model | The Bayesian thresholding method | Which method should I use? | Looking inside the result | References
Rendered fromuser-guide.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2026-08-09

Bayesian-thresholding multiple systems estimation
Introduction | The Bayesian-threshold procedure | Bayesian thresholding | MCMC estimation | MCMC settings and posterior output | A basic analysis | Improper priors | Four-list interactions | Checking existence with improper interaction priors | Examples with improper priors | Summary | References
Rendered frombayesian-threshold.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2026-08-16

BIC model selection and bootstrap inference
Introduction | BIC model selection | Inspecting the fit | Choosing the interaction order | Bootstrap inference | Practical bootstrap inference | Reading the confidence limits | Choosing the retained model set | Summary | References
Rendered frombic-model-selection-and-bootstrap-inference.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2026-10-13

Reproducing the 2024 BIC bootstrap investigations
Introduction | Running the reproduction code | Applications and published results | Table 2: Kosovo | Table 3: Korea | Figure 1: Five-list UK data | Figure 2: Five-list New Orleans data | Further investigations from the 2024 paper | Table 4: Location of the bootstrap BIC minimum | Table 5: Degree-2 ranking for Kosovo | Figure 3: Degree-1 and degree-2 rankings | Figure 4: Rank of the bootstrap-optimal model | Table 6: Downhill model search | Summary | References
Rendered fromreproduce-2024-bic-bootstrap.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2026-10-13

Sparse data, stepwise model selection and the bootstrap
Introduction | Sparse capture data and extended maximum likelihood | Existence and identifiability | The stepwise model-selection procedure | Allowing higher-order interactions | Bootstrap inference | Applications and published results | New Orleans: Section 4.1 | Five-list New Orleans data | Western site data: Section 4.2 | Simulation studies | Comparison with the BIC approach: Section 3.4 | Choosing the threshold: Section 4.3 | Relation to the current package | References
Rendered fromsparse-data-and-stepwise.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2026-10-13