MultipleSystemsEstimation
Population size estimation from multiple incomplete lists,
with particular attention to sparse data and model-selection
uncertainty.
The MultipleSystemsEstimation R package implements
Poisson log-linear methods for multiple systems estimation of population
size. Working from a table of counts of cases falling on a number of
lists and their possible overlaps, the aim is to estimate the size of
the dark figure — individuals appearing on none of the
lists.
Among other developments, the package addresses two important
challenges: sparse capture data, including combinations of lists with no
observed overlap, and the proper assessment of the uncertainty arising
from the selection of a model from a large range of possibilities.
Key features
- A range of alternative estimation methods:
BIC-based model selection, stepwise selection, Bayesian thresholding and
fixed-model estimation.
- Model-selection uncertainty: Bootstrap confidence
intervals that account for model selection, rather than conditioning on
the selected model.
- Sparse data: Methods for dealing properly with zero
capture counts and for testing the existence and identifiability of
estimates.
- Hierarchical models: Tools for generating,
exploring and comparing hierarchical log-linear models. The
implementation goes beyond the previously published papers in extending
all methods to handle higher-order interactions.
- Consistent interface: A common structure for
estimates, uncertainty intervals, fitted models and diagnostic
information.
Documentation
The User
Guide is the recommended starting point. It explains the
estimation methods, their application and the interpretation of
results.
Five companion vignettes provide further details of the methods and
their implementation.
References
- Chan, L., Silverman, B. W. and Vincent, K. (2021). Multiple systems
estimation for sparse capture data: inferential challenges When there
are nonoverlapping lists. Journal of the American Statistical
Association, 116(535), 1297–1306. https://doi.org/10.1080/01621459.2019.1708748
- Silverman, B. W. (2020). Multiple-systems analysis for the
quantification of modern slavery: classical and Bayesian approaches.
Journal of the Royal Statistical Society: Series A (Statistics in
Society), 183(3), 691–736. doi:10.1111/rssa.12505..
- Silverman, B. W., Chan, L. and Vincent, K. (2024). Bootstrapping
multiple systems estimates to account for model selection.
Statistics and Computing, 34, article 44. https://doi.org/10.1007/s11222-023-10346-9