Package: fastei 1.1.0

fastei: Methods for ''A Fast Alternative for the R x C Ecological Inference Case''

Estimates the probability matrix for the R×C Ecological Inference problem using the Expectation-Maximization Algorithm with four approximation methods for the E-Step, and an exact method as well. It also provides a bootstrap function to estimate the standard deviation of the estimated probabilities. In addition, it has functions that aggregate rows optimally to have more reliable estimates in cases of having few data points. For comparing the probability estimates of two groups, a Wald test routine is implemented. The library has data from the first round of the Chilean Presidential Election 2021 and can also generate synthetic election data. Methods described in Thraves, Charles; Ubilla, Pablo; Hermosilla, Daniel (2024) ''A Fast Ecological Inference Algorithm for the R×C case'' <doi:10.2139/ssrn.4832834>.

Authors:Charles Thraves [aut], Pablo Ubilla [aut], Daniel Hermosilla [aut, cre]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
fastei/json (API)

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

Bug tracker:https://github.com/danielhermosilla/ecological-inference-elections/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

ecological-inferenceelectionsinferencemcmc-methodsopenblascpp

6.35 score 7 stars 14 scripts 451 downloads 10 exports 2 dependencies

Last updated from:83642b005a. Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE169
linux-devel-x86_64NOTE199
source / vignettesOK264
linux-release-arm64NOTE171
linux-release-x86_64NOTE160
macos-release-arm64NOTE128
macos-release-x86_64NOTE215
macos-oldrel-arm64NOTE135
macos-oldrel-x86_64NOTE287
windows-develNOTE173
windows-releaseNOTE132
windows-oldrelNOTE135
wasm-releaseOK139

Exports:bootstrapeimget_agg_optget_agg_proxyget_eim_chilePCArun_emsave_eimsimulate_electionwaldtest

Dependencies:jsonliteRcpp

Using the covariate approach
Simulating a parametric election | Running the model | Reducing dimensionality with PCA
Rendered fromcovariates.Rmdusingknitr::rmarkdown

Last update: 2026-01-17
Started: 2026-01-17

Demonstration of the package usage
Estimate Voting Probabilities | Standard deviation estimates | Reduce Estimation Error using Group Aggregation | Test difference between estimates | Simulating Election Results
Rendered fromdemonstration.Rmdusingknitr::rmarkdown

Last update: 2025-05-20
Started: 2025-03-30