Package: lphom 0.3.5-5

Jose M. Pavía

lphom: Ecological Inference by Linear Programming under Homogeneity

Provides a bunch of algorithms based on linear programming for estimating, under the homogeneity hypothesis, RxC ecological contingency tables (or vote transition matrices) using mainly aggregate data (from voting units). References: Pavía and Romero (2022) <doi:10.1177/00491241221092725>. Pavía (2023) <doi:10.1007/s43545-023-00658-y>. Pavía and Romero (2024) <doi:10.1093/jrsssa/qnae013>. Pavía (2024) A local convergent ecological inference algorithm for RxC tables. Pavía and Penadés (2024). A bottom-up approach for ecological inference. Romero, Pavía, Martín and Romero (2020) <doi:10.1080/02664763.2020.1804842>. Acknowledgements: The authors wish to thank Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257) and Ministerio de Economía e Innovación (grant PID2021-128228NB-I00) for supporting this research.

Authors:Jose M. Pavía [aut, cre], Rafael Romero [aut]

lphom_0.3.5-5.tar.gz
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lphom_0.3.5-5.tgz(r-4.4-emscripten)lphom_0.3.5-5.tgz(r-4.3-emscripten)
lphom.pdf |lphom.html✨
lphom/json (API)
NEWS

# Install 'lphom' in R:
install.packages('lphom', repos = 'https://cloud.r-project.org')
Datasets:
  • France2017D - 2017 French Presidential Election. Department official results.
  • France2017P - 2017 French Presidential Election. Regional provisional results.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 214 downloads 15 exports 1 dependencies

Last updated 1 years agofrom:1d5cd108bf. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 29 2025
R-4.5-linuxOKMar 29 2025
R-4.4-linuxOKMar 29 2025

Exports:adjust2integersconfidence_intervals_pjkerror_lphomlclphomlp_apriorilphomlphom_duallphom_jointnslphomnslphom_dualnslphom_jointrslphomtslphomtslphom_dualtslphom_joint

Dependencies:lpSolve

Citation

To cite lphom in publications use:

Pavía, Jose M. and Romero, Rafael (2022). Improving estimates accuracy of voter transitions. Two new algorithms for ecological inference based on linear programming. Sociological Methods & Research, online available. URL = https://doi.org/10.1177/00491241221092725

Pavía Jose M. and Romero, Rafael (2024). Symmetry estimating R×C vote transfer matrices from aggregate data. Journal of the Royal Statistical Society, Series A – Statistics in Society, forthcoming. URL = https://doi.org/10.1093/jrsssa/qnae013

Romero, R, Pavia, JM, Martin, J and Romero G (2020). Assessing uncertainty of voter transitions estimated from aggregated data. Application to the 2017 French presidential election. Journal of Applied Statistics, 47(13-15), 2711-2736. URL = https://10.1080/02664763.2020.1804842

Corresponding BibTeX entries: 

  @Article{,
    title = {Improving estimates accuracy of voter transitions. Two new
      algorithms for ecological inference based on linear programming},
    author = {Jose M. Pavía and Rafael Romero},
    journal = {Sociological Methods & Research},
    year = {2022},
    doi = {10.1177/00491241221092725},
  }

  @Article{,
    title = {Symmetry estimating R×C vote transfer matrices from
      aggregate data},
    author = {Jose M. Pavía and Rafael Romero},
    journal = {Journal of the Royal Statistical Society, Series A –
      Statistics in Society},
    year = {2024},
    doi = {10.1093/jrsssa/qnae013},
  }

  @Article{,
    title = {Assessing uncertainty of voter transitions estimated from
      aggregated data. Application to the 2017 French presidential
      election},
    author = {Rafael Romero and Jose M. Pavía and Jorge Martín and
      Gerardo Romero},
    journal = {Journal of Applied Statistics},
    year = {2020},
    volume = {47},
    number = {13-15},
    pages = {2711--2736},
    doi = {10.1080/02664763.2020.1804842},
  }