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ungroup

ungroup: An R package for efficient estimation of smooth distributions from coarsely binned data - Published in JOSS (2018)

https://github.com/mpascariu/ungroup

Science Score: 93.0%

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    CITATION.cff file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 8 DOI reference(s) in README and JOSS metadata
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    Academic publication links
    Links to: joss.theoj.org
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    Institutional organization owner
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    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

distributions glm smoothing ungrouping

Scientific Fields

Engineering Computer Science - 40% confidence
Last synced: 6 months ago · JSON representation

Repository

Estimating Smooth Distributions from Coarsely Binned Data - R Package

Basic Info
  • Host: GitHub
  • Owner: mpascariu
  • License: other
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 7.86 MB
Statistics
  • Stars: 15
  • Watchers: 3
  • Forks: 10
  • Open Issues: 3
  • Releases: 0
Topics
distributions glm smoothing ungrouping
Created about 8 years ago · Last pushed about 2 years ago
Metadata Files
Readme Changelog Contributing License Code of conduct

README.md

Penalized Composite Link Model for Efficient Estimation of Smooth Distributions from Coarsely Binned Data

CRAN_Version codecov issues DOI

lifecycle license CRAN_Download_Badge1 CRAN_Download_Badge2

This repository contains a versatile method for ungrouping histograms (binned count data) assuming that counts are Poisson distributed and that the underlying sequence on a fine grid to be estimated is smooth. The method is based on the composite link model and estimation is achieved by maximizing a penalized likelihood. Smooth detailed sequences of counts and rates are so estimated from the binned counts. Ungrouping binned data can be desirable for many reasons: Bins can be too coarse to allow for accurate analysis; comparisons can be hindered when different grouping approaches are used in different histograms; and the last interval is often wide and open-ended and, thus, covers a lot of information in the tail area. Age-at-death distributions grouped in age classes and abridged life tables are examples of binned data. Because of modest assumptions, the approach is suitable for many demographic and epidemiological applications. For a detailed description of the method and applications see Rizzi et al. (2015).

Installation

  1. Make sure you have the most recent version of R
  2. Run the following code in your R console

R install.packages("ungroup")

Updating to the latest version of ungroup package

You can track (and contribute to) the development of ungroup at https://github.com/mpascariu/ungroup. To install it:

  1. Install the release version of devtools from CRAN with install.packages("devtools").

  2. Make sure you have a working development environment.

    • Windows: Install Rtools.
    • Mac: Install Xcode from the Mac App Store.
    • Linux: Install a compiler and various development libraries (details vary across different flavours of Linux).

  3. Install the development version of ungroup.

R devtools::install_github("mpascariu/ungroup")

Intro

Get started with ungroup by checking the vignette R browseVignettes(package = "ungroup")

Contributing

This software is an academic project. We welcome any issues and pull requests. * If ungroup is malfunctioning, please report the case by submitting an issue on GitHub. * If you wish to contribute, please submit a pull request following the guidelines in CONTRIBUTING.md.

References

Rizzi S, Gampe J and Eilers PHC. 2015. Efficient Estimation of Smooth Distributions From Coarsely Grouped Data. American Journal of Epidemiology, Volume 182, Issue 2, Pages 138-147.

Eilers PHC. 2007. Ill-posed problems with counts, the composite link model and penalized likelihood. Statistical Modelling, Volume 7, Issue 3, Pages 239-254.

Owner

  • Name: Marius D. Pascariu
  • Login: mpascariu
  • Kind: user
  • Location: Paris, France

R&D actuary. Statistical demographer. Data scientist. Interested in mortality modelling and forecasting.

JOSS Publication

ungroup: An R package for efficient estimation of smooth distributions from coarsely binned data
Published
September 20, 2018
DOI
Volume 3, Issue 29, Page 937
Authors
Marius D. Pascariu ORCID
Institute of Public Health, Center on Population Dynamics, University of Southern Denmark, Odense, Denmark
Maciej J. Dańko ORCID
Max Planck Institute for Demographic Research, Rostock, Germany
Jonas Schöley ORCID
Institute of Public Health, Center on Population Dynamics, University of Southern Denmark, Odense, Denmark
Silvia Rizzi
Institute of Public Health, Unit of Epidemiology Biostatistics and Biodemography, University of Southern Denmark, Odense, Denmark
Editor
Karthik Ram ORCID
Tags
composite link model GLM histogram binned data smoothing
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GitHub Events

Total
  • Watch event: 2
  • Fork event: 1
Last Year
  • Watch event: 2
  • Fork event: 1

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 91
  • Total Committers: 4
  • Avg Commits per committer: 22.75
  • Development Distribution Score (DDS): 0.198
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
mpascariu x****u@o****m 73
Marius D. Pascariu r****u@o****m 16
Tim Riffe t****e@g****m 1
Andreas Kiermeier a****r 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 10
  • Total pull requests: 6
  • Average time to close issues: 3 months
  • Average time to close pull requests: 24 days
  • Total issue authors: 4
  • Total pull request authors: 4
  • Average comments per issue: 2.4
  • Average comments per pull request: 2.33
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • timriffe (6)
  • StaffanBetner (2)
  • mpascariu (1)
  • ChristK (1)
Pull Request Authors
  • mpascariu (3)
  • timriffe (1)
  • andreaskiermeier (1)
  • jschoeley (1)
Top Labels
Issue Labels
bug (2) enhancement (2)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 424 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 7
  • Total maintainers: 1
cran.r-project.org: ungroup

Penalized Composite Link Model for Efficient Estimation of Smooth Distributions from Coarsely Binned Data

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 424 Last month
Rankings
Forks count: 7.3%
Stargazers count: 14.6%
Dependent repos count: 19.1%
Average: 20.4%
Dependent packages count: 28.6%
Downloads: 32.1%
Maintainers (1)
rpascariu@outlook.com
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.4.0 depends
  • Matrix * imports
  • Rcpp >= 0.12.0 imports
  • Rdpack >= 0.8 imports
  • pbapply >= 1.3 imports
  • MortalityLaws >= 1.5.0 suggests
  • knitr >= 1.20 suggests
  • rmarkdown >= 1.10 suggests
  • testthat >= 2.0.0 suggests