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1.4.4

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

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Overview

About
Maintained by Marius D. PascariuFirst published 2018-09-037 releasesCRAN page ↗GitHub ↗

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) <doi:10.1093/aje/kwv020>.

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Health

CRAN checks
13OK
Code health
Yes
Tests · ratio 0.11
not tracked
Coverage
100%
Documentation · exports
Yes
Vignettes
Yes
README
4
Dependencies · direct
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Dependencies

Declared dependencies
9 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.4.0
Imports (4)
LinkingTo (2)
Enhances (0)
none
Reverse dependencies
0direct
indirect (not tracked)

Nothing depends on this yet.

Code & Tests

Code Composition
R 1,748 (48%)Rd 1,576 (43%)Vignettes 145 (4%)C++ 115 (3%)Other 96 (3%)

Test coverage

Line coverage

Expression

Tests / Examples

Functions

46 15 exported

Complexity

3.3 avg / 28 max

Call network

46 nodes / 41 edges

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People & History

People (4)
Maintainer (1)
Author, Maintainer
Authors (4)
Author, Maintainer
Release timeline

7 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 1.4.4Latest
    2024-01-31 · current release · diff ↗
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • 1.4.2
    2021-06-28 · diff ↗
  • R
    R 4.1.0 released · 2021-05-18
  • 1.3.0
    2021-01-10 · diff ↗
  • R
    R 4.0.0 released · 2020-04-24
  • 1.1.5
    2019-12-11 · diff ↗
  • R
    R 3.6.0 released · 2019-04-26
  • 1.1.1
    2018-10-15 · diff ↗
  • 1.1.0
    2018-09-30 · diff ↗
  • 1.0.0
    2018-09-03
  • R
    R 3.5.0 released · 2018-04-23

Package metadata

First published
2018-09-03
Total releases
7 / 8 yrs
License
MIT + file LICENSE OSI
Download size
not tracked yet
Installed size
not tracked yet
With dependencies
not tracked yet