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ungroup  

Penalized Composite Link Model for Efficient Estimation of Smooth Distributions from Coarsely Binned Data
View on CRAN: Click here


Download and install ungroup package within the R console
Install from CRAN:
install.packages("ungroup")

Install from Github:
library("remotes")
install_github("cran/ungroup")

Install by package version:
library("remotes")
install_version("ungroup", "1.4.4")



Attach the package and use:
library("ungroup")
Maintained by
Marius D. Pascariu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-09-03
Latest Update: 2024-01-31
Description:
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>.
How to cite:
Marius D. Pascariu (2018). ungroup: Penalized Composite Link Model for Efficient Estimation of Smooth Distributions from Coarsely Binned Data. R package version 1.4.4, https://cran.r-project.org/web/packages/ungroup. Accessed 06 Mar. 2026.
Previous versions and publish date:
1.0.0 (2018-09-03 12:10), 1.1.0 (2018-09-30 12:10), 1.1.1 (2018-10-15 15:10), 1.1.5 (2019-12-11 09:50), 1.3.0 (2021-01-10 16:40), 1.4.2 (2021-06-28 18:10)
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