Penalized Composite Link Model for Efficient Estimation of Smooth Distributions from Coarsely Binned Data R package ungroup version 1.4.4 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>. Software Depends: R (>= 3.4.0) Imports: pbapply (>= 1.3), Rcpp (>= 0.12.0), Rdpack (>= 0.8), Matrix LinkingTo: Rcpp, RcppEigen Suggests: MortalityLaws (>= 1.5.0), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) Marius D. Pascariu <rpascariu@outlook.com> Comprehensive R Archive Network (CRAN) Marius D. Pascariu [aut, cre] (ORCID: <https://orcid.org/0000-0002-2568-6489>), Silvia Rizzi [aut], Jonas Schoeley [aut] (ORCID: <https://orcid.org/0000-0002-3340-8518>), Maciej J. Danko [aut] (ORCID: <https://orcid.org/0000-0002-7924-9022>) MIT + file LICENSE (https://CRAN.R-project.org/package=ungroup/LICENSE) 2024-01-31 application/tgz https://CRAN.R-project.org/package=ungroup doi:10.32614/CRAN.package.ungroup