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>.
|Author||Marius D. Pascariu [aut, cre] (<https://orcid.org/0000-0002-2568-6489>), Silvia Rizzi [aut], Jonas Schoeley [aut] (<https://orcid.org/0000-0002-3340-8518>), Maciej J. Danko [aut] (<https://orcid.org/0000-0002-7924-9022>)|
|Maintainer||Marius D. Pascariu <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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