Description Details Author(s) References See Also

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>.

To learn more about the package, start with the vignettes:
`browseVignettes(package = "ungroup")`

\insertNoCite*ungroup

**Maintainer**: Marius D. Pascariu rpascariu@outlook.com (ORCID)

Authors:

Silvia Rizzi srizzi@health.sdu.dk

Jonas Schoeley (ORCID)

Maciej J. Danko Danko@demogr.mpg.de (ORCID)

Useful links:

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