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

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>.

Package details

AuthorMarius 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>)
MaintainerMarius D. Pascariu <[email protected]>
URL https://github.com/mpascariu/ungroup
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ungroup documentation built on Oct. 15, 2018, 5:04 p.m.