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>.
Version: 1.4.4 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) Published: 2024-01-31 DOI: 10.32614/CRAN.package.ungroup Author: Marius D. Pascariu [aut, cre], Silvia Rizzi [aut], Jonas Schoeley [aut], Maciej J. Danko [aut] Maintainer: Marius D. Pascariu <rpascariu at outlook.com> BugReports: https://github.com/mpascariu/ungroup/issues License: MIT + file LICENSE URL: https://github.com/mpascariu/ungroup NeedsCompilation: yes Citation: ungroup citation info Materials: README NEWS CRAN checks: ungroup results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=ungroup to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4