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

This repository contains a 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).

Installation

Make sure you have the most recent version of R
Run the following code in your R console
```
install.packages("ungroup")
```

Updating to the latest version of `ungroup` package

You can track (and contribute to) the development of ungroup at https://github.com/mpascariu/ungroup. To install it:

Install the release version of devtools from CRAN with install.packages("devtools").
Make sure you have a working development environment.
- Windows: Install Rtools.
- Mac: Install Xcode from the Mac App Store.
- Linux: Install a compiler and various development libraries (details vary across different flavours of Linux).

Install the development version of ungroup.

devtools::install_github("mpascariu/ungroup")

Intro

Get started with ungroup by checking the vignette R browseVignettes(package = "ungroup")

Contributing

This software is an academic project. We welcome any issues and pull requests. * If ungroup is malfunctioning, please report the case by submitting an issue on GitHub. * If you wish to contribute, please submit a pull request following the guidelines in CONTRIBUTING.md.

References

Rizzi S, Gampe J and Eilers PHC. 2015. Efficient Estimation of Smooth Distributions From Coarsely Grouped Data. American Journal of Epidemiology, Volume 182, Issue 2, Pages 138-147.