ungroup: An R package for efficient estimation of smooth distributions from coarsely binned data

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Marius D. Pascariu, Maciej J. Danko, Jonas Schöley, Silvia Rizzi
Journal/Conference Name J. Open Source Software
Paper Category
Paper Abstract ungroup is an open source software library written in the R programming language (R Core Team, 2018) that introduces a versatile method for ungrouping histograms (binned count data) assuming that counts are Poisson distributed and that the underlying sequence over a fine grid to be estimated is smooth. The method is based on the composite link model (Thompson & Baker, 1981) and estimation is achieved by maximizing a penalized likelihood (P. H. Eilers, 2007), which extends standard generalized linear models. The penalized composite link model (PCLM) implements the idea that observed counts, interpreted as realizations from Poisson distributions, are indirect observations of a finer (ungrouped) but latent sequence. This latent sequence represents the distribution of expected means on a fine resolution and has to be estimated from the aggregated data. Estimates are obtained by maximizing a penalized likelihood. This maximization is performed efficiently by a version of the iteratively re-weighted least-squares algorithm. Optimal values of the smoothing parameter are chosen by minimizing Bayesian or Akaike’s Information Criterion (Hastie & Tibshirani, 1990).
Date of publication 2018
Code Programming Language R

Copyright Researcher 2022