The R Package smicd: Statistical Methods for Interval-Censored Data

Authors

• Paul Walter, Freie Universität Berlin

Date of this Version

6-2021

Document Type

Article

Citation

The R Journal (June 2021) 13(1); Editor: Dianne Cook

Comments

Copyright 2021, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

The package allows the use of two new statistical methods for the analysis of intervalcensored data: 1) direct estimation/prediction of statistical indicators and 2) linear (mixed) regression analysis. Direct estimation of statistical indicators, for instance, poverty and inequality indicators, is facilitated by a non parametric kernel density algorithm. The algorithm is able to account for weights in the estimation of statistical indicators. The standard errors of the statistical indicators are estimated with a non parametric bootstrap. Furthermore, the package offers statistical methods for the estimation of linear and linear mixed regression models with an interval-censored dependent variable, particularly random slope and random intercept models. Parameter estimates are obtained through a stochastic expectation-maximization algorithm. Standard errors are estimated using a non parametric bootstrap in the linear regression model and by a parametric bootstrap in the linear mixed regression model. To handle departures from the model assumptions, fixed (logarithmic) and data-driven (Box-Cox) transformations are incorporated into the algorithm