The R Journal: accepted article

This article will be copy edited and may be changed before publication.

The R Package smicd: Statistical Methods for Interval-Censored Data PDF download
Paul Walter

Abstract The package allows the use of two new statistical methods for the analysis of interval censored 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.

Received: ; online 2021-06-08
CRAN packages: actuar, fitdistrplus, smicd, stats, MASS, survival, lme4, nlme, ordinal, laeken, mlmRev
CRAN Task Views implied by cited CRAN packages: Econometrics, Psychometrics, SocialSciences, Distributions, Environmetrics, OfficialStatistics, Finance, SpatioTemporal, Survival, ChemPhys, ClinicalTrials, Multivariate, NumericalMathematics, Robust, Spatial, TeachingStatistics

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Paul Walter},
  title = {{The R Package smicd: Statistical Methods for Interval-
          Censored Data}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-045},
  url = {}