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.
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For attribution, please cite this work as
Walter, "The R Journal: The R Package smicd: Statistical Methods for Interval-Censored Data", The R Journal, 2021
BibTeX citation
@article{RJ-2021-045,
author = {Walter, Paul},
title = {The R Journal: The R Package smicd: Statistical Methods for Interval-Censored Data},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-045},
doi = {10.32614/RJ-2021-045},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {366-382}
}