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

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.

Paul Walter
2020-07-23

CRAN packages used

actuar, fitdistrplus, smicd, stats, MASS, survival, lme4, nlme, ordinal, laeken, mlmRev

CRAN Task Views implied by cited packages

Econometrics, Psychometrics, SocialSciences, Distributions, Environmetrics, OfficialStatistics, Finance, SpatioTemporal, Survival, ChemPhys, ClinicalTrials, Multivariate, NumericalMathematics, Robust, Spatial, TeachingStatistics

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Citation

For attribution, please cite this work as

Walter, "The R Journal: The R Package smicd: Statistical Methods for Interval-Censored Data", {The R Journal}, 2020

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 = {2020},
  note = {https://doi.org/10.32614/RJ-2021-045},
  doi = {10.32614/RJ-2021-045},
  volume = {13},
  issue = {1},
  issn = {2073-4859},
  pages = {396-412}
}