Contains a set of tools for constructing and coercing
  into and from the "mdate" class. 
  This date class implements ISO 8601-2:2019(E) and
  allows regular dates and times to be annotated
  to express unspecified date or time components,
  approximate or uncertain components,
  ranges, and sets of dates.
  The package therefore retains, represents, and reasons about data and time imprecision, 
  resolving to a single data/time only on demand.
  This is useful for describing and analysing temporal information,
  whether historical or recent, where date or time precision may vary.

Provides a flexible framework for estimating the variance-covariance matrix of estimated parameters. Estimation relies on unbiased estimating functions to compute the empirical sandwich variance. (i.e., M-estimation in the vein of Tsiatis et al. (2019) <doi:10.1201/9780429192692>.

User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015):
 - common effect and random effects meta-analysis;
 - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble);
 - three-level meta-analysis model;
 - generalised linear mixed model;
 - logistic regression with penalised likelihood for rare events;
 - Hartung-Knapp method for random effects model;
 - Kenward-Roger method for random effects model;
 - prediction interval and density of the prediction distribution;
 - expected proportion of comparable studies with clinically important benefit or harm;
 - statistical tests  for funnel plot asymmetry;
 - trim-and-fill method to evaluate bias in meta-analysis;
 - meta-regression;
 - cumulative meta-analysis and leave-one-out meta-analysis;
 - import data from 'RevMan 5';
 - produce forest plot summarising several (subgroup) meta-analyses.

Implement meta-analyses for simultaneously estimating individual means with shrinkage,
 isotonic regression and pretests. Include our original implementation of the isotonic regression via the pool-adjacent-violators algorithm (PAVA) algorithm.
 For the pretest estimator, the confidence interval for individual means are provided.
 Methodologies were published in 
 Taketomi et al. (2021) <doi:10.3390/axioms10040267>, Taketomi et al. (2022) <doi:10.3390/a15010026>, 
 Taketomi et al. (2023-) (under review).