A mixed collection of useful and semi-useful diverse
    statistical functions, some of which may even be referenced in
    The R Primer book. See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall.

Provides tools for creating and issuing nicely-formatted
    text within R diagnostic messages and those messages given during
    warnings and errors. The formatting of the messages can be
    customized using templating features. Issues with singular and
    plural forms can be handled through specialized syntax.

Fits the MESSI, hard constraint, and unconstrained models in Boss 
    et al. (2023) <doi:10.48550/arXiv.2306.17347> for mediation analyses with external 
    summary-level information on the total effect.

For the purposes of teaching, it is often desirable to show 
    examples of working with messy data and how to clean it. This R package 
    creates messy data from clean, tidy data frames so that students have a 
    clean example to work towards.

Matching with string distance has never been easier! 'messy.cats' contains various functions that employ string distance tools in order to make data management easier for users working with categorical data. Categorical data, especially user inputted categorical data that often tends to be plagued by typos, can be difficult to work with. 'messy.cats' aims to provide functions that make cleaning categorical data simple and easy.

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 to be annotated 
  to express unspecified date components,
  approximate or uncertain date components, 
  date ranges, and sets of dates. 
  This is useful for describing and analysing temporal information,
  whether historical or recent, where date 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>.