Format quantities of time or bytes into human-friendly strings.

Human names are complicated and nonstandard things. Humaniformat,
    which is based on Anthony Ettinger's 'humanparser' project (https://github.com/
    chovy/humanparser) provides functions for parsing human names, making a best-
    guess attempt to distinguish sub-components such as prefixes, suffixes, middle
    names and salutations.

Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling,
  in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) <doi:10.18564/jasss.3550>).
  The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) <doi:10.13140/2.1.2480.9923>).

Vapor pressure, relative humidity, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.

Low level spell checker and morphological analyzer based on the 
    famous 'hunspell' library <https://hunspell.github.io>. The package can analyze
    or check individual words as well as parse text, latex, html or xml documents.
    For a more user-friendly interface use the 'spelling' package which builds on
    this package to automate checking of files, documentation and vignettes in all
    common formats.

Contains various functions for data analysis, notably helpers and diagnostics for Bayesian modelling using Stan.

When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.