Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. 
    Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.

Functions for unconditional and conditional quantiles. These
    include methods for transformation-based quantile regression,
    quantile-based measures of location, scale and shape, methods for quantiles
    of discrete variables, quantile-based multiple imputation, restricted
    quantile regression, directional quantile classification, and quantile
	ratio regression.
	A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.

Provides three 'Quarto' website templates as an R project, which are commonly used by academics.
    Templates for personal websites and course/workshop websites are included, as well as a template with minimal content for customization.

A tool that can be customized to aid in the clean up of ecological data 
    collected using quadrats and can crop quadrats to ensure comparability between
    quadrats collected under different methodologies.

A range of quadratic forms are evaluated, using efficient methods.
 Unnecessary transposes are not performed.  Complex values are handled
 consistently.

A set of functions of increasing complexity allows users to
  (1) convert QuadKey-identified datasets, 
  based on 'Microsoft's Bing Maps Tile System', into Simple Features data frames,
  (2) transform Simple Features data frames into rasters, and 
  (3) process multiple 'Meta' ('Facebook') QuadKey-identified human mobility files
  directly into raster files.
  For more details, see D’Andrea et al. (2024) <doi:10.21105/joss.06500>.