Changepoint detection algorithms for R are widespread but have
    different interfaces and reporting conventions. 
    This makes the comparative analysis of results difficult. 
    We solve this problem by providing a tidy, unified interface for several 
    different changepoint detection algorithms. 
    We also provide consistent numerical and graphical reporting leveraging 
    the 'broom' and 'ggplot2' packages.

There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS').
    It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments.
    We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible.
    All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'.

A common interface to specifying clustering models, in the
    same style as 'parsnip'. Creates unified interface across different
    functions and computational engines.

Provides an intuitive interface for working with the
    competing risk endpoints. The package wraps the 'cmprsk' package, and
    exports functions for univariate cumulative incidence estimates and
    competing risk regression. Methods follow those introduced in Fine and
    Gray (1999) <doi:10.1002/sim.7501>.

Provides convenience functions for common data modification
    and analysis tasks in communication research. This includes functions
    for univariate and bivariate data analysis, index generation and
    reliability computation, and intercoder reliability tests. All
    functions follow the style and syntax of the tidyverse, and are
    construed to perform their computations on multiple variables at once.
    Functions for univariate and bivariate data analysis comprise summary
    statistics for continuous and categorical variables, as well as
    several tests of bivariate association including effect sizes.
    Functions for data modification comprise index generation and
    automated reliability analysis of index variables. Functions for
    intercoder reliability comprise tests of several intercoder
    reliability estimates, including simple and mean pairwise percent
    agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN:
    9780761915454), and various Kappa coefficients (Brennan & Prediger
    1981 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi:
    10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).