The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
  was developed to facilitate reproducible and open coding, specifically
  geared towards qualitative research methods. It was developed to be both
  human- and machine-readable, in the spirit of MarkDown and 'YAML'. The idea is
  that this makes it relatively easy to write other functions and packages
  to process 'ROCK' files. The 'rock' package contains functions for basic
  coding and analysis, such as collecting and showing coded fragments and
  prettifying sources, as well as a number of advanced analyses such as the
  Qualitative Network Approach and Qualitative/Unified Exploration of State
  Transitions. The 'ROCK' and this 'rock' package are described in the ROCK
  book (Zörgő & Peters, 2022; <https://rockbook.org>), in Zörgő & Peters
  (2024) <doi:10.1080/21642850.2022.2119144> and Peters, Zörgő and van der
  Maas (2022) <doi:10.31234/osf.io/cvf52>, and more information and
  tutorials are available at <https://rock.science>.

A collection of functions for interpretation and presentation
    of regression analysis.  These functions are used
    to produce the statistics lectures in
    <https://pj.freefaculty.org/guides/>. Includes regression
    diagnostics, regression tables, and plots of interactions and
    "moderator" variables. The emphasis is on "mean-centered" and
    "residual-centered" predictors. The vignette 'rockchalk' offers a
    fairly comprehensive overview.  The vignette 'Rstyle' has advice
    about coding in R.  The package title 'rockchalk' refers to our
    school motto, 'Rock Chalk Jayhawk, Go K.U.'.

'R6' class interface for handling relational database connections using 'DBI' package as backend.
  The class allows handling of connections to e.g. PostgreSQL, MariaDB and SQLite.
  The purpose is having an intuitive object allowing straightforward handling of SQL databases.

A set of functions for receiver operating characteristic (ROC) curve 
    estimation and area under the curve (AUC) calculation.
    All functions are designed to work with aggregated data; 
    nevertheless, they can also handle raw samples. 
    In 'ROCket', we distinguish two types of ROC curve representations:
    1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) 
    are functions of a parameter (the score),
    2) functions - TPR is a function of FPR.
    There are several ROC curve estimation methods available. An introduction to 
    the mathematical background of the implemented methods (and much more) can be found in 
    de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004).