The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
  was developed to facilitate reproducible and open coding, specifically
  geared towards qualitative research methods. Although it is a
  general-purpose toolkit, three specific applications have been
  implemented, specifically an interface to the 'rENA' package that
  implements Epistemic Network Analysis ('ENA'), means to process notes
  from Cognitive Interviews ('CIs'), and means to work with decentralized
  construct taxonomies ('DCTs'). The 'ROCK' and this 'rock' package are described
  in the ROCK book <https://rockbook.org> and more information, such as tutorials,
  is 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) <https://www.ine.pt/revstat/pdf/rs140101.pdf> 
    and Cai & Pepe (2004) <doi:10.1111/j.0006-341X.2004.00200.x>.