Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.

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>.