ROC graphs, sensitivity/specificity curves, lift charts,
  and precision/recall plots are popular examples of trade-off
  visualizations for specific pairs of performance measures. ROCR is a
  flexible tool for creating cutoff-parameterized 2D performance curves
  by freely combining two from over 25 performance measures (new
  performance measures can be added using a standard interface).
  Curves from different cross-validation or bootstrapping runs can be
  averaged by different methods, and standard deviations, standard
  errors or box plots can be used to visualize the variability across
  the runs. The parameterization can be visualized by printing cutoff
  values at the corresponding curve positions, or by coloring the
  curve according to cutoff. All components of a performance plot can
  be quickly adjusted using a flexible parameter dispatching
  mechanism. Despite its flexibility, ROCR is easy to use, with only
  three commands and reasonable default values for all optional
  parameters.

Provides tools for creating, manipulating and reading Research 
    Object Crates (RO-Crates), a lightweight approach to packaging research 
    data with structured metadata. Includes utilities for metadata generation,
    entity management, validation and reading existing RO-Crates following 
    the specification <https://w3id.org/ro/crate/1.2/>.

Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.

Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control).

Implements nonparametric and semiparametric methods for comparing two survival distributions under non-proportional hazards (non-PH). The methods are based on the Receiver Operating Characteristic (ROC) curve length (Bantis et al. (2021) <doi:10.1002/sim.8869>) and the overlap coefficient (OVL) (Franco-Pereira et al. (2021) <doi:10.1177/09622802211046386>), as well as a joint ROC length-OVL-based approach. These methods do not require prior knowledge of the underlying non-PH pattern and can accommodate right, left, and doubly censored data.