Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.

Connector to the REST API of a 'Rock' R server, to perform operations 
  on a remote R server session, or administration tasks. See 'Rock' documentation at
  <https://rockdoc.obiba.org/>.

Provides helper functions for authenticating and retrieving data from your 'ODK-X Sync Endpoint'. This is an early release intended for testing and feedback.

Implements ROC (Receiver Operating Characteristic)–Optimizing 
    Binary Classifiers, supporting both linear and kernel models. Both model 
    types provide a variety of surrogate loss functions. In addition, linear 
    models offer multiple regularization penalties, whereas kernel models 
    support a range of kernel functions. Scalability for large datasets is 
    achieved through approximation-based options, which accelerate training 
    and make fitting feasible on large data. Utilities are provided for model 
    training, prediction, and cross-validation. The implementation builds on 
    the ROC-Optimizing Support Vector Machines. For more information, see 
    Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>, 
    presented in the ROC Analysis in AI Workshop (ROCAI-2004).

Efficient diffusing of content across function documentations. Sections, parameters or dot parameters are extracted from function documentations and turned into valid Rd character strings, which are ready to diffuse into the 'roxygen' comments of another function by inserting inline code.

A toolkit for analyzing classifier performance by using receiver
    operating characteristic (ROC) curves. Performance may be assessed on
    a single classifier or multiple ones simultaneously, making it suitable for
    comparisons. In addition, different metrics allow the evaluation of local
    performance when working within restricted ranges of sensitivity and
    specificity. For details on the different implementations, see
    McClish D. K. (1989) <doi:10.1177/0272989X8900900307>,
    Vivo J.-M., Franco M. and Vicari D. (2018) <doi:10.1007/S11634-017-0295-9>,
    Jiang Y., et al (1996) <doi:10.1148/radiology.201.3.8939225>,
    Franco M. and Vivo J.-M. (2021) <doi:10.3390/math9212826> and
    Carrington, André M., et al (2020) <doi: 10.1186/s12911-019-1014-6>.