A testing workbench to evaluate tools that calculate precision-recall curves.
    Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.

Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth.

Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.

An implementation of the "Design Analysis" proposed by 
    Gelman and Carlin (2014) <doi:10.1177/1745691614551642>. It combines 
    the evaluation of Power-Analysis with other inferential-risks as 
    Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also
    Altoè et al. (2020) <doi:10.3389/fpsyg.2019.02893> and 
    Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.

Derives prediction rule ensembles (PREs). Largely follows the
    procedure for deriving PREs as described in Friedman & Popescu (2008; 
    <DOI:10.1214/07-AOAS148>), with adjustments and improvements. The 
    main function pre() derives prediction rule ensembles consisting of 
    rules and/or linear terms for continuous, binary, count, multinomial, 
    and multivariate continuous responses. Function gpe() derives 
    generalized prediction ensembles, consisting of rules, hinge and linear 
    functions of the predictor variables.