Implements recently developed projection 
    pursuit algorithms for finding optimal linear cluster
    separators. The clustering algorithms use optimal
    hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>;
    minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
    and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.

Calculates partial and semi-partial
        (part) correlations along with p-value.

A clinical decision support system for sub-symptom threshold
    aerobic exercise (SSTAE) prescription in adolescents with persistent
    post-concussion symptoms (PPCS). Implements an evidence-based protocol
    derived from a systematic review of seven studies (Li, 2026; <doi:10.17605/osf.io/kvuf6>),
    encoding safety screening, Buffalo Concussion Treadmill Test (BCTT)-guided
    heart rate prescription, session-level progress tracking, and evidence
    disclosure using the Grading of Recommendations, Assessment, Development
    and Evaluation (GRADE) framework into an open-source tool for athletic
    trainers and clinicians. Designed to support implementation in
    resource-limited settings where BCTT equipment may be unavailable.
    GRADE certainty of evidence: LOW. For clinician use only; not a
    substitute for clinical judgement.