This is a collection of R games and other funny stuff, such as the
    classic Mine sweeper and sliding puzzles.

Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, 
    protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' 
    from gene set over-expression analysis. Performs clustering of pathway based on similarity 
    of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) 
    when up and down regulated genes are known. The methods are described in a paper currently
    submitted by Orecchioni et al, 2020 in Nanoscale.

The FunCC algorithm allows to apply the FunCC algorithm to simultaneously cluster the rows and the columns of a data matrix whose inputs are functions.

Compute alpha and beta contributional diversity metrics,
    which is intended for linking taxonomic and functional microbiome
    data. See 'GitHub' repository for the tutorial:
    <https://github.com/gavinmdouglas/FuncDiv/wiki>. Citation: Gavin M.
    Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023)
    <doi:10.1093/bioinformatics/btac809>.

Provides functional control charts 
    for statistical process monitoring of functional data, 
    using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507>, 
    Centofanti et al. (2021) <doi:10.1080/00401706.2020.1753581>,
    and Capezza et al. (2024) <doi:10.1080/00401706.2024.2327346>.
    The package is thoroughly illustrated in the paper of 
    Capezza et al (2023) <doi:10.1080/00224065.2023.2219012>.

YACFP (Yet Another Convenience Function Package). get_age() is a fast & accurate tool for measuring fractional years between two dates. abbr_to_colClass() is a much more concise way of feeding many types to a colClass argument in a data reader. stale_package_check() tries to identify any library() calls to unused packages.

Statistical hypothesis testing methods for
 inferring model-free functional dependency using asymptotic
 chi-squared or exact distributions. Functional test
 statistics are asymmetric and functionally optimal, unique
 from other related statistics. Tests in this package reveal
 evidence for causality based on the causality-by-
 functionality principle. They include asymptotic functional
 chi-squared tests (Zhang & Song 2013) <doi:10.48550/arXiv.1311.2707>,
 an adapted functional chi-squared test (Kumar & Song 2022) 
 <doi:10.1093/bioinformatics/btac206>, 
 and an exact functional test (Zhong & Song 2019)
 <doi:10.1109/TCBB.2018.2809743> (Nguyen et al. 2020)
 <doi:10.24963/ijcai.2020/372>. The normalized functional
 chi-squared test was used by Best Performer 'NMSUSongLab'
 in HPN-DREAM (DREAM8) Breast Cancer Network Inference
 Challenges (Hill et al. 2016) <doi:10.1038/nmeth.3773>. A
 function index (Zhong & Song 2019)
 <doi:10.1186/s12920-019-0565-9> (Kumar et al. 2018)
 <doi:10.1109/BIBM.2018.8621502> derived from the
 functional test statistic offers a new effect size measure
 for the strength of functional dependency, a better
 alternative to conditional entropy in many aspects. For
 continuous data, these tests offer an advantage over
 regression analysis when a parametric functional form
 cannot be assumed; for categorical data, they provide a
 novel means to assess directional dependency not possible
 with symmetrical Pearson's chi-squared or Fisher's exact
 tests.