Build friendly R packages that
    praise their users if they have done something
    good, or they just need it to feel better.

Curated color palettes drawn from India's natural beauty -
    Himalayan snow, Thar dunes, Kerala backwaters, Andaman reefs, Spiti's
    cold desert, Kashmir's autumn chinar, and more. Provides discrete and
    continuous palettes with first-class 'ggplot2' integration through
    scale_color_prakriti() and scale_fill_prakriti(), plus base graphics
    helpers for displaying palettes.

A novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) <doi:10.1186/s12859-022-05123-w>.

A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>.
 Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.

Provides functions for generating novel partially residual
    balanced multi-session designs. These designs arrange products over
    sessions and periods under partial balance restrictions and compute
    canonical efficiency factors for direct and residual (carryover)
    effects. For general background on PRBMS and related crossover design
    literature, see 
    Aggarwal and Jha (2006) <doi:10.1080/03610920600695824> and 
    Fardos et al. (2023) <doi:10.18576/jsap/120227>.