Institutional performance assessment remains a key challenge to a multitude of stakeholders. Existing indicators such as h-type indicators, g-type indicators, and many others do not reflect expertise of institutions that defines their research portfolio. The package offers functionality to compute and visualise two novel indices: the x-index and the xd-index. The x-index evaluates an institution's scholarly expertise within a specific discipline or field, while the xd-index provides a broader assessment of overall scholarly expertise considering an institution's publication pattern and strengths across coarse thematic areas. These indices offer a nuanced understanding of institutional research capabilities, aiding stakeholders in research management and resource allocation decisions. Lathabai, H.H., Nandy, A., and Singh, V.K. (2021) <doi:10.1007/s11192-021-04188-3>. Nandy, A., Lathabai, H.H., and Singh, V.K. (2023) <doi:10.5281/zenodo.8305585>. This package provides the h-, g-, x-, xd-indices, and their variants for use with standard format of Web of Science (WoS) scrapped datasets.

Extremely fast hashing of R objects using 'xxHash'.  R objects are hashed via
    the standard serialization mechanism in R.  Raw byte vectors and strings
    can be handled directly for compatibility with hashes created on 
    other systems.  This implementation is a wrapper around the 'xxHash' 'C'
    library which is available from <https://github.com/Cyan4973/xxHash>.

Tools to analyze sex differences in omics data for complex diseases. It includes functions for differential expression analysis using the 'limma' method <doi:10.1093/nar/gkv007>, interaction testing between sex and disease, pathway enrichment with 'clusterProfiler' <doi:10.1089/omi.2011.0118>, and gene regulatory network (GRN) construction and analysis using 'igraph'. The package enables a reproducible workflow from raw data processing to biological interpretation.

Fits archetypal analysis models, including Euclidean,
    probabilistic, kernel, and directional variants. Methods include classical
    archetypal analysis from Cutler and Breiman (1994)
    <doi:10.1080/00401706.1994.10485840>, PCHA and kernel variants from
    Mørup and Hansen (2012) <doi:10.1016/j.neucom.2011.06.033>,
    probabilistic archetypal analysis from Seth and Eugster (2016)
    <doi:10.1007/s10994-015-5498-8>, directional archetypal analysis from
    Olsen et al. (2022) <doi:10.3389/fnins.2022.911034>, AA++
    initialization from Mair and Sjölund (2023)
    <doi:10.48550/arXiv.2301.13748>, coreset-style initialization from
    Mair and Brefeld (2019)
    <https://proceedings.neurips.cc/paper_files/paper/2019/file/7f278ad602c7f47aa76d1bfc90f20263-Paper.pdf>,
    and adapted AIC from Suleman (2017)
    <doi:10.1109/FUZZ-IEEE.2017.8015385>. Provides initialization helpers,
    model selection paths, plotting methods, 'broom' methods, and a
    'tidymodels' recipe step.