It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.

Users may specify what fundamental qualities of a new study have 
             or have not changed in an attempt to reproduce or replicate an 
             original study. A comparison of the differences is visualized. 
             Visualization approach follows 'Patil', 'Peng', and 
             'Leek' (2016) <doi:10.1101/066803>.

Static analysis for R scientific data analysis code. Flags
    patterns that often correspond to hidden scientific commitments --
    silent error swallowing, smuggled defaults, label leakage in
    selection-stage code, magic-eps floors in 'BIC' formulas, and
    shadow-overwrite of sourced helpers. Designed for agentic coding
    workflows; high recall over precision; structured 'ANALYSIS_OK'
    waivers as the audit trail.

Tools for quantitative research in scientometrics and bibliometrics.
    This package provides routines for importing bibliographic data from
    Clarivate Web of Science (<https://www.webofscience.com/wos/>) and
    performing bibliometric analysis.

Omics data (e.g. transcriptomics, proteomics,
    metagenomics...)  offer a detailed and multi-dimensional perspective
    on the molecular components and interactions within complex biological
    (eco)systems.  Analyzing these data requires adapted procedures, which
    are implemented as steps according to the 'recipes' package.

Inter-rater reliability analysis for binary classification
    tasks involving two or more raters within a signal
    detection-theoretic framework. User-supplied rating data are
    standardised into a common long-format structure. The package
    automatically computes Cohen's kappa for two raters and
    Fleiss' kappa for multiple raters. When ground-truth labels
    are available, rater-specific hit rates, false-alarm rates,
    sensitivity, specificity, and decision thresholds are
    estimated from observed classification responses using
    standard signal detection-theoretic transformations
    (DeCarlo, 1998)
    <doi:10.1037/1082-989X.3.2.186>. The package implements the
    Strategic Convergence Index (SCI; Gianeselli, 2026)
    <doi:10.1177/00131644261417643>, defined as
    SCI = 1 - [Var(t_i) / Var_max], where Var(t_i) denotes the
    variance of rater-specific decision thresholds and Var_max
    denotes the reference variance under maximal threshold
    dispersion. SCI quantifies convergence in rater decision
    criteria beyond observed agreement alone and complements
    classical agreement coefficients by distinguishing agreement
    in observed categorical outcomes from convergence in latent
    decision thresholds under an explicit signal
    detection-theoretic model of categorical judgment. The
    package provides structured summaries and threshold-based
    diagnostics for applications in which similar agreement
    coefficients may reflect substantively different underlying
    decision criteria across raters.