A spatially-aware cell clustering algorithm is provided with cluster significance assessment. It comprises four key modules: spatially-aware cell-gene co-embedding, cell clustering, signature gene identification, and cluster significant assessment. More details can be referred to Peng Xie, et al. (2025) <doi:10.1016/j.cell.2025.05.035>.

Generate cofeature (feature by sample) matrices. The package 
  utilizes ggplot2::geom_tile() to generate the matrix allowing for easy
  additions from the base matrix.

While individual calibrated radiocarbon dates can span several centuries, combining multiple dates together with any chronological constraints can make a chronology much more robust and precise. This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 <doi:10.11141/ia.13.2>. Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 <doi:10.1017/S0003598X00080534>; Nicholls and Jones 2001 <doi:10.1111/1467-9876.00250>). The paper accompanying this R package is Blaauw et al. 2024 <doi:10.1017/RDC.2024.56>.

A curated list of copepod-fish ecological interaction records.
    It contains the taxonomy of the copepod and the fish and the publication
    from which the information was obtained. This database contains only marine
    and brackish water fish species. It excludes fish species that inhabit only
    freshwater.

Provides tools for factor analysis in high-dimensional settings under
    copula-based factor models. It includes functions to simulate factor-model data
    with copula-distributed idiosyncratic errors (e.g., Clayton, Gumbel, Frank,
    Student t and Gaussian copulas) and to perform diagnostic tests such as the
    Kaiser-Meyer-Olkin measure and Bartlett's test of sphericity. Estimation routines
    include principal component based factor analysis, projected principal component
    analysis, and principal orthogonal complement thresholding for large covariance
    matrix estimation. The philosophy of the package is described in Guo G. (2023)
    <doi:10.1007/s00180-022-01270-z>.