Provide the 'CrossClustering' algorithm (Tellaroli et al.
    (2016) <doi:10.1371/journal.pone.0152333>), which is a partial
    clustering algorithm that combines the Ward's minimum variance and
    Complete Linkage algorithms, providing automatic estimation of a
    suitable number of clusters and identification of outlier elements.

Contains functions for the construction of carryover
        balanced crossover designs. In addition contains functions to
        check given designs for balance.

Analyzes spatial transcriptomic data using cells-by-genes and cell location matrices to find gene pairs that coordinate their expression between spatially adjacent cells. It enables quantitative analysis and graphical assessment of these cross-expression patterns. See Sarwar et al. (2025) <doi:10.1101/2024.09.17.613579> and <https://github.com/gillislab/CrossExpression/> for more details.

Provides a general cross-fitting engine for semiparametric estimation
    (e.g., double/debiased machine learning). Supports user-defined target
    functionals and directed acyclic graphs of nuisance learners with per-node
    training fold widths, target-specific evaluation windows, and
    fold-allocation modes ("overlap", "disjoint", "independence"). Returns
    either numeric estimates (mode = "estimate") or cross-fitted prediction
    functions (mode = "predict"), with configurable aggregation over panels
    and repetitions, reuse-aware caching, and failure isolation, making it
    well-suited for simulation studies and large benchmarks.

A local haplotyping visualization toolbox to capture major patterns 
    of co-inheritance between clusters of linked variants, whilst connecting findings 
    to phenotypic and demographic traits across individuals. 'crosshap' enables users 
    to explore and understand genomic variation across a trait-associated region. 
    For an example of successful local haplotype analysis, see Marsh et al. (2022) 
    <doi:10.1007/s00122-022-04045-8>.

Linear or nonlinear cross-lagged panel model can be built from input data. Users can choose the appropriate method from three methods for constructing nonlinear cross lagged models. These three methods include polynomial regression, generalized additive model and generalized linear mixed model.In addition, a function for determining linear relationships is provided. 
    Relevant knowledge of cross lagged models can be learned through the paper by Fredrik Falkenström (2024) <doi:10.1016/j.cpr.2024.102435> and the paper by A Gasparrini (2010) <doi:10.1002/sim.3940>.