Implements different inventory models, the bullwhip effect and other supply chain performance variables. Marchena Marlene (2010) <arXiv:1009.3977>.

R version of 'scperturb' tool for single-cell perturbation 
    analysis. Contains wrappers for performing E-statistics for Seurat objects. 
    More details on the method can be found in Peidli et al. (2023) 
    <doi:10.1101/2022.08.20.504663> and in Székely and Rizzo (2004).

Implementation of prediction and inference procedures for Synthetic Control methods using least square, lasso, ridge, or simplex-type constraints. Uncertainty is quantified with prediction intervals as developed in Cattaneo, Feng, and Titiunik (2021) <doi:10.1080/01621459.2021.1979561> for a single treated unit and in Cattaneo, Feng, Palomba, and Titiunik (2025) <doi:10.1162/rest_a_01588> for multiple treated units and staggered adoption. More details about the software implementation can be found in Cattaneo, Feng, Palomba, and Titiunik (2025) <doi:10.18637/jss.v113.i01>.

Reports markers list, differentially expressed genes, associated pathways, cell-type annotations, does batch correction and other related single cell analyses all wrapped within 'Seurat'.

Compute ploidy of single cells (or nuclei)
    based on single-cell (or single-nucleus)
    ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing)
    data <https://github.com/fumi-github/scPloidy>.

Add-on for the 'scan' package that creates plots 
  from single-case data frames ('scdf'). It includes functions for styling 
  single-case plots, adding phase-based lines to indicate various statistical 
  parameters, and predefined themes for presentations and publications. More 
  information and in depth examples can be found in the online book 
  "Analyzing Single-Case Data with R and 'scan" 
  Jürgen Wilbert (2025) <https://jazznbass.github.io/scan-Book/>.

Provide a workflow to jointly embed chromatin accessibility peaks and expressed genes into a shared low-dimensional space using paired single-cell ATAC-seq (scATAC-seq) and single-cell RNA-seq (scRNA-seq) data. It integrates regulatory relationships among peak-peak interactions (via 'Cicero'), peak-gene interactions (via Lasso, random forest, and XGBoost), and gene-gene interactions (via principal component regression). With the input of paired scATAC-seq and scRNA-seq data matrices, it assigns a low-dimensional feature vector to each gene and peak. Additionally, it supports the reconstruction of gene-gene network with low-dimensional projections (via epsilon-NN) and then the comparison of the networks of two conditions through manifold alignment implemented in 'scTenifoldNet'. See <doi:10.1093/bioinformatics/btaf483> for more details.