Differential partial correlation identification with the ridge and the fusion penalties.

The automated clustering and quantification of the digital PCR
            data is based on the combination of 'DBSCAN' (Hahsler et al. (2019)
            <doi:10.18637/jss.v091.i01>) and 'c-means' (Bezdek et al. (1981) 
            <doi:10.1007/978-1-4757-0450-1>) algorithms.
            The analysis is independent of multiplexing geometry, dPCR system, 
            and input amount.
            The details about input data and parameters are available in the
            vignette.

Analysis, visualisation and simulation of digital polymerase chain
    reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). 
    Supports data formats of commercial systems (Bio-Rad QX100 and
    QX200; Fluidigm BioMark) and other systems.

A programmatic interface to Health Canada's Drug Product 
    Database (DPD) REST API for querying information about drugs 
    approved for use in Canada. More information on the DPD can be found
    in the API guide (<https://health-products.canada.ca/api/documentation/dpd-documentation-en.html>).

Gaussian mixture modeling of one- and two-dimensional data, provided in original or binned form, with an option to estimate the number of model components. The method uses Gaussian Mixture Models (GMM) with initial parameters determined by a dynamic programming algorithm, leading to stable and reproducible model fitting. For more details see Zyla, J., Szumala, K., Polanski, A., Polanska, J., & Marczyk, M. (2026) <doi:10.1016/j.jocs.2026.102811>.

The Directed Prediction Index ('DPI') is a causal discovery method
    for observational data designed to quantify the relative endogeneity
    of outcome (Y) versus predictor (X) variables in regression models.
    By comparing the coefficients of determination (R-squared)
    between the Y-as-outcome and X-as-outcome models
    while controlling for sufficient confounders and
    simulating k random covariates, it can quantify relative endogeneity,
    providing a necessary but insufficient condition for causal direction
    from a less endogenous variable (X) to a more endogenous variable (Y).
    Methodological details are provided at
    <https://psychbruce.github.io/DPI/>.
    This package also includes functions for data simulation and network
    analysis (correlation, partial correlation, and Bayesian Networks).