Dynamic path analysis with estimation of the corresponding direct, indirect, and total effects, based on Fosen et al., (2006) <doi:10.1007/s10985-006-9004-2>. The main outcome of interest is a counting process from survival analysis (or recurrent events) data. At each time of event, ordinary linear regression is used to estimate the relation between the covariates, while Aalen's additive hazard model is used for the regression of the counting process on the covariates.

Use dynamic programming method to solve l1 convex clustering with identical weights.

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.

The Directed Prediction Index ('DPI') is
    a quasi-causal inference method for cross-sectional data
    designed to quantify the relative endogeneity (relative dependence)
    of outcome (Y) versus predictor (X) variables in regression models.
    By comparing the proportion of variance explained (R-squared)
    between the Y-as-outcome model and the X-as-outcome model
    while controlling for a sufficient number of possible confounders,
    it suggests a plausible (admissible) direction of influence
    from a more exogenous variable (X) to a more endogenous variable (Y).
    Methodological details are provided at
    <https://psychbruce.github.io/DPI/>.