The Directed Prediction Index ('DPI') is
    a quasi-causal inference (causal discovery) method for observational 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 can suggest a plausible (admissible) direction of influence
    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).

Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.

Data frame, tibble, or tbl objects are converted to data package objects using specific metadata labels (name, version, title, homepage, description). A data package object ('dpkg') can be written to disk as a 'parquet' file or released to a 'GitHub' repository. Data package objects can be read into R from online repositories and downloaded files are cached locally across R sessions.

Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.

A fast, consistent tool for working with data frame like
    objects, both in memory and out of memory.