The backtest package provides facilities for exploring
        portfolio-based conjectures about financial instruments
        (stocks, bonds, swaps, options, et cetera).

Decomposition for differences-in-differences with variation in
    treatment timing from Goodman-Bacon (2018) <doi:10.3386/w25018>.

The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012)
 can be used to estimate the causal effect of a continuous exposure on a continuous outcome.
 This package provides an approximate sensitivity analysis of BAC with regards to the
 hyperparameter omega. BACprior also provides functions to guide the user in their choice
 of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).

Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.

Bindings to 'badgen' <https://www.npmjs.com/package/badgen> to
    generate beautiful 'svg' badges in R without internet access. Images can
    be converted to 'png' using the 'rsvg' package as shown in examples.

Query information and generate badge for using in README and GitHub Pages.

Waffle plots are rectangular pie charts that represent a quantity or abundances using
    colored squares or other symbol. This makes them better at transmitting information as the
    discrete number of squares is easier to read than the circular area of pie charts.
    While the original waffle charts were rectangular with 10 rows and columns, with a single square
    representing 1%, they are nowadays popular in various infographics to visualize any
    proportional ratios.

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.