Covariance is of universal prevalence across various disciplines within statistics.
    We provide a rich collection of geometric and inferential tools for convenient analysis of
    covariance structures, topics including distance measures, mean covariance estimator,
    covariance hypothesis test for one-sample and two-sample cases, and covariance estimation.
    For an introduction to covariance in multivariate statistical analysis,
    see Schervish (1987) <doi:10.1214/ss/1177013111>.

Dissects a package environment or 'covr' coverage object in order to cross
    reference tested code with the lines that are evaluated, as well as linking
    those evaluated lines to the documentation that they are described within.
    Connecting these three pieces of information provides a mechanism of 
	linking tests to documented behaviors.

Implements a specific form of segmented linear regression
    with two independent variables. The visualization of that function looks 
    like a quarter segment of a cowbell giving the package its name. 
    The package has been specifically constructed for the case where minimum 
    and maximum value of the dependent and two independent variables 
    are known a prior, which is usually the case
    when those values are derived from Likert scales.

Provides various features that help with creating publication-quality figures
    with 'ggplot2', such as a set of themes, functions to align plots and arrange
    them into complex compound figures, and functions that make it easy to annotate
    plots and or mix plots with images. The package was originally written for
    internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package).
    It has also been used extensively in the book Fundamentals of Data
    Visualization.

Allows printing of character strings as messages/warnings/etc.
    with ASCII animals, including cats, cows, frogs, chickens, ghosts,
    and more.

Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) <doi:10.48550/arXiv.2206.02296>; Rava (2021) <https://escholarship.org/uc/item/8h1846gs>.