Performs a test for second-order stationarity of time series based
    on unsystematic sub-samples.

Hubbell's Unified Neutral Theory of Biodiversity.

Calculates a Mahalanobis distance for every row of a set of
    outcome variables (Mahalanobis, 1936
    <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis
    distance is calculated using a conditional covariance matrix (i.e., a
    covariance matrix of the outcome variables after controlling for a set
    of predictors). Plotting the output of the cond_maha() function can
    help identify which elements of a profile are unusual after
    controlling for the predictors.

Historical voting data of the United Nations General Assembly. This
    includes votes for each country in each roll call, as well as descriptions and
    topic classifications for each vote.

This program realizes a universal estimation approach that accommodates
    multi-category variables and effect scales, making up for the deficiencies
    of the existing approaches when dealing with non-binary exposures and
    complex models. The estimation via bootstrapping can simultaneously provide results of
    causal mediation on risk difference (RD), odds ratio (OR) and risk ratio (RR) scales with tests of the effects' difference.
    The estimation is also applicable to many other settings, e.g., moderated mediation, inconsistent covariates,
    panel data, etc. The high flexibility and compatibility
    make it possible to apply for any type of model, greatly meeting the needs of
    current empirical researches.

We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.<DOI:10.1111/biom.13024> for more information.