Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) <https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx>.

Many treatment effect estimators can be written as weighted outcomes.
    These weights have established use cases like checking covariate balancing via packages like 'cobalt'.
    This package takes the original estimator objects and outputs these outcome weights.
    It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>.
    This version is compatible with the 'grf' package and provides an internal implementation of Double Machine Learning.

Provides a random forest based implementation of the method
    described in Chapter 7.1.2 (Regression model based anomaly detection)
    of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as
    follows: Each numeric variable is regressed onto all other variables
    by a random forest. If the scaled absolute difference between observed
    value and out-of-bag prediction of the corresponding random forest is
    suspiciously large, then a value is considered an outlier. The package
    offers different options to replace such outliers, e.g. by realistic
    values found via predictive mean matching. Once the method is trained
    on a reference data, it can be applied to new data.

Ensemble functions for outlier/anomaly detection. There is a new ensemble method proposed using 
    Item Response Theory. Existing outlier ensemble methods from Schubert et al (2012) <doi:10.1137/1.9781611972825.90>, 
    Chiang et al (2017) <doi:10.1016/j.jal.2016.12.002> and Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549>
    are also included.

Sequential outlier identification for Gaussian mixture models using
    the distribution of Mahalanobis distances. The optimal number
    of outliers is chosen based on the dissimilarity between the theoretical and
    observed distributions of the scaled squared sample Mahalanobis distances.
    Also includes an extension for Gaussian linear cluster-weighted models using
    the distribution of studentized residuals. 
    Doherty, McNicholas, and White (2025) <doi:10.48550/arXiv.2505.11668>.