Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). The algorithm implements a penalty term to minimize the differences between the total sub-annual flows and the annual flow. The method is described in Nguyen et al (2020) <DOI:10.1002/essoar.10504791.1>.

Functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses.  The response dimension reduction is theoretically founded in Yoo and Cook (2008) <doi:10.1016/j.csda.2008.07.029>. Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) <doi:10.1080/02331888.2017.1410152> and Yoo (2019) <doi:10.1016/j.jkss.2019.02.001>. The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace.

Functions provide comprehensive treatments for estimating, inferring, testing and model selecting in linear regression models with structural breaks. The tests, estimation methods, inference and information criteria implemented are discussed in Bai and Perron (1998) "Estimating and Testing Linear Models with Multiple Structural Changes" <doi:10.2307/2998540>.

Summarize multiple biomarker responses of aquatic organisms to
    contaminants using Cliff’s delta, as described in Pham & Sokolova (2023)
    <doi:10.1002/ieam.4676>.

Fit generalized linear models with binomial responses using  a median modified score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to median bias reduction. This method respects equivariance under reparameterizations for each parameter component and also solves the infinite estimates problem (data separation).