Computational tools for meta-analysis of diagnostic accuracy test. Bootstrap-based computational methods of the confidence interval for AUC of summary ROC curve and some related AUC-based inference methods are available (Noma et al. (2021) <doi:10.1080/23737484.2021.1894408>).

The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.

Implementation of double machine learning (DML) algorithms in R, 
    based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning 
    in Partially Linear Endogenous Models" <arXiv:2101.12525> and Emmenegger and
    Buehlmann (2021) <arXiv:2108.13657> "Double Machine Learning for Partially 
    Linear Mixed-Effects Models with Repeated Measurements". 
    First part: our goal is to perform inference for the linear parameter in partially
    linear models with confounding variables.
    The standard DML estimator of the linear parameter has a two-stage least
    squares interpretation, which can lead to a large variance and overwide
    confidence intervals.
    We apply regularization to reduce the variance of the estimator,
    which produces narrower confidence intervals that are approximately valid.
    Nuisance terms can be flexibly estimated with machine learning algorithms.
    Second part: our goal is to estimate and perform inference for the linear 
    coefficient in a partially linear mixed-effects model
    with DML. Machine learning algorithms allows us to incorporate more
    complex interaction structures and high-dimensional variables.

Mixed model analysis for quantitative genetics
  with multi-trait responses and pedigree-based partitioning 
  of individual variation into a range of environmental and 
  genetic variance components for individual and 
  maternal effects. Method documented in dmmOverview.pdf; dmm is an 
  implementation of dispersion mean model described by
  Searle et al. (1992) "Variance Components", Wiley, NY.
  Dmm() can do 'MINQUE', 'bias-corrected-ML', and 'REML'  variance and 
  covariance component estimates.