Dynamic model averaging for binary and continuous
        outcomes.

Functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) (<DOI:10.1016/0304-4076(80)90070-6>).

Functions for fitting a Bayesian model for grouping binary
    dissimilarity matrices in homogeneous clusters. Currently, it includes
    methods only for binary data (<doi:10.18637/jss.v100.i16>).

DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015).
  Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.
  Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>.
  Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs),
  a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008).
  Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common
  features from within standard diffusion models with the addition of superimposed controlled and automatic activation.
  The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural
  conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality
  to fit the model to observed data. Further details are provided in the following paper: 
  Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction 
  time and error rate data. Methods in Psychology, 100074. <doi:10.1016/j.metip.2021.100074>.

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.