The 'abn' R package facilitates Bayesian network analysis, a
    probabilistic graphical model that derives from empirical data a
    directed acyclic graph (DAG). This DAG describes the dependency
    structure between random variables. The R package 'abn' provides
    routines to help determine optimal Bayesian network models for a given
    data set. These models are used to identify statistical dependencies
    in messy, complex data. Their additive formulation is equivalent to
    multivariate generalised linear modelling, including mixed models with
    independent and identically distributed (iid) random effects. The core
    functionality of the 'abn' package revolves around model selection,
    also known as structure discovery. It supports both exact and
    heuristic structure learning algorithms and does not restrict the data
    distribution of parent-child combinations, providing flexibility in
    model creation and analysis. The 'abn' package uses Laplace
    approximations for metric estimation and includes wrappers to the
    'INLA' package. It also employs 'JAGS' for data simulation purposes.
    For more resources and information, visit the 'abn' website.

Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.

An implementation of the Abnormal Blood Profile Score (ABPS,
    part of the Athlete Biological Passport program of the World Anti-Doping
    Agency), which combines several blood parameters into a single score in
    order to detect blood doping (Sottas et al. (2006)
    <doi:10.2202/1557-4679.1011>). The package also contains functions to
    calculate other scores used in anti-doping programs, such as the
    OFF-score (Gore et al. (2003) <http://www.haematologica.org/content/88/3/333>),
    as well as example data.