Tools to perform model selection alongside estimation under Linear, Logistic, Negative binomial, Quantile, and Skew-Normal regression. Under the spike-and-slab method, a probability for each possible model is estimated with the posterior mean, credibility interval, and standard deviation of coefficients and parameters under the most probable model.

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.

Contains the functions to implement the methodology and considerations laid out by Marks et al. in the manuscript Measuring Abnormality in High Dimensional Spaces: Applications in Biomechanical Gait Analysis. As of 2/27/2018 this paper has been submitted and is under scientific review. Using high-dimensional datasets to measure a subject’s overall level of abnormality as compared to a reference population is often needed in outcomes research. Utilizing applications in instrumented gait analysis, that article demonstrates how using data that is inherently non-independent to measure overall abnormality may bias results. A methodology is introduced to address this bias to accurately measure overall abnormality in high dimensional spaces. While this methodology is in line with previous literature, it differs in two major ways. Advantageously, it can be applied to datasets in which the number of observations is less than the number of features/variables, and it can be abstracted to practically any number of domains or dimensions. After applying the proposed methodology to the original data, the researcher is left with a set of uncorrelated variables (i.e. principal components) with which overall abnormality can be measured without bias. Different considerations are discussed in that article in deciding the appropriate number of principal components to keep and the aggregate distance measure to utilize.