Fraser Iain Lewis [aut] (<https://orcid.org/0000-0003-4580-2712>),
Depends: nnet, lme4, Rgraphviz, R (>= 4.0.0)
Suggests: INLA, knitr, R.rsp, testthat, entropy, moments, boot, brglm
Description: Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables.
An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables.
'abn' provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data. The additive formulation of these models is equivalent to multivariate generalised linear modelling (including mixed models with iid random effects).
The usual term to describe this model selection process is structure discovery.
The core functionality is concerned with model selection - determining the most robust empirical model of data from interdependent variables. Laplace approximations are used to estimate goodness of fit metrics and model parameters, and wrappers are also included to the INLA package which can be obtained from <https://www.r-inla.org>.
The computing library JAGS <https://mcmc-jags.sourceforge.io> is used to simulate 'abn'-like data.
A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website <http://r-bayesian-networks.org>.

Author: Reinhard Furrer [cre, aut] (<https://orcid.org/0000-0002-6319-2332>),
Imports: methods, rjags, nnet, lme4, Rgraphviz, doParallel, foreach

Imports: methods, rjags, nnet, lme4, graph, Rgraphviz, doParallel,

Title: Measure a Subject's Abnormality with Respect to a Reference
Authors@R: person("Michael", "Marks", email = "mmarks13@gmail.com", role = c("aut", "cre"))
Description: 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.