* Bayesian Graphical Lasso* (

**BayesianGLasso**)

Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) <doi:10.1214/12-BA729>.

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**Stochastic Gradient Markov Chain Monte Carlo****sgmcmc**)

Provides functions that performs popular stochastic gradient Markov chain Monte Carlo (SGMCMC) methods on user specified models. The required gradients are automatically calculated using ‘TensorFlow’ <https://…/>, an efficient library for numerical computation. This means only the log likelihood and log prior functions need to be specified. The methods implemented include stochastic gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian Monte Carlo (SGHMC), stochastic gradient Nose-Hoover thermostat (SGNHT) and their respective control variate versions for increased efficiency.

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**Subgroup Discovery and Bump Hunting****subgroup.discovery**)

Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper ‘Bump hunting in high-dimensional data’ by Jerome H. Friedman and Nicholas I. Fisher. <doi:10.1023/A:1008894516817> PRIM involves finding a set of ‘rules’ which combined imply unusually large (or small) values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high (low) values for the target variable. The regions are described by simple rules of the type if: condition-1 and … and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible. There are many problems where finding such regions is of considerable practical interest. Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.

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**Estimate (Generalized) Linear Mixed Models with Factor Structures****PLmixed**)

Utilizes the ‘lme4’ package and the optim() function from ‘stats’ to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628>. Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models.

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**A Monadic Pipeline System****rmonad**)

A monadic solution to pipeline analysis. All operations — and the errors, warnings and messages they emit — are merged into a directed graph. Infix binary operators mediate when values are stored, how exceptions are handled, and where pipelines branch and merge. The resulting structure may be queried for debugging or report generation. ‘rmonad’ complements, rather than competes with, non-monadic pipeline packages like ‘magrittr’ or ‘pipeR’.