Zig-Zag Sampler (RZigZag)
Implements the Zig-Zag algorithm with subsampling and control variates (ZZ-CV) of (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> as applied to Bayesian logistic regression, as well as basic Zig-Zag for a Gaussian target distribution.

Functional Data Analysis for Density Functions by Transformation to a Hilbert Space (fdadensity)
An implementation of the methodology described in Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport.

Alpha-NOMINATE Ideal Point Estimator (anominate)
Fits ideal point model described in Carroll, Lewis, Lo, Poole and Rosenthal (2013), ‘The Structure of Utility in Models of Spatial Voting,’ American Journal of Political Science 57(4): 1008–1028, <doi:10.1111/ajps.12029>.

Testing for Equivalence and Noninferiority (EQUIVNONINF)
Making available in R the complete set of programs accompanying S. Wellek’s (2010) monograph ”Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition” (Chapman&Hall/CRC).

Multivariate Information Inductive Causation (miic)
We report an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data, genomic alterations during tumor development and co-evolving residues in protein structures. For more information you can refer to: Verny et al. Plos Comput Biol. (2017) <doi:10.1371/journal.pcbi.1005662>.

Statistical Tools for Covariance Analysis (CovTools)
Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.

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