* Analyze and Create Elegant Directed Acyclic Graphs* (

**ggdag**)

Tidy, analyze, and plot directed acyclic graphs (DAGs). ‘ggdag’ is built on top of ‘dagitty’, an R package that uses the ‘DAGitty’ web tool (<http://dagitty.net> ) for creating and analyzing DAGs. ‘ggdag’ makes it easy to tidy and plot ‘dagitty’ objects using ‘ggplot2’ and ‘ggraph’, as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.

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**Adaptable Regularized Hotelling’s T^2 Test for High-Dimensional Data****ARHT**)

Perform the Adaptable Regularized Hotelling’s T^2 test (ARHT) proposed by Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with various probabilistic alternative prior models. It contains a function to consistently estimate higher order moments of the population covariance spectral distribution using the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>). In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately with a given correlation matrix when the degrees of freedom are large.

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**Fast Adaptive Shrinkage/Thresholding Algorithm****fasta**)

A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.

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**Random Number Generation for Generalized Poisson Distributions****RNGforGPD**)

Generation of univariate and multivariate data that follow the generalized Poisson distribution. The details of the method are explained in Demirtas (2017) <DOI:10.1080/03610918.2014.968725>.

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**Sparse Principal Component Analysis (SPCA)****sparsepca**)

Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few ‘active’ (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>.