Graphical Tools of Histogram PCA (GraphPCA)
Histogram principal components analysis is the generalization of the PCA. Histogram data are adapted to design complex and big data which histograms used as variables (big data adapter). Functions implemented provides numerical and graphical tools of an extension of PCA. Sun Makosso Kallyth (2016) <doi:10.1002/sam.11270>. Sun Makosso Kallyth and Edwin Diday (2012) <doi:10.1007/s11634-012-0108-0>.

Convert Plot to ‘grob’ or ‘ggplot’ Object (ggplotify)
Convert plot function call (using expression or formula) to ‘grob’ or ‘ggplot’ object that compatible to the ‘grid’ and ‘ggplot2’ ecosystem. With this package, we are able to e.g. using ‘cowplot’ to align plots produced by ‘base’ graphics, ‘grid’, ‘lattice’, ‘vcd’ etc. by converting them to ‘ggplot’ objects.

Linear Model with Tree-Based Lasso Regularization for Rare Features (rare)
Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2018) <arXiv:1803.06675>. The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free.

Tools, Measures and Statistical Tests for Cultural Evolution (cultevo)
Provides tools for measuring the compositionality of signalling systems (in particular the information-theoretic measure due to Spike (2016) <http://…/25930> and the Mantel test for distance matrix correlation (after Dietz 1983) <doi:10.1093/sysbio/32.1.21>), functions for computing string and meaning distance matrices as well as an implementation of the Page test for monotonicity of ranks (Page 1963) <doi:10.1080/01621459.1963.10500843> with exact p-values up to k = 22.

Power Analysis for a SMART Design (smartsizer)
A set of tools for determining the necessary sample size in order to identify the optimal dynamic treatment regime in a sequential, multiple assignment, randomized trial (SMART). Utilizes multiple comparisons with the best methodology to adjust for multiple comparisons. Designed for an arbitrary SMART design. Please see Artman (2018) <arXiv:1804.04587> for more details.

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