**Chronomics Analysis Toolkit (CAT): Analyze Periodicity** (**CATkit**)

Performs analysis of sinusoidal rhythms in time series data: actogram, smoothing, autocorrelation, crosscorrelation, several flavors of cosinor.

**Bayesian Evidence Combination** (**BayesCombo**)

Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis.

**Pathwise Calibrated Sparse Shooting Algorithm** (**picasso**)

Implement a new family of efficient algorithms, called PathwIse CalibrAted Sparse Shooting AlgOrithm, for a variety of sparse learning problems, including Sparse Linear Regression, Sparse Logistic Regression, Sparse Column Inverse Operator and Sparse Multivariate Regression. Different types of active set identification schemes are implemented, such as cyclic search, greedy search, stochastic search and proximal gradient search. Besides, the package provides the choices between convex (L1 norm) and non-convex (MCP and SCAD) regularizations. Moreover, group regularization, such as group Lasso, group MCP and group SCAD, are also implemented for Sparse Linear Regression, Sparse Logistic Regression and Sparse Multivariate Regression.

**Relevant Component Analysis for Supervised Distance Metric Learning** (**RECA**)

Relevant Component Analysis (RCA) tries to find a linear transformation of the feature space such that the effect of irrelevant variability is reduced in the transformed space.

**Quantitative Analysis of Complex Networks** (**QuACN**)

Quantitative Analysis of Complex Networks. This package offers a set of topological network measures to analyze complex Networks structurally.

**General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics** (**BayesianTools**)

General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

**Significant Variable Selection in Linear Regression** (**SignifReg**)

Provide a significant variable selection procedure with different directions (forward, backward, stepwise) based on diverse criteria (Mallows’ Cp, AIC, BIC, adjusted r-square, p-value). The algorithm selects a final model with only significant variables based on a correction choice of False Discovery Rate, Bonferroni, or no correction.

**Group Testing Procedures for Signal Detection and Goodness-of-Fit** (**SetTest**)

It provides cumulative distribution function (CDF), quantile, p-value, statistical power calculator and random number generator for a collection of group-testing procedures, including the Higher Criticism tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests, the one-sided phi-divergence tests, etc. The input are a group of p-values. The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of signal detection, the null hypothesis means no signals. In the context of the goodness-of-fit testing, which contrasts a group of i.i.d. random variables to a given continuous distribution, the input p-values can be obtained by the CDF transformation. The null hypothesis means that these random variables follow the given distribution. For reference, see Hong Zhang, Jiashun Jin and Zheyang Wu. ‘Distributions and Statistical Power of Optimal Signal Detection Methods in Finite Samples’, submitted.