A methodology that makes use of the factor structure of large
dimensional panels to understand the nature of nonstationarity inherent
in data. This is referred to as PANIC, Panel Analysis of Nonstationarity
in Idiosyncratic and Common Components.
PANIC (2004)<doi:10.1111/j.1468-0262.2004.00528.x> includes
valid pooling methods that allow panel tests to be constructed.
PANIC (2004) can detect whether the nonstationarity in a series is
pervasive, or variable specific, or both.
PANIC (2010) <doi:10.1017/s0266466609990478> includes
two new tests on the idiosyncratic component that estimates the pooled
autoregressive coefficient and sample moment, respectively. The PANIC
model approximates the number of factors based on
Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

Model-Based Clustering with Regularization Methods for
High-Dimensional Data
Model-based clustering and identifying informative features based on regularization methods. The package includes three regularization methods - PAirwise Reciprocal fuSE (PARSE) penalty proposed by Wang, Zhou and Hoeting (2016), the adaptive L1 penalty (APL1) and the adaptive pairwise fusion penalty (APFP). Heatmaps are included to shown the identification of informative features.
stats, mvtnorm, gplots, foreach, doParallel, grDevices, utils

Zhiqiu Hu; Shizhong Xu; Zhiquan Wang; Rongcai Yang
An R package for polygenic trait analysis

An R package for polygenic trait analysis.

Calculate Power and Sample Size for Two Sample Mean Tests
Power calculations are a critical component of any research study to determine the
minimum sample size necessary to detect differences between multiple groups. Here we
present an 'R' package, 'PASSED', that performs power and sample size calculations for
the test of two-sample means or ratios with data following beta,
gamma (Chang et al. (2011), <doi:10.1007/s00180-010-0209-1>), normal,
Poisson (Gu et al. (2008), <doi:10.1002/bimj.200710403>), binomial, geometric, and
negative binomial (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>) distributions.