Title: A Tool for Identification of Differentially Expressed Pathways
Author: Fontoura, C.A.R.S. and Mombach, J.C.M.
Description: An R tool suited to Affymetrix microarray data that combines three different statistical tests (Bootstrap, Fisher exact and Wilcoxon signed rank) to evaluate genetic pathway alterations.
Imports: stringr, rlist, VennDiagram, grDevices, stats, utils, grid

Title: Implementation of the PAWL algorithm
Description: Implementation of the Parallel Adaptive Wang-Landau
algorithm. Also implemented for comparison: parallel adaptive
Depends: methods, mvtnorm, foreach, reshape, ggplot2

Title: Summarize Daily Physical Activity from 'activPAL' Accelerometer
Author: Yukun Zhang [aut,cre], Haocheng Li [aut], Sarah Keadle [ctb], Charles Matthews [ctb], Raymond Carroll [ctb]
Description: Summarize physical activity (Mets, Proportion, Total, etc.) in the given time period from accelerometer data. This package has been tested by data exported from 'activPAL'. 'activPAL' is a wearable device for medical and healthcare research application developed by PAL Technologies, Glasgow, Scotland. A simulated accelerometer sample dataset is provided in this package. See https://github.com/YukunZhang/PAactivPAL1.0 for more information.
Packaged: 2015-12-23 05:49:24 UTC; Yukun Zhang

Description: Summarize physical activity (Mets, Proportion, Total, etc.) in the given time period from accelerometer data. This package has been tested by data exported from 'activPAL'. 'activPAL' is a wearable device for medical and healthcare research application developed by PAL Technologies, Glasgow, Scotland. A simulated accelerometer sample dataset is provided in this package. See https://github.com/YukunZhang/PAactivPAL for more information.
Packaged: 2016-04-15 17:46:44 UTC; Yukun Zhang

Title: Prediction and Accuracy Measures for Nonlinear Models and for
Description: We propose a pair of summary measures for the predictive power of a prediction
function based on a regression model. The regression model can be linear
or nonlinear, parametric, semi-parametric, or nonparametric, and correctly
specified or mis-specified. The first measure, R-squared, is an extension of
the classical R-squared statistic for a linear model, quantifying the prediction
function's ability to capture the variability of the response. The second
measure, L-squared, quantifies the prediction function's bias for predicting the
mean regression function. When used together, they give a complete summary of
the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <arXiv:1611.03063> for more details.