Imports: methods, dplyr, sp, sf, geodist, pracma, corpcor, foreach,

Title: Genome-wide significance for whole genome sequencing studies
Author: ChangJiang Xu and Celia M.T. Greenwood
Description: Estimate genome-wide significance using extrapolation approach

Title: Estimating Genome-Wide Significance for Whole Genome Sequencing
Studies, Either Single SNP Tests or Region-Based Tests
Description: The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.
Packaged: 2016-06-20 17:39:12 UTC; ChangJiang

Packaged: 2016-09-12 14:07:49 UTC; ChangJiang

Title: First Passage Time Simulation for Gaussian Diffusion Processes
Maintainer: Maria Francesca Carfora <f.carfora@iac.cnr.it>
Description: In this package we consider Gaussian Diffusion processes and smooth thresholds. After evaluating the mean of the process to check the subthreshold regimen hypothesis, the FPT density function is reconstructed via the numerical quadrature of the integral equation in (Buonocore 1987); first passage times are also generated by the method in (Buonocore 2014) and results are compared. The timestep of the simulations can iteratively be refined. User should provide the functional form for the drift and the infinitesimal variance in the script 'userfunc.R' and for the threshold in the script 'userthresh.R'. All the parameters required by the implementation are to be set in the script 'userparam.R'. Example scripts for common drifts and thresholds are given.

Title: Train and Apply a Gaussian Stochastic Process Model
Description: Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or MAP estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>.  Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.
Author: William J. Welch [aut, cre, cph]
Maintainer: William J. Welch <will@stat.ubc.ca>