Author: Megan Heyman, Snigdhansu Chatterjee
Description: Perform the Wild Scale-Enhanced (WiSE) bootstrap.  Specifically, the user may supply a single or multiple equally-spaced time series and use the WiSE bootstrap to select a wavelet-smoothed model.  Conversely, a pre-selected smooth level may also be specified for the time series.  Quantities such as the bootstrap sample of wavelet coefficients, smoothed bootstrap samples, and specific hypothesis testing and confidence region results of the wavelet coefficients may be obtained.  Additional functions are available to the user which help format the time series before analysis.  This methodology is recommended to aid in model selection and signal extraction.
Note:  This package specifically uses wavelet bases in the WiSE bootstrap methodology, but the theoretical construct is much more versatile.

Title: Fitting many skinny linear models to a single data set
Author: Mark Seligman, with contributions from Chris Fraley.
SystemRequirements: Nvidia's CUDA toolkit (release >= 4.1); Linux
operationg system; gcc (release <= 4.5).
Description: WideLM fits large numbers of linear models to a fixed
design matrix and response.  An Nvidia GPU is used to
accelerate model fitting, with the CUDA API enabling parallel
execution of multiple individual fits.  The individual models
must have modest size.  The package implements a chunking
interface to the GPU kernel, allowing the caller to stream
model specifications.  Hence the number of fits to be performed

Title: Derivatives of the Diffusion Density and Cumulative Distribution
Authors@R: c(person("Raphael", "Hartmann", email = "raphael.hartmann@protonmail.com", role = c("aut", "cre")),
person("Karl C.", "Klauer", email="christoph.klauer@psychologie.uni-freiburg.de", role=c("cph", "aut", "ctb", "ths")),
person("Steven G.", "Johnson", email="stevenj@math.mit.edu", role=c("ctb")),
person("Jean M.", "Linhart", email="jlinhart@stata.com", role=c("ctb")),
Description: Calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF)
with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0,
the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the
non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable.
For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) <https://github.com/stevengj/cubature>. Numerical integration is
required whenever sv, sw, and/or st0 is not zero. Note that numerical integration reduces speed of the computation.