Defines the underlying pipeline structure for reproducible 
    neuroscience, adopted by 'RAVE' (reproducible analysis and visualization 
    of intracranial electroencephalography); provides high-level class 
    definition to build, compile, set, execute, and share analysis pipelines. 
    Both R and 'Python' are supported, with 'Markdown' and 'shiny' dashboard 
    templates for extending and building customized pipelines. See the full 
    documentations at <https://rave.wiki>; to cite us, 
    check out our paper by Magnotti, Wang, and Beauchamp (2020, 
    <doi:10.1016/j.neuroimage.2020.117341>), or run 
    citation("ravepipeline") for details.

Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.

Implemented fast and memory-efficient Notch-filter, 
    Welch-periodogram, discrete wavelet spectrogram for minutes of 
    high-resolution signals, fast 3D convolution, image registration,
    3D mesh manipulation; providing fundamental toolbox for intracranial 
    Electroencephalography (iEEG) pipelines. 
    Documentation and examples about 'RAVE' project are provided at 
    <https://rave.wiki>, and the paper by John F. Magnotti, 
    Zhengjia Wang, Michael S. Beauchamp (2020) 
    <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("ravetools")' for 
    details.

In order to facilitate R instruction for actuaries, we have organized several 
  sets of publicly available data of interest to non-life actuaries. In addition, we suggest 
  a set of packages, which most practicing actuaries will use routinely. Finally, there is 
  an R markdown skeleton for basic reserve analysis.

Assess LC–MS system performance by visualizing instrument log
    files and monitoring raw quality control samples within a project.

The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for 
    credit risk scoring models. This package calculates the K–S statistic and 
    plots the true-positive rate and false-positive rate to measure the model strength. 
    This package was written with the credit marketer, who uses risk models in 
    conjunction with his campaigns. The users could read more details from 
    Thrasher (1992) <doi:10.1002/dir.4000060408> and 'pyks' <https://pypi.org/project/pyks/>.