Installs 'Neuroconductor' packages from the release repository <https://neuroconductor.org/releases/> or from 'GitHub'.

Neural decoding is method of analyzing neural data that  
    uses a pattern classifiers to predict experimental conditions based 
    on neural activity. 'NeuroDecodeR' is a system of objects that 
    makes it easy to run neural decoding analyses. For more information
    on neural decoding see Meyers & Kreiman (2011)
    <doi:10.7551/mitpress/8404.003.0024>.

Downloads and reads data from Human 'Connectome' Project 
    <https://db.humanconnectome.org> using Amazon Web Services ('AWS') 
    'S3' buckets.

A collection of data structures that represent
    volumetric brain imaging data. The focus is on basic data handling for 3D
    and 4D neuroimaging data. In addition, there are function to read and write
    NIFTI files and limited support for reading AFNI files.

Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.

Preprocessing pipeline for normalizing and cleaning T1-weighted, T2-weighted and FLAIR MRI images coming from different sources, diseases, patients, scanners and sites.

Generates functional Magnetic Resonance Imaging (fMRI) 
             time series or 4D data. Some high-level 
             functions are created for fast data generation with only 
             a few arguments and a diversity of functions to define 
             activation and noise. For more advanced users it is possible 
             to use the low-level functions and manipulate the arguments.
             See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.

Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.