Contains functions to query and visualize the Neuroimaging features 
  associated with genetically regulated gene expression (GReX). The primary utility,
  neuroimaGene(), relies on a list of user-defined genes and returns a table of
  neuroimaging features (NIDPs) associated with each gene. This resource 
  is designed to assist in the interpretation of genome-wide and transcriptome-wide
  association studies that evaluate brain related traits. Bledsoe (2024) 
  <doi:10.1016/j.ajhg.2024.06.002>. In addition there are several visualization 
  functions that generate summary plots and 2-dimensional visualizations of regional 
  brain measures. Mowinckel (2020).

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>.

Tools for the structured processing of PET neuroimaging data 
    in preparation for the estimation of Simultaneous Confidence Corridors (SCCs) 
    for one-group, two-group, or single-patient vs group comparisons. 
    The package facilitates PET image loading, data restructuring, integration 
    into a Functional Data Analysis framework, contour extraction, identification 
    of significant results, and performance evaluation. It bridges established 
    packages (e.g., 'oro.nifti') with novel statistical methodologies (e.g., 
    'ImageSCC') and enables reproducible analysis pipelines, including 
    comparison with Statistical Parametric Mapping ('SPM').

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>.