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>.

Calculate the precision in mean differences (raw or Cohen's D) and 
    correlation coefficients for different sample sizes. Uses permutations of the
    collected functional magnetic resonance imaging (fMRI) region of interest data.
    Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) 
    <doi:10.31234/osf.io/cz32t>.

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.