Functions that support stable prediction and classification with radiomics data through factor-analytic modeling. For details, see Peeters et al. (2019) <doi:10.48550/arXiv.1903.11696>.

Contains R-functions to perform an fMRI analysis as described in
             Polzehl and Tabelow (2019) <DOI:10.1007/978-3-030-29184-6>,
             Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>,
             Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>,
             Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.

Lightweight utilities to estimate autoregressive (AR) and
    autoregressive moving average (ARMA) noise models from residuals and apply
    matched generalized least squares to whiten functional magnetic resonance
    imaging (fMRI) design and data matrices. The ARMA estimator follows a
    classic 1982 approach <doi:10.1093/biomet/69.1.81>, and a restricted AR
    family mirrors workflows described by Cox (2012)
    <doi:10.1016/j.neuroimage.2011.08.056>.

Creates, manipulates, and evaluates hemodynamic response functions
    and event-related regressors for functional magnetic resonance imaging data
    analysis. Supports multiple basis sets including Canonical, Gamma, Gaussian,
    B-spline, and Fourier bases. Features decorators for time-shifting and blocking,
    and efficient convolution algorithms for regressor construction. Methods are based
    on standard fMRI analysis techniques as described in Jezzard et al. (2001, ISBN:9780192630711).

Methods for performing fMRI quality assurance (QA) measurements of
  test objects. Heavily  based on the fBIRN procedures detailed by Friedman and 
  Glover (2006) <doi:10.1002/jmri.20583>.

Data-driven fMRI denoising with projection scrubbing (Pham et al 
    (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for 
    DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) 
    <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al 
    (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical 
    Components Correction) (Muschelli et al (2014)
    <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance
    regression. Projection scrubbing is also applicable to other
    outlier detection tasks involving high-dimensional data.