A fast class noise detector which provides noise score for each observations. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

Provides a collection of functions to fit and explore single, multi-component and restricted Frequency Modulated Moebius (FMM) models. 'FMM' is a nonlinear parametric regression model capable of fitting non-sinusoidal shapes in rhythmic patterns. Details about the mathematical formulation of 'FMM' models can be found in Rueda et al. (2019) <doi:10.1038/s41598-019-54569-1>.

Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.

Use R to access to the 'FMP Cloud' API <https://fmpcloud.io/> and 
    'Financial Modeling Prep' API <https://financialmodelingprep.com/developer/docs/>.
    Data available includes stock prices, market indexes, company fundamentals,
    13F holdings data, and much more. A valid API token must be set to enable
    functions.

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

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.