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>.

Provides a flexible interface to the 'Financial Modeling Prep' API 
    <https://site.financialmodelingprep.com/developer/docs>. The package 
    supports all available endpoints and parameters, enabling R users 
    to interact with a wide range of financial data.

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) <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>.