Heterogeneity pursuit methodologies for regularized finite mixture regression by effects-model formulation proposed by Li et al. (2021) <arXiv:2003.04787>.

Generate planar and spherical triangle meshes,
    compute finite element calculations for 1-, 2-, and 3-dimensional flat
    and curved manifolds with associated basis function spaces, methods for
    lines and polygons, and transparent handling of coordinate reference
    systems and coordinate transformation, including 'sf' and 'sp' geometries.
    The core 'fmesher' library code was originally part of the 'INLA' package,
    and implements parts of "Triangulations and Applications" by
    Hjelle and Daehlen (2006) <doi:10.1007/3-540-33261-8>.

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>.

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>.