Provides a function called COTUCKER3() (Co-Inertia Analysis
    + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of
    matrices, as well as other functions called PCA() (Principal Component Analysis)
    and BGA() (Between-Groups Analysis), which perform analysis of one matrix,
    COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA()
    (Partial Triadic Analysis), STATIS(), STATISDUAL() and TUCKER3(), which perform analysis of a sequence
    of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO()
    (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS
    method), which also perform analysis of two sequences of matrices.

This is designed for use with an arbitrary set of equations with
 an arbitrary set of unknowns.
 The user selects "fixed" values for enough unknowns to leave as many variables as
 there are equations, which in most cases means the system is properly
 defined and a unique solution exists. The function, the fixed values
 and initial values for the remaining unknowns are fed to a nonlinear backsolver.  
 The original version of "TK!Solver" , now a product of Universal Technical Systems (<https://www.uts.com>) was the inspiration for this function.

Kernel-based 'Tweedie' compound Poisson gamma model using high-dimensional predictors for the analyses of zero-inflated response variables. The package features built-in estimation, prediction and cross-validation tools and supports choice of different kernel functions. For more details, please see Yi Lian, Archer Yi Yang, Boxiang Wang, Peng Shi & Robert William Platt (2023) <doi:10.1080/00401706.2022.2156615>.

A new set of tools to help with the development of detailed ecological 
    niche models using multiple algorithms. Pre-modeling analyses and 
    explorations can be done to prepare data. Model calibration (model selection)
    can be done by creating and testing models with several parameter combinations.  
    Handy options for producing final models with transfers are included. Other tools to 
    assess extrapolation risks and variability in model transfers are also 
    available. Methodological and theoretical basis for the methods implemented 
    here can be found in:
    Peterson et al. (2011) <https://www.degruyter.com/princetonup/view/title/506966>,
    Radosavljevic and Anderson (2014) <doi:10.1111/jbi.12227>, 
    Peterson et al. (2018) <doi:10.1111/nyas.13873>,
    Cobos et al. (2019) <doi:10.7717/peerj.6281>,
    Alkishe et al. (2020) <doi:10.1016/j.pecon.2020.03.002>,
    Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>,
    Arias-Giraldo and Cobos (2024) <doi:10.17161/bi.v18i.21742>,
    Cobos et al. (2024) <doi:10.17161/bi.v18i.21742>.