Implements five factor extraction methods for asset pricing and
    macroeconomic forecasting: principal component analysis (PCA), partial
    least squares (PLS), scaled PCA (sPCA) of Huang, Jiang, Li, Tong, and
    Zhou (2022) <doi:10.1287/mnsc.2021.4020>, the reduced-rank approach
    (RRA) of He, Huang, Li, and Zhou (2023) <doi:10.1287/mnsc.2022.4563>,
    and Instrumented PCA (IPCA) of Kelly, Pruitt, and Su (2019)
    <doi:10.1016/j.jfineco.2019.05.001>.

Functions to filter GPS/Argos locations, as well as assessing the sample size for the analysis of animal distributions. The filters remove temporal and spatial duplicates, fixes located at a given height from estimated high tide line, and locations with high error as described in Shimada et al. (2012) <doi:10.3354/meps09747> and Shimada et al. (2016) <doi:10.1007/s00227-015-2771-0>. Sample size for the analysis of animal distributions can be assessed by the conventional area-based approach or the alternative probability-based approach as described in Shimada et al. (2021) <doi:10.1111/2041-210X.13506>.

Implementation of the modified skew discrete Laplace (SDL) regression model.
    The package provides a set of functions for a complete analysis of integer-valued data, 
    where the dependent variable is assumed to follow a modified SDL distribution. This regression
    model is useful for the analysis of integer-valued data and experimental studies in which
    paired discrete observations are collected.

An extensible framework for developing species distribution
    models using individual and community-based approaches, generate ensembles of
    models, evaluate the models, and predict species potential distributions in
    space and time. For more information, please check the following paper:
    Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.

Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ćevid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.1080/10618600.2025.2569602>).