Distributed estimation method is based on a Laplace factor model
    to solve the estimates of load and specific variance. The philosophy of
    the package is described in Guangbao Guo. (2022).
    <doi:10.1007/s00180-022-01270-z>.

Interface for 'Rcpp' users to 'dlib' <http://dlib.net> which is a
    'C++' toolkit containing machine learning algorithms and computer vision tools.
    It is used in a wide range of domains including robotics, embedded devices,
    mobile phones, and large high performance computing environments. This package
    allows R users to use 'dlib' through 'Rcpp'.

Collection of functions for fitting and interpreting distributed lag interaction models (DLIM). A DLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a continuous variable. Includes a dlim() function for fitting, predict() function for inference, and plotting functions for visualization. Details on methodology are described in Demateis et al. (2024) <doi:10.1002/env.2843>.

Implementation of the Decorrelated Local Linear estimator proposed 
    in <arxiv:1907.12732>. It constructs the confidence interval for the derivative 
    of the function of interest under the high-dimensional sparse additive model.

Provides routines for Maximum likelihood,
    Kalman filtering and smoothing, and Bayesian
    analysis of Normal linear State Space models, also known as 
    Dynamic Linear Models.

As a distributed imputation strategy, the Distributed full
        information Multiple Imputation method is developed to impute
        missing response variables in distributed linear regression.
        The philosophy of the package is described in 'Guo' (2025)
        <doi:10.1038/s41598-025-93333-6>.

A collection of data-limited management procedures that can be evaluated 
    with management strategy evaluation with the 'MSEtool' package, or applied to 
    fishery data to provide management recommendations.

Estimation of distributed lag models (DLMs) based on a Bayesian additive regression trees framework. Includes several extensions of DLMs: treed DLMs and distributed lag mixture models (Mork and Wilson, 2023) <doi:10.1111/biom.13568>; treed distributed lag nonlinear models (Mork and Wilson, 2022) <doi:10.1093/biostatistics/kxaa051>; heterogeneous DLMs (Mork, et. al., 2024) <doi:10.1080/01621459.2023.2258595>; monotone DLMs (Mork and Wilson, 2024) <doi:10.1214/23-BA1412>. The package also includes visualization tools and a 'shiny' interface to check model convergence and to help interpret results.