Implements approximate Bayesian inference for Structural
    Equation Models (SEM) using a custom adaptation of the Integrated
    Nested Laplace Approximation as described in Rue et al. (2009)
    <doi:10.1111/j.1467-9868.2008.00700.x>. Provides a computationally
    efficient alternative to Markov Chain Monte Carlo (MCMC) for Bayesian
    estimation, allowing users to fit latent variable models using the
    'lavaan' syntax.

A collection of functions for creating color schemes.
    Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory Project Office.

Functionality to dynamically define R functions and S4 methods
 with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling
 conventions.

Generates Rd files from R source code with comments.
 The main features of the default syntax are that
 (1) docs are defined in comments near the relevant code,
 (2) function argument names are not repeated in comments, and
 (3) examples are defined in R code, not comments.
 It is also easy to define a new syntax.

Contains bibliographic information for the U.S. Geological Survey
    (USGS) Idaho National Laboratory (INL) Project Office.

Interpretation methods for analyzing the behavior and individual
    predictions of modern neural networks in a three-step procedure: Converting 
    the model, running the interpretation method, and visualizing the results. 
    Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004)
    <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance
    propagation ('LRP') described by Bach et al. (2015)
    <doi:10.1371/journal.pone.0130140>, deep learning important features
    ('DeepLIFT') described by Shrikumar et al.  (2017) <doi:10.48550/arXiv.1704.02685>
    and gradient-based methods like 'SmoothGrad' described by Smilkov et
    al. (2017) <doi:10.48550/arXiv.1706.03825>, 'Gradient x Input' 
    or 'Vanilla Gradient'.
    Details can be found in the accompanying scientific paper: Koenen & Wright 
    (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).