Comprehensive toolkit for Environmental Phillips Curve analysis 
    featuring multidimensional instrumental variable creation, transfer entropy 
    causal discovery, network analysis, and state-of-the-art econometric methods.
    Implements geographic, technological, migration, geopolitical, financial, 
    and natural risk instruments with robust diagnostics and visualization.
    Provides 24 different instrumental variable approaches with empirical validation.
    Methods based on Phillips (1958) <doi:10.1111/j.1468-0335.1958.tb00003.x>,
    transfer entropy by Schreiber (2000) <doi:10.1103/PhysRevLett.85.461>, and 
    weak instrument tests by Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006>.

Frequently one needs a convenient way to build and tune
             several models in one go.The goal is to provide a number of machine learning convenience 
             functions. It provides the ability to build, tune and obtain predictions of 
             several models in one function. The models are built using functions from
             'caret' with easier to read syntax.
             Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.

Computes indirect effects, conditional effects, and conditional
  indirect effects in a structural equation model or path model after model
  fitting, with no need to define any user parameters or label any paths in
  the model syntax, using the approach presented in Cheung and Cheung
  (2024) <doi:10.3758/s13428-023-02224-z>. Can also form bootstrap
  confidence intervals by doing bootstrapping only once and reusing the
  bootstrap estimates in all subsequent computations. Supports bootstrap
  confidence intervals for standardized (partially or completely) indirect
  effects, conditional effects, and conditional indirect effects as described
  in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui,
  and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by
  structural equation modeling using lavaan() or regression using lm().