Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).

A framework to assist creation of marine ecosystem models,
             generating either 'R' or 'C++' code which can then be optimised using
             the 'TMB' package and standard 'R' tools. Principally designed to
             reproduce gadget2 models in 'TMB', but can be extended beyond
             gadget2's capabilities.
             Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) <doi:10.18637/jss.v070.i05> "TMB: Automatic Differentiation and Laplace Approximation.".
             Begley, J., & Howell, D. (2004) <https://core.ac.uk/download/pdf/225936648.pdf> "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.".

Make efficient Rust implementations of graph adjustment
    identification distances available in R. These distances (based on ancestor,
    optimal, and parent adjustment) count how often the respective adjustment
    identification strategy leads to causal inferences that are incorrect
    relative to a ground-truth graph when applied to a candidate graph instead.
    See also Henckel, Würtzen, Weichwald (2024) <doi:10.48550/arXiv.2402.08616>.

A group of sample points are evaluated against a
        user-defined expression, the sample points are lists of
        parameters with values that may be substituted into that
        expression. The genetic algorithm attempts to make the result
        of the expression as low as possible (usually this would be the
        sum of residuals squared).

Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm.  
 For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>. 
 This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. 
 Further study is going on for the nonorthogonal cases and generalized linear models. 
 These works are in part supported by the National Natural Foundation of China (No.12171076).