Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.

Implements a high dimensional mediation analysis algorithm using Local False Discovery Rates. The methodology is described in Roy and Zhang (2024) <doi:10.48550/arXiv.2402.13933>.

The Iterative Proportional Fitting (IPF) algorithm operates on count data. 
    This package offers implementations for several algorithms that extend this to 
    nested structures: 'parent' and 'child' items for both of which constraints can be provided.
    The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>,
    Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>,
    and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods
    is also provided such as 'Truncate, replicate, sample' <doi:10.1016/j.compenvurbsys.2013.03.004>.

R interface to 'MLflow', open source platform for
    the complete machine learning life cycle, see <https://mlflow.org/>.
    This package supports installing 'MLflow', tracking experiments,
    creating and running projects, and saving and serving models.

Climate-sensitive, single-tree forest simulator based on
  data-driven machine learning. It simulates the main forest processes—
  radial growth, height growth, mortality, crown recession, regeneration,
  and harvesting—so users can assess stand development under climate and
  management scenarios. The height model is described by Skudnik and
  Jevšenak (2022) <doi:10.1016/j.foreco.2022.120017>, the basal-area
  increment model by Jevšenak and Skudnik (2021) <doi:10.1016/j.foreco.2020.118601>,
  and an overview of the MLFS package, workflow, and applications is
  provided by Jevšenak, Arnič, Krajnc, and Skudnik (2023), Ecological
  Informatics <doi:10.1016/j.ecoinf.2023.102115>.