Estimates Two-way Fixed Effects difference-in-differences/event-study models using the approach proposed by Gardner (2021) <doi:10.48550/arXiv.2207.05943>. To avoid the problems caused by OLS estimation of the Two-way Fixed Effects model, this function first estimates the fixed effects and covariates using untreated observations and then in a second stage, estimates the treatment effects.

A basic, clear implementation of tree-based gradient boosting
    designed to illustrate the core operation of boosting models. Tuning
    parameters (such as stochastic subsampling, modified learning rate, or
    regularization) are not implemented. The only adjustable parameter is the
    number of training rounds. If you are looking for a high performance boosting
    implementation with tuning parameters, consider the 'xgboost' package.

Directed Dependence Coefficient (didec) is a measure of directed dependence. Multivariate Feature Ordering by Conditional Independence (MFOCI) is a variable selection algorithm based on didec. Hierarchical Variable Clustering (VarClustPartition) is a variable clustering method based on didec. For more information, see the paper by 
    Ansari and Fuchs (2024, <doi:10.48550/arXiv.2212.01621>), and the paper by 
    Fuchs and Wang (2024, <doi:10.1016/j.ijar.2024.109185>).

Enables the user to build a citation network/graph from bibliographic data and, based on modularity and heterocitation metrics, assess the degree of awareness/cross-fertilization between two corpora/communities. This toolset is optimized for Scopus data.

Provides a big-data-friendly and memory-efficient difference-in-differences estimator for staggered (and non-staggered) treatment contexts.

Estimation of Difference-in-Differences (DiD) estimators from de Chaisemartin et al. (2025) <doi:10.48550/arXiv.2405.04465> in Heterogeneous Adoption Designs with Quasi Untreated Groups.

Estimates Two-way Fixed Effects difference-in-differences/event-study models using the imputation-based approach proposed by Borusyak, Jaravel, and Spiess (2021).