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.

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.

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

Estimate the effect of a treatment on an outcome in sharp Difference-in-Difference designs with multiple groups and periods. 
    It computes the DIDM estimator introduced in Section 4 of "Two-Way Fixed Effects Estimators with Heterogeneous
    Treatment Effects" (Chaisemartin, D'Haultfoeuille (2020)  <doi:10.1257/aer.20181169>), which
    generalizes the standard DID estimator with two groups, two periods and a binary treatment to situations
    with many groups,many periods and a potentially non-binary treatment. For each pair of consecutive time
    periods t-1 and t and for each value of the treatment d, the package computes a DID estimator comparing
    the outcome evolution among the switchers, the groups whose treatment changes from d to some other value
    between t-1 and t, to the same evolution among control groups whose treatment is equal to d both in t-1 and t.
    Then the DIDM estimator is equal to the average of those DIDs across all pairs of consecutive time periods and
    across all values of the treatment. Under a parallel trends assumption, DIDM is an unbiased and consistent estimator
    of the average treatment effect among switchers, at the time period when they switch.
    The package can also compute placebo estimators that can be used to test the parallel trends assumption.
    Finally, in staggered adoption designs where each group's treatment is weakly increasing over time,
    it can compute estimators of switchers' dynamic treatment effects, one time period or more after they have
    started receiving the treatment.