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).

A wrapper for the Julia package 'DiDInt.jl' <https://ebjamieson97.github.io/DiDInt.jl/stable/>
 which implements intersection difference-in-differences (DID-INT), a method
 developed by Karim & Webb (2025) <doi:10.48550/arXiv.2412.14447>. Allows for
 unbiased estimation of the average effect of treatment on the treated (ATT) in
 cases when the common causal covariates assumption is violated. Also computes
 p-values for the ATT via the randomization inference procedure described in
 MacKinnon and Webb (2020) <doi:10.1016/j.jeconom.2020.04.024>.

Estimators of Difference-in-Differences based on de Chaisemartin and D'Haultfoeuille.

Estimation of heterogeneity-robust difference-in-differences estimators, with a binary, discrete, or continuous treatment, in designs where past treatments may affect the current outcome.

This hosts the findRFM function which generates RFM scores on a 1-5 point scale for
             customer transaction data. The function consumes a data frame with Transaction Number,
             Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes.
             The function returns a data frame with RFM data for the sales information.

Allows to visualize high-density electroencephalography (HD-EEG) data through interactive plots and animations, enabling exploratory and communicative analysis of temporal-spatial brain signals. Funder: Masaryk University (Grant No. MUNI/A/1457/2023).

Implements a likelihood-based method for genome polarization, identifying
    which alleles of SNV markers belong to either side of a barrier to gene flow. The
    approach co-estimates individual assignment, barrier strength, and divergence between
    sides, with direct application to studies of hybridization. Includes VCF-to-diem
    conversion and input checks, support for mixed ploidy and parallelization, and tools
    for visualization and diagnostic outputs. Based on diagnostic index expectation
    maximization as described in Baird et al. (2023) <doi:10.1111/2041-210X.14010>.