Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.

Longitudinal data offers insights into population changes over
 time but often requires a flexible structure, especially with varying
 follow-up intervals. Panel data is one way to store such records, though
 it adds complexity to analysis. The 'tvtools' package for R simplifies
 exploring and analyzing panel data.

Provides functions for propensity score
        estimating and weighting, nonresponse weighting, and diagnosis
        of the weights.

Provides functions for propensity score
        estimation and weighting for continuous exposures as described in Zhu, Y., 
        Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for
        estimating generalized propensity scores with continuous treatments.
        Journal of Causal Inference, 3(1), 25-40. <doi:10.1515/jci-2014-0022>.

Provides functions for estimating natural direct and indirect effects for mediation analysis. It uses weighting where the weights are functions of estimates of the probability of exposure or treatment assignment (Hong, G (2010). <https://cepa.stanford.edu/sites/default/files/workshops/GH_JSM%20Proceedings%202010.pdf> Huber, M. (2014). <doi:10.1002/jae.2341>). Estimation of probabilities can use generalized boosting or logistic regression. Additional functions provide diagnostics of the model fit and weights. The vignette provides details and examples.

High-performance parsing of 'Tableau' workbook files into
    tidy data frames and dependency graphs for other visualization tools
    like R 'Shiny' or 'Power BI' replication.

An open-access tool/framework that constitutes the core functions
  to analyze terrestrial water cycle data across various spatio-temporal scales.

Implements Time-Weighted Dynamic Time Warping (TWDTW), 
    a measure for quantifying time series similarity. The TWDTW algorithm, 
    described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and 
    Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional 
    time series of various resolutions. It is particularly suitable for comparing 
    time series with seasonality for environmental and ecological data analysis, 
    covering domains such as remote sensing imagery, climate data, hydrology, 
    and animal movement. The 'twdtw' package offers a user-friendly 'R' interface, 
    efficient 'Fortran' routines for TWDTW calculations, flexible time weighting 
    definitions, as well as utilities for time series preprocessing and visualization.