CRAN Task View: ModelDeployment (https://CRAN.R-project.org/view=ModelDeployment)
CRAN Task View: WebTechnologies (https://CRAN.R-project.org/view=WebTechnologies)

CRAN Task View: Distributions (https://CRAN.R-project.org/view=Distributions)
CRAN Task View: NaturalLanguageProcessing (https://CRAN.R-project.org/view=NaturalLanguageProcessing)

CRAN Task View: Econometrics (https://CRAN.R-project.org/view=Econometrics)
CRAN Task View: Environmetrics (https://CRAN.R-project.org/view=Environmetrics)
CRAN Task View: Finance (https://CRAN.R-project.org/view=Finance)
CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData)
CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries)

Supplies tools for tabulating and analyzing the results of predictive models. The methods employed are applicable to virtually any predictive model and make comparisons between different methodologies straightforward.

Provides the conditional Nelson-Aalen and Aalen-Johansen estimators. The methods are based on Bladt & Furrer (2023), in preparation.

Compute approach bias scores using different scoring algorithms,
    compute bootstrapped and exact split-half reliability estimates,
    and compute confidence intervals for individual participant scores.

A set of Shiny apps for effective communication and understanding in statistics. The current version includes properties of normal distribution, properties of sampling distribution, one-sample z and t tests, two samples independent (unpaired) t test and analysis of variance.

Provides a suite of functions for analyzing sequences of events. Users can generate and code sequences based on predefined rules, with a special focus on the identification of sequences coded as 'ABA' (when one element appears, followed by a different one, and then followed by the first). Additionally, the package offers the ability to calculate the length of consecutive 'ABA'-coded sequences sharing common elements. The methods implemented in this package are based on the work by Ziembowicz, K., Rychwalska, A., & Nowak, A. (2022). <doi:10.1177/10464964221118674>.