CRAN Task View: MachineLearning (https://CRAN.R-project.org/view=MachineLearning)
CRAN Task View: Spatial (https://CRAN.R-project.org/view=Spatial)

CRAN Task View: CompositionalData (https://CRAN.R-project.org/view=CompositionalData)
CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData)

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)

Simulate population dynamics from realistically complex matrix
    population models in a plug-and-play fashion. Supports aspatial and 
    spatially implicit models with one or more species and time-varying
    covariates, stochasticity, density dependence, additions or removals of
    individuals, interspecific interactions, and metapopulations.

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

Implements a simple version of multivariate matching using a propensity score, near-exact matching, near-fine balance, and robust Mahalanobis distance matching (Rosenbaum 2020 <doi:10.1146/annurev-statistics-031219-041058>).  You specify the variables, and the program does everything else.

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.