Performs modeling and forecasting of park visitor counts
	using social media data and (partial) on-site visitor counts.
	Specifically, the model is built based on an automatic decomposition
	of the trend and seasonal components of the social media-based park visitor counts,
	from which short-term forecasts of the visitor counts and percent changes
	in the visitor counts can be made. A reference for the underlying model that 'VisitorCounts' uses can be found at 
    Russell Goebel, Austin Schmaltz, Beth Ann Brackett, Spencer A. Wood, Kimihiro Noguchi (2023) <doi:10.1002/for.2965> .

Provides a suite of analytical functionalities to process and analyze
    visual meteor observations from the Visual Meteor Database
    of the International Meteor Organization <https://www.imo.net/>.

Provides an R interface to the 'vis.js' JavaScript charting
    library. It allows an interactive visualization of networks.

Provides methods to transform omop_result objects into
    formatted tables and figures, facilitating the visualization of study
    results working with the Observational Medical Outcomes Partnership
    (OMOP) Common Data Model.

Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.)

Visualization of the most powerful statistical hypothesis test.
    The function vistat() visualizes the statistical hypothesis testing between the 
    dependent variable (response) varsample and the independent variable (feature) varfactor. 
    The statistical hypothesis test (including the eventual corresponding post-hoc analysis) with
    the highest statistical power fulfilling the assumptions of the corresponding test is chosen based on a decision tree.
    A graph displaying the raw data accordingly to the chosen test is generated, the test statistics including eventual 
    post-hoc-analysis are returned. The automated workflow is especially suited for browser based interfaces to 
    server-based deployments of R. Implemented tests: lm(), t.test(), wilcox.test(), aov(), kruskal.test(), fisher.test(), chisqu.test().
    Implemented tests to check the normal distribution of standardized residuals: shapiro.test() and ad.test().
    Implemented post-hoc tests: TukeyHSD() for aov() and pairwise.wilcox.test() for kruskal.test().
    For the comparison of averages, the following algorithm is implemented: 
    If the p-values of the standardized residuals of both shapiro.test() or ad.test() are smaller 
    than 1-conf.level, kruskal.test() resp. wilcox.test() are performed, otherwise the oneway.test()
    and aov() resp. t.test() are performed and displayed. Exception: 
    If the sample size is bigger than 100, t.test() is always performed and wilcox.test() is never executed 
    (Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>).
    For the test of independence of count data, Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is implemented: 
    If more than 20 percent of all cells have a count smaller than 5, fisher.test() is performed and displayed,
    otherwise chisqu.test(). In both cases case an additional mosaic plot is generated.