Strided Iterator and Range (strider)
The strided iterator adapts multidimensional buffers to work with the C++ standard library and range-based for-loops. Given a pointer or iterator into a multidimensional data buffer, one can generate an iterator range using make_strided to construct strided versions of the standard library’s begin and end. For constructing range-based for-loops, a strided_range class is provided. These help authors avoid integer-based indexing, which in some cases can impede algorithm performance and introduce indexing errors. This library exists primarily to expose the header file to other R projects.

WebVR’ Data Visualizations with ‘RStudio Shiny’ and ‘Mozilla A-Frame’ (shinyaframe)
Make R data available in Web-based virtual reality experiences for immersive, cross-platform data visualizations. Includes the ‘gg-aframe’ JavaScript package for a Grammar of Graphics declarative HTML syntax to create 3-dimensional data visualizations with ‘Mozilla A-Frame’ <https://aframe.io>.

Nonparametric Independence Tests Based on Entropy Estimation (IndepTest)
Implementations of the weighted Kozachenko-Leonenko entropy estimator and independence tests based on this estimator, (Kozachenko and Leonenko (1987) <http://…/ppi797> ). Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors.

Multi-Category Classification Accuracy (mcca)
It contains six robust diagnostic accuracy methods to evaluate three or four category classifiers. Hypervolume Under Manifold (HUM), described in the paper: Jialiang Li (2008) <doi:10.1093/biostatistics/kxm050>. Jialiang Li (2014) <doi:10.3109/1354750X.2013.868516>. Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in the paper: Jialiang Li (2013) <doi:10.1093/biostatistics/kxs047>. Polytomous Discrimination Index (PDI), described in the paper: Van Calster B (2012) <doi:10.1007/s10654-012-9733-3>. Jialiang Li (2017) <doi:10.1177/0962280217692830>.

Multilevel Networks Analysis (multinets)
Analyze multilevel networks as described in Lazega et al (2008) <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an extension to ‘igraph’.

Trajectories Data Mining (TrajDataMining)
Contains a set of methods for trajectory data preparation, such as filtering, compressing and clustering, and for trajectory pattern discovery.

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