An alternative canonical correlation/redundancy analysis function, with associated print, plot, and summary methods.  A method for generating helio plots is also included.

Procedures to perform consensus clustering starting from a dissimilarity matrix or a data matrix. It's allowed to select if the subsampling has to be by samples or features. In case of computational heavy load, the procedures can run in parallel.

Another implementation of general regression neural network in R
    based on Specht (1991) <DOI:10.1109/72.97934>. It is applicable to the 
    functional approximation or the classification.

Obtain historical and near real time data related to stocks, index 
    and currencies from the Yahoo Finance API. This package is community maintained 
    and is not officially supported by 'Yahoo'. The accuracy of data is only as 
    correct as provided on <https://finance.yahoo.com/>.

Performs nearest neighbor-based imputation using one or more alternative 
    approaches to processing multivariate data. These include methods based on canonical 
    correlation: analysis, canonical correspondence analysis, and a multivariate adaptation 
    of the random forest classification and regression techniques of Leo Breiman and Adele 
    Cutler. Additional methods are also offered. The package includes functions for 
    comparing the results from running alternative techniques, detecting imputation targets 
    that are notably distant from reference observations, detecting and correcting 
    for bias, bootstrapping and building ensemble imputations, and mapping results.

This collection of data exploration tools was developed at
    Yale University for the graphical exploration of complex
    multivariate data; barcode and gpairs now have their own
    packages.  The big.read.table() function provided here may be
    useful for large files when only a subset is needed (but please
    see the note in the help page for this function).

Implements the 'libyaml' 'YAML' 1.1 parser and emitter
    (<https://pyyaml.org/wiki/LibYAML>) for R.