Description: A collection of functions to perform Detrended Fluctuation Analysis (DFA exponent), GUEDES et al. (2019) <doi:10.1016/j.physa.2019.04.132> , Detrended cross-correlation coefficient (RHODCCA), GUEDES & ZEBENDE (2019) <doi:10.1016/j.physa.2019.121286>, DMCA cross-correlation coefficient and Detrended multiple cross-correlation coefficient (DMC), GUEDES & SILVA-FILHO & ZEBENDE (2018) <doi:10.1016/j.physa.2021.125990>, both with sliding windows approach.

Title: SlimPLS multivariate feature selection
Depends: R (>= 2.0.1), e1071, randomForest, methods, bioDist, zipfR
Description: SlimPLS is an implementation of SlimPLS multivariate
Packaged: Fri Aug 6 17:50:46 2010; oferlav1

Title: Sparse Multiple Canonical Correlation Network Analysis Tool
A canonical correlation based framework for constructing phenotype-specific multi-omics networks by integrating multiple omics data types and a quantitative phenotype of interest.
Packaged: 2019-02-24 15:16:50 UTC; wenjennyshi

Title: Small Count Rounding of Tabular Data
Author: Øyvind Langsrud and Johan Heldal
Depends: R (>= 3.0.0), SSBtools, Matrix, methods
Description: A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function RoundViaDummy() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398>.

person("Johan", "Heldal", role = c("aut")))
Description: A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398>.