"christian@sigg-iten.ch", role = c("aut", "cre")), person("R
Description: This package implements two algorithms for canonical correlation
analysis (CCA) that are based on iterated regression
steps. By choosing the appropriate regression algorithm for each data
modality, it is possible to enforce sparsity, non-negativity or other kinds
of constraints on the projection vectors. Multiple canonical variables are
computed sequentially using a generalized deflation scheme, where the
additional correlation not explained by previous variables is maximized.
'nscancor' is used to analyze paired data from two domains, and has the same
interface as the 'cancor' function from the 'stats' package (plus some extra
parameters). 'mcancor' is appropriate for analyzing data from three or more
Suggests: CCA, glmnet, MASS, PMA, testthat (>= 0.8), roxygen2

Title: Prepares and Writes Files to Submit to the National Student
Authors@R: person("Arie", "Spirgel", email = "spirgel@gmail.com",
Description: Prepares and writes files to submit to the National Student Clearinghouse's

Title: Nonlinear Sufficient Dimension Reduction
Description: Provides tools to implement both unsupervised and supervised nonlinear dimension reduction methods. Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and Sliced Average Variance Estimation (SAVE) are useful methods to reduce the dimensionality of covariates. However, they produce linear combinations of covariates. Kernel PCA, generalized SIR, and generalized SAVE address this problem by extending the applicability of the dimension reduction problem to nonlinear settings. This package includes a comprehensive algorithm for  kernel PCA, generalized SIR, and generalized SAVE, including methods for choosing tuning parameters and some essential functions. The algorithms for this package is from Li (2018, ISBN:9780367734725).

Description: Provides tools to implement both unsupervised and supervised nonlinear dimension reduction methods. Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and Sliced Average Variance Estimation (SAVE) are useful methods to reduce the dimensionality of covariates. However, they produce linear combinations of covariates. Kernel PCA, generalized SIR, and generalized SAVE address this problem by extending the applicability of the dimension reduction problem to nonlinear settings. This package includes a comprehensive algorithm for  kernel PCA, generalized SIR, and generalized SAVE, including methods for choosing tuning parameters and some essential functions.
Packaged: 2021-06-03 05:50:40 UTC; kyongwonkim