Package: nsdr
Type: Package
Title: Nonlinear Sufficient Dimension Reduction
Version: 0.1.0
Authors@R: 
  c(person(given = "Bing",
           family = "Li",
           role = "aut"),
    person(given = "Kyongwon",
           family = "Kim",
           role = c("aut", "cre"),
           email = "kimk@ewha.ac.kr"))
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).
Depends: R (>= 3.5.0)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1.9001
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2021-05-09 22:10:57 UTC; kyongwon
Author: Bing Li [aut],
  Kyongwon Kim [aut, cre]
Maintainer: Kyongwon Kim <kimk@ewha.ac.kr>
Repository: CRAN
Date/Publication: 2021-05-11 09:30:02 UTC
