---
- branch: MAIN
  date: Wed Jul 31 13:09:37 UTC 2019
  files:
  - new: '1.3103'
    old: '1.3102'
    path: pkgsrc/doc/CHANGES-2019
    pathrev: pkgsrc/doc/CHANGES-2019@1.3103
    type: modified
  - new: '1.413'
    old: '1.412'
    path: pkgsrc/math/Makefile
    pathrev: pkgsrc/math/Makefile@1.413
    type: modified
  - new: '1.1'
    old: '0'
    path: pkgsrc/math/R-acepack/DESCR
    pathrev: pkgsrc/math/R-acepack/DESCR@1.1
    type: added
  - new: '1.1'
    old: '0'
    path: pkgsrc/math/R-acepack/Makefile
    pathrev: pkgsrc/math/R-acepack/Makefile@1.1
    type: added
  - new: '1.1'
    old: '0'
    path: pkgsrc/math/R-acepack/distinfo
    pathrev: pkgsrc/math/R-acepack/distinfo@1.1
    type: added
  id: 20190731T130937Z.412a92a356ad5f5c520ca6a609842be2513a660e
  log: |
    R-acepack: initial commit.

    Two nonparametric methods for multiple regression transform selection
    are provided. The first, Alternative Conditional Expectations (ACE),
    is an algorithm to find the fixed point of maximal correlation, i.e.
    it finds a set of transformed response variables that maximizes R^2
    using smoothing functions [see Breiman, L., and J.H. Friedman. 1985.
    "Estimating Optimal Transformations for Multiple Regression and
    Correlation". Journal of the American Statistical Association.
    80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is
    the Additivity Variance Stabilization (AVAS) method which works better
    than ACE when correlation is low [see Tibshirani, R.. 1986.
    "Estimating Transformations for Regression via Additivity and Variance
    Stabilization". Journal of the American Statistical Association.
    83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction
    to these two methods is in chapter 16 of Frank Harrel's "Regression
    Modeling Strategies" in the Springer Series in Statistics.
  module: pkgsrc
  subject: 'CVS commit: pkgsrc'
  unixtime: '1564578577'
  user: brook