Package: MLDS
Type: Package
Title: Maximum Likelihood Difference Scaling
Version: 0.1-7
Date: 2009-01-14
Author: Kenneth Knoblauch and Laurence T. Maloney,
based in part on C code written by Laurence T. Maloney
and J. N. Yang
Maintainer: Ken Knoblauch <ken.knoblauch@inserm.fr>
Depends: R (>= 2.6), graphics, stats, utils, base
Imports: MASS
LazyLoad: yes
LazyData: yes
Description:  Difference scaling is a method for scaling perceived
supra-threshold differences.  The package contains functions that
allow the user to design and run a difference scaling experiment,
to fit the resulting data by maximum likelihood and test the
internal validity of the estimated scale.
License: GPL
Packaged: Tue Jan 13 16:24:36 2009; knoblauch

Author: Kenneth Knoblauch and Laurence T. Maloney, based in part on C
code written by Laurence T. Maloney and J. N. Yang
Description: Difference scaling is a method for scaling perceived
supra-threshold differences.  The package contains functions
that allow the user to design and run a difference scaling
experiment, to fit the resulting data by maximum likelihood and
test the internal validity of the estimated scale.
Packaged: Wed May 13 03:37:32 2009; knoblauch

Packaged: 2009-08-16 15:27:26 UTC; knoblauch

Packaged: 2009-08-21 13:53:10 UTC; knoblauch

Packaged: 2009-09-16 13:27:59 UTC; knoblauch

Packaged: 2009-09-26 14:02:57 UTC; knoblauch

Packaged: 2009-11-12 12:39:22 UTC; knoblauch

Packaged: 2010-05-27 16:38:24 UTC; knoblauch

Packaged: 2010-06-14 21:43:11 UTC; knoblauch

Packaged: 2010-10-01 11:58:39 UTC; knoblauch

Packaged: 2011-04-15 11:20:51 UTC; knoblauch

Packaged: 2011-09-30 11:52:18 UTC; knoblauch

Depends: R (>= 2.10), graphics, stats, utils, base

Title: Collection of Machine Learning Datasets for Supervised Machine
person("Gary", "Hutson", , "hutsons-hacks@outlook.com", c("aut", "cre"),
Description: Contains a collection of datasets for working with machine learning tasks.
It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression.
The aim of this package is to use data generated around health and other domains.
Imports: ConfusionTableR, dplyr, parsnip, rsample, recipes, workflows,