Package: HH
Type: Package
Title: Statistical Analysis and Data Display: Heiberger and Holland
Version: 3.1-32
Date: 2016-06-22
Author: Richard M. Heiberger
Maintainer: Richard M. Heiberger <rmh@temple.edu>
Depends: R (>= 3.0.2), lattice, stats, grid, latticeExtra, multcomp, gridExtra (>= 2.0.0), graphics
Imports: reshape2, leaps, vcd, colorspace, RColorBrewer, shiny (>=
0.13.1), Hmisc, abind, Rmpfr (>= 0.6.0), grDevices, methods
Suggests: mvtnorm, car, Rcmdr, RcmdrPlugin.HH, TeachingDemos, microplot
Description: Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2016-06-22 04:32:23 UTC; rmh
Repository: CRAN
Date/Publication: 2016-06-23 01:13:28
Encoding: UTF-8
Package: COMBIA
Type: Package
Title: Synergy/Antagonism Analyses of Drug Combinations
Version: 1.0-4
Date: 2015-07-24
Author: Muhammad Kashif
Maintainer: Muhammad Kashif <Muhammad.Kashif@medsci.uu.se>
Description: A comprehensive synergy/antagonism analyses of drug combinations with
quality graphics and data. The analyses can be performed by Bliss independence and Loewe
additivity models. COMBIA provides improved statistical analysis and makes only very weak assumption of data variability
while calculating bootstrap intervals (BIs). Finally, package saves analyzed data,
2D and 3D plots ready to use in research publications. COMBIA does not require manual
data entry. Data can be directly input from wetlab experimental platforms
for example fluostar, automated robots etc. One needs to call a single function only
to perform all analysis (examples are provided with sample data).
Depends: hash, gdata, lattice, latticeExtra, oro.nifti
Imports: grDevices, stats, utils
License: GPL (>= 2)
LazyLoad: yes
NeedsCompilation: no
Packaged: 2015-07-26 08:41:27 UTC; dawood
Repository: CRAN
Date/Publication: 2015-07-26 18:53:01
Package: DCL
Type: Package
Title: Claims Reserving under the Double Chain Ladder Model
Version: 0.1.0
Date: 2013-10-24
Depends: lattice, latticeExtra
Author: Maria Dolores Martinez-Miranda, Jens Perch Nielsen and Richard Verrall
Maintainer: Maria Dolores Martinez-Miranda <mmiranda@ugr.es>
Description: Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).
License: GPL-2
Packaged: 2013-10-24 21:26:23 UTC; Usuario
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-10-25 07:16:10
Package: vwr
Type: Package
Title: Useful functions for visual word recognition research
Version: 0.3.0
Date: 2013-08-07
Author: Emmanuel Keuleers
Maintainer: Emmanuel Keuleers <emmanuel.keuleers@ugent.be>
Description: Functions and data for use in visual word recognition research:
Computation of neighbors (Hamming and Levenshtein
distances), average distances to neighbors (e.g., OLD20),
and Coltheart's N. Also includes the LD1NN algorithm to
detect bias in the composition of a lexical decision task. Most of
the functions support parallel execution. Supplies wordlists for
several languages. Uses the string distance functions from the stringdist package by Mark van der Loo.
Depends: R (>= 3.0.1), stringdist, lattice, latticeExtra
Suggests: parallel
License: GPL (>= 3)
LazyData: yes
Encoding: UTF-8
Packaged: 2013-08-19 09:48:23 UTC; emmanuel
NeedsCompilation: no
LazyDataCompression: xz
Repository: CRAN
Date/Publication: 2013-08-19 12:28:49
Package: wskm
Version: 1.4.28
Date: 2015-07-08
Title: Weighted k-Means Clustering
Authors@R: c(person("Graham", "Williams", email="graham.williams@togaware.com", role="aut"),
person("Joshua Z", "Huang", email="zx.huang@szu.edu.cn", role="aut"),
person("Xiaojun", "Chen", email="xjchen.hitsz@gmail.com", role="aut"),
person("Qiang", "Wang", role="aut"),
person("Longfei", "Xiao", role="aut"),
person("He", "Zhao", email="Simon.Yansen.Zhao@gmail.com", role="cre"))
Maintainer: He Zhao <Simon.Yansen.Zhao@gmail.com>
Depends: R (>= 2.10), grDevices, stats, lattice, latticeExtra, clv
Description: Entropy weighted k-means (ewkm) is a weighted subspace
clustering algorithm that is well suited to very high
dimensional data. Weights are calculated as the importance of
a variable with regard to cluster membership. The two-level
variable weighting clustering algorithm tw-k-means (twkm)
introduces two types of weights, the weights on individual
variables and the weights on variable groups, and they are
calculated during the clustering process. The feature group
weighted k-means (fgkm) extends this concept by grouping
features and weighting the group in addition to weighting
individual features.
License: GPL (>= 3)
Copyright: 2011-2014 Shenzhen Institutes of Advanced Technology Chinese
Academy of Sciences
LazyLoad: yes
LazyData: yes
URL: https://github.com/SimonYansenZhao/wskm,
http://english.siat.cas.cn/
BugReports: https://github.com/SimonYansenZhao/wskm/issues
NeedsCompilation: yes
Packaged: 2015-07-08 11:47:00 UTC; simon
Author: Graham Williams [aut],
Joshua Z Huang [aut],
Xiaojun Chen [aut],
Qiang Wang [aut],
Longfei Xiao [aut],
He Zhao [cre]
Repository: CRAN
Date/Publication: 2015-07-08 14:46:30
providing 
.