Package: memisc
Type: Package
Title: Tools for Management of Survey Data and the Presentation of
Analysis Results
Version: 0.99.6
Date: 2016-02-21
Author: Martin Elff (with contributions from Christopher N. Lawrence, Dave Atkins, Jason W. Morgan, Achim Zeileis)
Maintainer: Martin Elff <memisc@elff.eu>
Description: One of the aims of this package is to make life easier for
useRs who deal with survey data sets. It provides an
infrastructure for the management of survey data including
value labels, definable missing values, recoding of variables,
production of code books, and import of (subsets of) SPSS and
Stata files. Further, it provides functionality to produce
tables and data frames of arbitrary descriptive statistics and
(almost) publication-ready tables of regression model
estimates, which can be exported to LaTeX and HTML.
License: GPL-2
LazyLoad: Yes
Depends: R (>= 3.0.0), lattice, stats, methods, utils, MASS
Suggests: splines, knitr
Enhances: AER, car, eha, lme4, ordinal, simex
Imports: grid
VignetteBuilder: knitr
URL:
http://www.elff.eu/software/memisc/,http://github.com/melff/memisc/
BugReports: http://github.com/melff/memisc/issues
NeedsCompilation: yes
Packaged: 2016-02-21 20:42:49 UTC; elff
Repository: CRAN
Date/Publication: 2016-02-22 01:32:31
Package: latticeExtra
Version: 0.6-28
Date: 2016-01-09
Title: Extra Graphical Utilities Based on Lattice
Author: Deepayan Sarkar <deepayan.sarkar@r-project.org>, Felix Andrews <felix@nfrac.org>
Maintainer: Deepayan Sarkar <deepayan.sarkar@r-project.org>
Description: Building on the infrastructure provided by the lattice
package, this package provides several new high-level
functions and methods, as well as additional utilities
such as panel and axis annotation functions.
Depends: R (>= 2.10.0), lattice, RColorBrewer
Imports: grid, stats, utils, grDevices
Suggests: maps, mapproj, deldir, tripack, zoo, MASS, quantreg, mgcv
URL: http://latticeextra.r-forge.r-project.org/
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2016-02-09 11:06:00 UTC; deepayan
Repository: CRAN
Date/Publication: 2016-02-09 14:36:31
Package: gammSlice
Type: Package
Title: Generalized additive mixed model analysis via slice sampling
Version: 1.3
Date: 2015-01-21
Author: Tung Pham and Matt Wand
Maintainer: Tung Pham <pham.t@unimelb.edu.au>
Description: Uses a slice sampling-based Markov chain Monte Carlo to
conduct Bayesian fitting and inference for generalized additive
mixed models (GAMM). Generalized linear mixed models and
generalized additive models are also handled as special cases
of GAMM.
Depends: R (>= 2.13), KernSmooth, lattice, mgcv
License: GPL (>= 2)
Packaged: 2015-01-21 06:46:35 UTC; tungp
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-01-21 08:24:52
Package: gcbd
Type: Package
Title: GPU/CPU Benchmarking in Debian-based systems
Version: 0.2.5
Date: $Date$
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>
Description: GPU/CPU Benchmarking on Debian-package based systems
This package benchmarks performance of a few standard linear algebra
operations (such as a matrix product and QR, SVD and LU decompositions)
across a number of different BLAS libraries as well as a GPU implementation.
.
To do so, it takes advantage of the ability to 'plug and play' different
BLAS implementations easily on a Debian and/or Ubuntu system. The current
version supports
- reference blas (refblas) which are unaccelerated as a baseline
- Atlas which are tuned but typically configure single-threaded
- Atlas39 which are tuned and configured for multi-threaded mode
- Goto Blas which are accelerated and multithreaded
- Intel MKL which are a commercial accelerated and multithreaded version.
As for GPU computing, we use the CRAN package
- gputools
.
For Goto Blas, the gotoblas2-helper script from the ISM in Tokyo can be
used. For Intel MKL we use the Revolution R packages from Ubuntu 9.10.
License: GPL (>= 2)
LazyLoad: yes
Depends: R (>= 2.11.1), RSQLite, plyr, reshape, lattice
Suggests: gputools, Matrix
SystemRequirements: Debian or Ubuntu system with access to Goto Blas,
Intel MKL, Atlas development build as well as a Nvidia GPU with
CUDA support
OS_type: unix
Packaged: 2013-12-12 03:32:49.992031 UTC; edd
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-12-12 07:29:10
Package: loa
Type: Package
Title: Lattice Options and Add-Ins
Version: 0.2.38
Date: 2016-03-01
Author: Karl Ropkins
Maintainer: Karl Ropkins <k.ropkins@its.leeds.ac.uk>
Description: Various plots and functions that make use of the lattice/trellis plotting framework.
The plots (which include 'loaPlot', 'GoogleMap' and 'trianglePlot') use panelPal(), a function that
extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel
and panel-to-key synchronization/management.
Depends: R (>= 3.0.0), lattice
Imports: methods, MASS, grid, png, RgoogleMaps, RColorBrewer, mgcv
License: GPL (>= 2)
LazyLoad: yes
LazyData: yes
Packaged: 2016-03-01 12:01:33 UTC; Karl
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2016-03-01 17:08:00
Package: mirt
Version: 1.18
Date: 2016-06-24
Type: Package
Title: Multidimensional Item Response Theory
Authors@R: c( person("Phil", family="Chalmers", email =
"rphilip.chalmers@gmail.com", role = c("aut", "cre", "cph")),
person("Joshua", family="Pritikin", role=c('ctb')),
person("Alexander", family="Robitzsch", role=c('ctb')),
person("Mateusz", family="Zoltak", role=c('ctb')),
person("KwonHyun", family="Kim", role=c('ctb')),
person("Carl F.", family="Falk", role=c('ctb')),
person("Adam", family="Meade", role=c('ctb')))
Description: Analysis of dichotomous and polytomous response data using
unidimensional and multidimensional latent trait models under the Item
Response Theory paradigm. Exploratory and confirmatory models can be
estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
bi-factor and two-tier analyses are available for modeling item testlets.
Multiple group analysis and mixed effects designs also are available for
detecting differential item and test functioning as well as modelling
item and person covariates.
VignetteBuilder: knitr
Depends: stats, R (>= 3.1.0), stats4, lattice, methods
Imports: GPArotation, Rcpp, sfsmisc, mgcv, numDeriv
Suggests: boot, latticeExtra, directlabels, shiny, knitr, Rsolnp, alabama, sirt, mirtCAT
ByteCompile: yes
LazyLoad: yes
LazyData: yes
LinkingTo: Rcpp, RcppArmadillo
License: GPL (>= 3)
Repository: CRAN
Maintainer: Phil Chalmers <rphilip.chalmers@gmail.com>
URL: https://github.com/philchalmers/mirt,
https://github.com/philchalmers/mirt/wiki
BugReports: https://github.com/philchalmers/mirt/issues?state=open
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-06-24 06:34:10 UTC; phil
Author: Phil Chalmers [aut, cre, cph],
Joshua Pritikin [ctb],
Alexander Robitzsch [ctb],
Mateusz Zoltak [ctb],
KwonHyun Kim [ctb],
Carl F. Falk [ctb],
Adam Meade [ctb]
Date/Publication: 2016-06-24 09:53:44
Package: mixOmics
Type: Package
Title: Omics Data Integration Project
Version: 6.0.0
Date: 2016-06-13
Depends: R (>= 2.10), MASS, lattice, ggplot2
Imports: igraph, rgl, ellipse, corpcor, RColorBrewer, plyr, parallel, dplyr, tidyr, reshape2, methods
Author: Kim-Anh Le Cao, Florian Rohart, Ignacio Gonzalez, Sebastien Dejean with key contributors Benoit Gautier, Francois Bartolo
and contributions from Pierre Monget, Jeff Coquery, FangZou Yao, Benoit Liquet.
Maintainer: Kim-Anh Le Cao <k.lecao@uq.edu.au>
Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: horizontal integration with regularised Generalised Canonical Correlation Analysis and vertical integration with multi-group Partial Least Squares.
License: GPL (>= 2)
URL: http://www.mixOmics.org
BugReports: mixomics@math.univ-toulouse.fr or
https://bitbucket.org/klecao/package-mixomics/issues
Repository: CRAN
Date/Publication: 2016-06-14 12:08:22
Packaged: 2016-06-14 06:49:17 UTC; klecao
NeedsCompilation: no
LazyData: true
providing 
.