New package oasis with initial version 0.99.5
Package: oasis
Type: Package
Title: Multiple Sclerosis Lesion Segmentation using Magnetic Resonance Imaging (MRI)
Version: 0.99.5
Date: 2015-12-26
Authors@R: c(person(given = "Elizabeth M.", "Sweeney", email = "emsweene@jhsph.edu", role = c("aut", "cre")), person(given = "John", family = "Muschelli", email = "muschellij2@gmail.com", role = c("aut")), person( given = "R. Taki", family = "Shinohara", email = "rshi@mail.med.upenn.edu", role = c("aut")))
Maintainer: Elizabeth M. Sweeney <emsweene@jhsph.edu>
Description: Trains and makes predictions from the OASIS method, described in detail in the paper "OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI" <doi:10.1016/j.nicl.2013.03.002>. OASIS is a method for multiple sclerosis (MS) lesion segmentation on structural magnetic resonance image (MRI) studies. OASIS creates probability maps of lesion presence using the FLAIR, T2, T1, and PD structural MRI volumes. This packages allows for training of the OASIS model and prediction of OASIS probability maps from a trained model with user supplied studies that have a gold standard lesion segmentation masks. The package will also create OASIS probability maps for MRI studies using the OASIS model from the OASIS paper if no gold standard lesion segmentation masks are available.
Depends: fslr (>= 1.5.0)
Imports: stats, parallel, oro.nifti
Suggests: downloader
License: GPL-2
LazyLoad: yes
LazyData: yes
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2015-12-26 18:11:17 UTC; johnmuschelli
Author: Elizabeth M. Sweeney [aut, cre], John Muschelli [aut], R. Taki Shinohara [aut]
Repository: CRAN
Date/Publication: 2015-12-26 21:18:11

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Package lcmm updated to version 1.7.4 with previous version 1.7.3.0 dated 2015-10-23

Title: Extended Mixed Models Using Latent Classes and Latent Processes
Description: Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.
Author: Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit Liquet
Maintainer: Cecile Proust-Lima <cecile.proust-lima@inserm.fr>

Diff between lcmm versions 1.7.3.0 dated 2015-10-23 and 1.7.4 dated 2015-12-26

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 lcmm-1.7.4/lcmm/R/plotfit.R               |    4 
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New package validateRS with initial version 1.0.0
Package: validateRS
Type: Package
Title: One-Sided Multivariate Testing Procedures for Rating Systems
Version: 1.0.0
Date: 2015-12-24
Author: Coppens F., Mayer M., Millischer L., Resch F., Sauer S., Schulze K.
Maintainer: Coppens F. <francois.coppens@nbb.be>
Description: An implementation of statistical tests for the validation of rating systems as described in the ECB Working paper ''Advances in multivariate back-testing for credit risk underestimation'', by F. Coppens, M. Mayer, L. Millischer, F. Resch, S. Sauer, K. Schulze (ECB WP series, forthcoming).
Depends: truncnorm, triangle, reshape2, data.table
License: EUPL
Packaged: 2015-12-26 08:57:42 UTC; Karine
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-12-26 10:04:35

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New package TransferEntropy with initial version 1.0
Package: TransferEntropy
Type: Package
Title: The Transfer Entropy Package
Version: 1.0
Date: 2015-12-26
Authors@R: c( person("Ghazaleh", "Haratinezhad Torbati", email = "ghazale.hnt@gmail.com", role = c("aut","cre")), person("Glenn", "Lawyer", email = "lawyer@mpi-inf.mpg.de", role = c("aut")) )
Maintainer: Ghazaleh Haratinezhad Torbati <ghazale.hnt@gmail.com>
Description: Estimates the transfer entropy from one time series to another, where each time series consists of continuous random variables. The transfer entropy is an extension of mutual information which takes into account the direction of information flow, under the assumption that the underlying processes can be described by a Markov model. Two estimation methods are provided. The first calculates transfer entropy as the difference of mutual information. Mutual information is estimated using the Kraskov method, which builds on a nearest-neighbor framework (see package references). The second estimation method estimate transfer entropy via the a generalized correlation sum.
License: GPL (>= 2) | file LICENSE
Depends: R (>= 3.0.0)
Imports: Rcpp (>= 0.11.6)
LinkingTo: Rcpp
URL: https://github.com/Healthcast/TransEnt
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-12-26 09:45:09
Packaged: 2015-12-26 00:45:12 UTC; ghazale
Author: Ghazaleh Haratinezhad Torbati [aut, cre], Glenn Lawyer [aut]

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Package spacetime updated to version 1.1-5 with previous version 1.1-4 dated 2015-04-24

Title: Classes and Methods for Spatio-Temporal Data
Description: Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.
Author: Edzer Pebesma [cre, aut], Benedikt Graeler [ctb], Tom Gottfried [ctb], Robert J. Hijmans [ctb]
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

Diff between spacetime versions 1.1-4 dated 2015-04-24 and 1.1-5 dated 2015-12-26

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 spacetime-1.1-5/spacetime/NAMESPACE                   |    8 
 spacetime-1.1-5/spacetime/R/ST-methods.R              |    6 
 spacetime-1.1-5/spacetime/R/STFDF-methods.R           |   11 
 spacetime-1.1-5/spacetime/R/STIDF-methods.R           |   17 
 spacetime-1.1-5/spacetime/R/coerce.R                  |    6 
 spacetime-1.1-5/spacetime/R/eof.R                     |   25 
 spacetime-1.1-5/spacetime/R/raster.R                  |   31 
 spacetime-1.1-5/spacetime/R/stplot.R                  |    8 
 spacetime-1.1-5/spacetime/R/tgrass.R                  |    7 
 spacetime-1.1-5/spacetime/TODO                        |    4 
 spacetime-1.1-5/spacetime/build/vignette.rds          |binary
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 spacetime-1.1-5/spacetime/inst/doc/jss816.Rnw         |    2 
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 spacetime-1.1-5/spacetime/inst/doc/stgvis.Rmd         |    5 
 spacetime-1.1-5/spacetime/inst/doc/stgvis.html        |  995 +++++++++---------
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New package NNLM with initial version 0.4.0
Package: NNLM
Type: Package
Title: Fast and Versatile Non-Negative Matrix Factorization
Description: This is a package for Non-Negative Linear Models (NNLM). It implements fast sequential coordinate descent algorithms for non-negative linear regression and non-negative matrix factorization. It supports mean square error and Kullback-Leibler divergence loss. Many other features are also implemented, including missing value imputation, domain knowledge integration, designable W and H matrices and multiple forms of regularizations.
Version: 0.4.0
Date: 2015-12-15
Authors@R: c(person("Xihui", "Lin", email = "xihuil.silence@gmail.com", role = c("aut", "cre")), person("Paul C Boutros", role = "aut", email = "Paul.Boutros@oicr.on.ca"))
Depends: R (>= 3.0.1)
Imports: Rcpp (>= 0.11.0), stats, utils
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
LazyData: yes
LazyLoad: yes
NeedsCompilation: yes
Suggests: testthat, knitr, rmarkdown, mice, missForest, ISOpureR
VignetteBuilder: knitr
RoxygenNote: 5.0.0
License: BSD_2_clause + file LICENSE
BugReports: https://github.com/linxihui/NNLM/issues
URL: https://github.com/linxihui/NNLM
Packaged: 2015-12-26 04:37:25 UTC; xlin
Author: Xihui Lin [aut, cre], Paul C Boutros [aut]
Maintainer: Xihui Lin <xihuil.silence@gmail.com>
Repository: CRAN
Date/Publication: 2015-12-26 09:45:05

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