Wed, 13 Sep 2017

Package udpipe updated to version 0.1.1 with previous version 0.1.0 dated 2017-09-01

Title: Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the 'UDPipe' 'NLP' Toolkit
Description: This natural language processing toolkit provides language-agnostic 'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency parsing' of raw text. Next to text parsing, the package also allows you to train annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided at <http://universaldependencies.org/format.html>. The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at <doi:10.18653/v1/K17-3009>.
Author: Jan Wijffels [aut, cre, cph], BNOSAC [cph], Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic [cph], Milan Straka [cph], Jana Strakov√° [cph]
Maintainer: Jan Wijffels <jwijffels@bnosac.be>

Diff between udpipe versions 0.1.0 dated 2017-09-01 and 0.1.1 dated 2017-09-13

 DESCRIPTION                         |   18 
 MD5                                 |   64 +-
 NAMESPACE                           |   28 -
 NEWS.md                             |   24 
 R/RcppExports.R                     |   38 -
 R/datasets.R                        |   48 -
 R/pkg.R                             |   26 
 R/udpipe_models.R                   |  236 ++++----
 R/udpipe_parse.R                    |  376 +++++++-------
 R/udpipe_train.R                    |  288 +++++-----
 README.md                           |  180 +++---
 build/vignette.rds                  |binary
 inst/doc/udpipe-annotation.R        |  118 ++--
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 inst/dummydata/traindata.conllu     |   80 +--
 inst/models-ud-2.0/params_parser    |  136 ++---
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 inst/models-ud-2.0/params_tokenizer |  136 ++---
 man/annotation_params.Rd            |   64 +-
 man/as.data.frame.udpipe_connlu.Rd  |   78 +-
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 man/udpipe_load_model.Rd            |   82 +--
 man/udpipe_train.Rd                 |  260 ++++-----
 src/RcppExports.cpp                 |    4 
 src/rcpp_udpipe.cpp                 |   55 --
 src/udpipe.cpp                      |    3 
 vignettes/udpipe-annotation.Rmd     |  320 ++++++------
 vignettes/udpipe-train.Rmd          |  548 ++++++++++----------
 33 files changed, 3267 insertions(+), 3259 deletions(-)

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New package PSTR with initial version 1.0.1
Package: PSTR
Type: Package
Title: Panel Smooth Transition Regression Modelling
Version: 1.0.1
Authors@R: person("Yukai", "Yang", email = "yukai.yang@statistik.uu.se",role = c("aut", "cre"))
Description: Provides the Panel Smooth Transition Regression (PSTR) modelling. The modelling procedure consists of three stages: Specification, Estimation and Evaluation. The package offers sharp tools helping the package user(s) to conduct model specification tests, to do PSTR model estimation, and to do model evaluation. The tests implemented in the package allow for cluster-dependency and are heteroskedasticity-consistent. The wild bootstrap and wild cluster bootstrap tests are also implemented. Parallel computation (as an option) is implemented in some functions, especially the bootstrap tests. The package suits tasks running many cores on super-computation servers.
Depends: R (>= 3.0.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Imports: tibble, snowfall
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2017-09-13 11:46:30 UTC; yyang
Author: Yukai Yang [aut, cre]
Maintainer: Yukai Yang <yukai.yang@statistik.uu.se>
Repository: CRAN
Date/Publication: 2017-09-13 17:34:48 UTC

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Package glmnet updated to version 2.0-12 with previous version 2.0-10 dated 2017-05-06

Title: Lasso and Elastic-Net Regularized Generalized Linear Models
Description: Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
Author: Jerome Friedman [aut, cre], Trevor Hastie [aut, cre], Noah Simon [aut, ctb], Junyang Qian [ctb], Rob Tibshirani [aut, cre]
Maintainer: Trevor Hastie <hastie@stanford.edu>

Diff between glmnet versions 2.0-10 dated 2017-05-06 and 2.0-12 dated 2017-09-13

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New package ADPF with initial version 0.0.1
Package: ADPF
Title: Use Least Squares Polynomial Regression and Statistical Testing to Improve Savitzky-Golay
Version: 0.0.1
Authors@R: c( person("Phillip", "Barak", email = "phillip.barak@wisc.edu", role = "aut"), person("Samuel", "Kruse", email = "samdkruse@gmail.com", role = c("cre","aut")))
Maintainer: Samuel Kruse <samdkruse@gmail.com>
Description: This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) <doi:10.1021/ac00113a006> for more information.
Depends: R (>= 3.2.4), stats, utils
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-09-13 08:59:55 UTC; Administrator
Author: Phillip Barak [aut], Samuel Kruse [cre, aut]
Repository: CRAN
Date/Publication: 2017-09-13 17:50:51 UTC

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Package adlift updated to version 1.3-3 with previous version 1.3-2 dated 2012-11-06

Title: An Adaptive Lifting Scheme Algorithm
Description: Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.
Author: Matt Nunes [aut, cre], Marina Knight [aut], Guy Nason [ctb, ths]
Maintainer: Matt Nunes <m.nunes@lancaster.ac.uk>

Diff between adlift versions 1.3-2 dated 2012-11-06 and 1.3-3 dated 2017-09-13

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Package DescTools updated to version 0.99.22 with previous version 0.99.21 dated 2017-06-29

Title: Tools for Descriptive Statistics
Description: A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
Author: Andri Signorell. Includes R source code and/or documentation previously published by (in alphabetical order): Ken Aho, Andreas Alfons, Nanina Anderegg, Tomas Aragon, Antti Arppe, Adrian Baddeley, Kamil Barton, Ben Bolker, Frederico Caeiro, Stephane Champely, Daniel Chessel, Leanne Chhay, Clint Cummins, Michael Dewey, Harold C. Doran, Stephane Dray, Charles Dupont, Dirk Eddelbuettel, Jeff Enos, Claus Ekstrom, Martin Elff, Kamil Erguler, Richard W. Farebrother, John Fox, Romain Francois, Michael Friendly, Tal Galili, Matthias Gamer, Joseph L. Gastwirth, Yulia R. Gel, Juergen Gross, Gabor Grothendieck, Frank E. Harrell Jr, Richard Heiberger, Michael Hoehle, Christian W. Hoffmann, Torsten Hothorn, Markus Huerzeler, Wallace W. Hui, Pete Hurd, Rob J. Hyndman, Pablo J. Villacorta Iglesias, Matthias Kohl, Mikko Korpela, Max Kuhn, Detlew Labes, Friederich Leisch, Jim Lemon, Dong Li, Martin Maechler, Arni Magnusson, Daniel Malter, George Marsaglia, John Marsaglia, Alina Matei, David Meyer, Weiwen Miao, Giovanni Millo, Yongyi Min, David Mitchell, Markus Naepflin, Daniel Navarro, Henric Nilsson, Klaus Nordhausen, Derek Ogle, Hong Ooi, Nick Parsons, Sandrine Pavoine, Tony Plate, Roland Rapold, William Revelle, Tyler Rinker, Brian D. Ripley, Caroline Rodriguez, Nathan Russell, Nick Sabbe, Venkatraman E. Seshan, Greg Snow, Michael Smithson, Werner A. Stahel, Mark Stevenson, Matthias Templ, Terry Therneau, Yves Tille, Adrian Trapletti, Kevin Ushey, Jeremy VanDerWal, Bill Venables, John Verzani, Gregory R. Warnes, Stefan Wellek, Hadley Wickham, Rand R. Wilcox, Peter Wolf, Daniel Wollschlaeger, Thomas Yee, Achim Zeileis
Maintainer: Andri Signorell <andri@signorell.net>

Diff between DescTools versions 0.99.21 dated 2017-06-29 and 0.99.22 dated 2017-09-13

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 DescTools-0.99.22/DescTools/R/Desc.R                 |   19 
 DescTools-0.99.22/DescTools/R/DescTools.r            |  557 +++++++++----------
 DescTools-0.99.22/DescTools/R/Dialogs.r              |    7 
 DescTools-0.99.22/DescTools/R/LinMod.R               |    8 
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New package AurieLSHGaussian with initial version 0.1.0
Package: AurieLSHGaussian
Type: Package
Title: Creates a Neighbourhood Using Locality Sensitive Hashing for Gaussian Projections
Version: 0.1.0
Author: Aritra Banerjee
Maintainer: Aritra Banerjee <aritra306@gmail.com>
Description: Uses locality sensitive hashing and creates a neighbourhood graph for a data set and calculates the adjusted rank index value for the same. It uses Gaussian random planes to decide the nature of a given point. Datar, Mayur, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni(2004) <doi:10.1145/997817.997857>.
License: GPL-2
Encoding: UTF-8
LazyData: TRUE
Depends: flexclust, igraph, stringdist, reshape2, lsa
Imports: stats
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-09-12 19:07:48 UTC; User
Repository: CRAN
Date/Publication: 2017-09-13 08:27:17 UTC

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Package aroma.core updated to version 3.1.1 with previous version 3.1.0 dated 2017-03-23

Title: Core Methods and Classes Used by 'aroma.*' Packages Part of the Aroma Framework
Description: Core methods and classes used by higher-level aroma.* packages part of the Aroma Project, e.g. aroma.affymetrix and aroma.cn.
Author: Henrik Bengtsson [aut, cre, cph], Mark Robinson [ctb], Ken Simpson [ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between aroma.core versions 3.1.0 dated 2017-03-23 and 3.1.1 dated 2017-09-13

 DESCRIPTION                     |   12 ++++++------
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 4 files changed, 19 insertions(+), 11 deletions(-)

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