Fri, 10 Aug 2018

Package MAINT.Data updated to version 1.2.3 with previous version 1.2.2 dated 2018-06-30

Title: Model and Analyse Interval Data
Description: Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix that takes into account the intrinsic nature of interval data, and lead to four different possible configuration structures. The Skew-Normal parameters can be estimated by maximum likelihood, while Normal parameters may be estimated by maximum likelihood or robust trimmed maximum likelihood methods.
Author: Pedro Duarte Silva <psilva@porto.ucp.pt>, Paula Brito <mpbrito.fep.up.pt>
Maintainer: Pedro Duarte Silva <psilva@porto.ucp.pt>

Diff between MAINT.Data versions 1.2.2 dated 2018-06-30 and 1.2.3 dated 2018-08-10

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Package tesseract updated to version 2.3 with previous version 2.2 dated 2018-07-10

Title: Open Source OCR Engine
Description: Bindings to 'Tesseract' <https://opensource.google.com/projects/tesseract>: a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between tesseract versions 2.2 dated 2018-07-10 and 2.3 dated 2018-08-10

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Package RSNNS updated to version 0.4-11 with previous version 0.4-10.1 dated 2018-08-10

Title: Neural Networks using the Stuttgart Neural Network Simulator (SNNS)
Description: The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.
Author: Christoph Bergmeir [aut, cre, cph], José M. Benítez [ths], Andreas Zell [ctb] (Part of original SNNS development team), Niels Mache [ctb] (Part of original SNNS development team), Günter Mamier [ctb] (Part of original SNNS development team), Michael Vogt [ctb] (Part of original SNNS development team), Sven Döring [ctb] (Part of original SNNS development team), Ralf Hübner [ctb] (Part of original SNNS development team), Kai-Uwe Herrmann [ctb] (Part of original SNNS development team), Tobias Soyez [ctb] (Part of original SNNS development team), Michael Schmalzl [ctb] (Part of original SNNS development team), Tilman Sommer [ctb] (Part of original SNNS development team), Artemis Hatzigeorgiou [ctb] (Part of original SNNS development team), Dietmar Posselt [ctb] (Part of original SNNS development team), Tobias Schreiner [ctb] (Part of original SNNS development team), Bernward Kett [ctb] (Part of original SNNS development team), Martin Reczko [ctb] (Part of original SNNS external contributors), Martin Riedmiller [ctb] (Part of original SNNS external contributors), Mark Seemann [ctb] (Part of original SNNS external contributors), Marcus Ritt [ctb] (Part of original SNNS external contributors), Jamie DeCoster [ctb] (Part of original SNNS external contributors), Jochen Biedermann [ctb] (Part of original SNNS external contributors), Joachim Danz [ctb] (Part of original SNNS development team), Christian Wehrfritz [ctb] (Part of original SNNS development team), Patrick Kursawe [ctb] (Contributors to SNNS Version 4.3), Andre El-Ama [ctb] (Contributors to SNNS Version 4.3)
Maintainer: Christoph Bergmeir <c.bergmeir@decsai.ugr.es>

Diff between RSNNS versions 0.4-10.1 dated 2018-08-10 and 0.4-11 dated 2018-08-10

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Package RcppCWB updated to version 0.2.5 with previous version 0.2.4 dated 2018-06-15

Title: 'Rcpp' Bindings for the 'Corpus Workbench' ('CWB')
Description: 'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (<http://cwb.sourceforge.net>). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (<https://www.r-project.org/ Licenses/GPL-3>). The 'CWB' relies on 'pcre' (BSD license, see <https://www.pcre.org/ licence.txt>) and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en. html>). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'.
Author: Andreas Blaette [aut, cre], Bernard Desgraupes [aut], Sylvain Loiseau [aut], Oliver Christ [ctb], Bruno Maximilian Schulze [ctb], Stefan Evert [ctb], Arne Fitschen [ctb]
Maintainer: Andreas Blaette <andreas.blaette@uni-due.de>

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Package nonlinearTseries updated to version 0.2.4 with previous version 0.2.3 dated 2015-07-25

Title: Nonlinear Time Series Analysis
Description: Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Author: Constantino A. Garcia [aut, cre], Gunther Sawitzki [ctb]
Maintainer: Constantino A. Garcia <constantino.garciama@ceu.es>

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Package kernlab updated to version 0.9-27 with previous version 0.9-26 dated 2018-04-30

Title: Kernel-Based Machine Learning Lab
Description: Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.
Author: Alexandros Karatzoglou [aut, cre], Alex Smola [aut], Kurt Hornik [aut], National ICT Australia (NICTA) [cph], Michael A. Maniscalco [ctb, cph], Choon Hui Teo [ctb]
Maintainer: Alexandros Karatzoglou <alexandros.karatzoglou@gmail.com>

Diff between kernlab versions 0.9-26 dated 2018-04-30 and 0.9-27 dated 2018-08-10

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Package ModelMetrics updated to version 1.2.0 with previous version 1.1.0 dated 2016-08-26

Title: Rapid Calculation of Model Metrics
Description: Collection of metrics for evaluating models written in C++ using 'Rcpp'.
Author: Tyler Hunt [aut, cre]
Maintainer: Tyler Hunt <thunt@snapfinance.com>

Diff between ModelMetrics versions 1.1.0 dated 2016-08-26 and 1.2.0 dated 2018-08-10

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Package lava updated to version 1.6.3 with previous version 1.6.2 dated 2018-07-02

Title: Latent Variable Models
Description: A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (two-stage)... The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
Author: Klaus K. Holst [aut, cre], Brice Ozenne [ctb], Thomas Gerds [ctb]
Maintainer: Klaus K. Holst <klaus@holst.it>

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 lava-1.6.3/lava/R/estimate.lvm.R        |  129 +--
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 lava-1.6.3/lava/R/finalize.R            |  382 ++++-----
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 lava-1.6.3/lava/R/pcor.R                |   15 
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 lava-1.6.3/lava/R/predict.R             |    7 
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 lava-1.6.3/lava/R/revdiag.R             |    2 
 lava-1.6.3/lava/R/score.R               |    1 
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 lava-1.6.3/lava/R/subgraph.R            |    1 
 lava-1.6.3/lava/R/twostage.R            |  121 ++-
 lava-1.6.3/lava/R/utils.R               |   34 
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 lava-1.6.3/lava/R/zib.R                 |  451 +++++------
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Package atlas updated to version 1.0.0 with previous version 0.6.0 dated 2018-06-01

Title: Stanford 'ATLAS' Search Engine API
Description: Stanford 'ATLAS' (Advanced Temporal Search Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. This package is designed to interface directly with an instance of 'ATLAS' search engine and facilitates API queries and data dumps. Prerequisite is a good knowledge of the temporal language to be able to efficiently construct a query. More information available at <https://shahlab.stanford.edu/start>.
Author: Vladimir Polony
Maintainer: Vladimir Polony <podalv@gmail.com>

Diff between atlas versions 0.6.0 dated 2018-06-01 and 1.0.0 dated 2018-08-10

 DESCRIPTION           |    6 +++---
 MD5                   |   14 +++++++++-----
 NAMESPACE             |    3 +++
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 10 files changed, 23 insertions(+), 18 deletions(-)

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New package telefit with initial version 1.0.0
Package: telefit
Type: Package
Title: Estimation and Prediction for Remote Effects Spatial Process Models
Version: 1.0.0
Date: 2018-07-09
Author: Joshua Hewitt
Maintainer: Joshua Hewitt <joshua.hewitt@colostate.edu>
Description: Implementation of the remote effects spatial process (RESP) model for teleconnection. The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures). The RESP model is introduced in Hewitt et al. (2018) <arXiv:1612.06303>. Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>.
License: GPL-3
Depends: R (>= 3.0.2)
Imports: abind, coda, cowplot, doRNG, dplyr, fields, itertools, mvtnorm, raster, scoringRules, stringr, foreach, ggplot2, gtable, reshape2, scales, sp, SDMTools
LinkingTo: Rcpp (>= 0.12.4), RcppArmadillo, RcppEigen (>= 0.3.3.3.1)
RoxygenNote: 6.0.1
Suggests: testthat
LazyData: true
SystemRequirements: A system with a recent-enough C++11 compiler (such as g++-4.8 or later).
NeedsCompilation: yes
Packaged: 2018-08-07 14:37:34 UTC; pointdex
Repository: CRAN
Date/Publication: 2018-08-10 16:30:02 UTC

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Package RQuantLib updated to version 0.4.5 with previous version 0.4.4 dated 2017-11-08

Title: R Interface to the 'QuantLib' Library
Description: The 'RQuantLib' package makes parts of 'QuantLib' accessible from R The 'QuantLib' project aims to provide a comprehensive software framework for quantitative finance. The goal is to provide a standard open source library for quantitative analysis, modeling, trading, and risk management of financial assets.
Author: Dirk Eddelbuettel, Khanh Nguyen (2009-2010), Terry Leitch (since 2016)
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RQuantLib versions 0.4.4 dated 2017-11-08 and 0.4.5 dated 2018-08-10

 RQuantLib-0.4.4/RQuantLib/src/rquantlib.h                      |only
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 RQuantLib-0.4.5/RQuantLib/DESCRIPTION                          |    9 
 RQuantLib-0.4.5/RQuantLib/MD5                                  |   65 ++---
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 RQuantLib-0.4.5/RQuantLib/src/RcppExports.cpp                  |  120 ++++++++++
 RQuantLib-0.4.5/RQuantLib/src/affine.cpp                       |    9 
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 RQuantLib-0.4.5/RQuantLib/src/modules.cpp                      |    6 
 RQuantLib-0.4.5/RQuantLib/src/sabr.cpp                         |   10 
 RQuantLib-0.4.5/RQuantLib/src/schedule.cpp                     |    3 
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 RQuantLib-0.4.5/RQuantLib/src/vanilla.cpp                      |    4 
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 35 files changed, 453 insertions(+), 174 deletions(-)

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New package esDesign with initial version 1.0.0
Package: esDesign
Type: Package
Title: Adaptive Enrichment Designs with Sample Size Re-Estimation
Version: 1.0.0
Date: 2018-07-27
Author: Zhao Yang, Ruitao Lin, Guosheng Yin and Ying Yuan
Maintainer: Zhao Yang <yangz98@connect.hku.hk>
Description: Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation. In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995) <doi:10.2307/2533262>, and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.
Depends: R (>= 3.2.0)
Imports: stats
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-08-07 17:45:54 UTC; yangzhao
Repository: CRAN
Date/Publication: 2018-08-10 16:30:06 UTC

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Package dnet updated to version 1.1.4 with previous version 1.1.3 dated 2018-02-23

Title: Integrative Analysis of Omics Data in Terms of Network, Evolution and Ontology
Description: The focus of the dnet by Fang and Gough (2014) <doi:10.1186/s13073-014-0064-8> is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.
Author: Hai Fang and Julian Gough
Maintainer: Hai Fang <hfang@well.ox.ac.uk>

Diff between dnet versions 1.1.3 dated 2018-02-23 and 1.1.4 dated 2018-08-10

 DESCRIPTION               |    8 ++++----
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Package BGGE updated to version 0.6.5 with previous version 0.6.4 dated 2018-07-23

Title: Bayesian Genomic Linear Models Applied to GE Genome Selection
Description: Application of genome prediction for a continuous variable, focused on genotype by environment (GE) genomic selection models (GS). It consists a group of functions that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) <doi:10.1007/s00122-013-2243-1> and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches. The prediction function uses an orthogonal transformation of the data and specific priors present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.
Author: Italo Granato [aut, cre], Luna-Vázquez Francisco J. [aut], Cuevas Jaime [aut]
Maintainer: Italo Granato <italo.granato@gmail.com>

Diff between BGGE versions 0.6.4 dated 2018-07-23 and 0.6.5 dated 2018-08-10

 DESCRIPTION |    8 ++++----
 MD5         |    4 ++--
 R/BGGE.R    |    2 --
 3 files changed, 6 insertions(+), 8 deletions(-)

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Package XML updated to version 3.98-1.15 with previous version 3.98-1.14 dated 2018-08-09

Title: Tools for Parsing and Generating XML Within R and S-Plus
Description: Many approaches for both reading and creating XML (and HTML) documents (including DTDs), both local and accessible via HTTP or FTP. Also offers access to an 'XPath' "interpreter".
Author: Duncan Temple Lang and the CRAN Team
Maintainer: ORPHANED

Diff between XML versions 3.98-1.14 dated 2018-08-09 and 3.98-1.15 dated 2018-08-10

 DESCRIPTION  |    9 +++------
 MD5          |    6 +++---
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 4 files changed, 24 insertions(+), 16 deletions(-)

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Package imp4p updated to version 0.7 with previous version 0.6 dated 2018-06-27

Title: Imputation for Proteomics
Description: Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.
Author: Quentin Giai Gianetto
Maintainer: Quentin Giai Gianetto <quentin2g@yahoo.fr>

Diff between imp4p versions 0.6 dated 2018-06-27 and 0.7 dated 2018-08-10

 DESCRIPTION       |    8 ++++----
 MD5               |   14 ++++++++------
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Package genBaRcode updated to version 1.1.0 with previous version 1.0.0 dated 2017-09-21

Title: Analysis and Visualization Tools for Genetic Barcode Data
Description: Provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).
Author: Lars Thielecke <lars.thielecke@tu-dresden.de>
Maintainer: Lars Thielecke <lars.thielecke@tu-dresden.de>

Diff between genBaRcode versions 1.0.0 dated 2017-09-21 and 1.1.0 dated 2018-08-10

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Package MCMCprecision updated to version 0.3.9 with previous version 0.3.8 dated 2018-04-08

Title: Precision of Discrete Parameters in Transdimensional MCMC
Description: Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2018, Statistics & Computing) <doi:10.1007/s11222-018-9828-0> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.
Author: Daniel W. Heck [aut, cre]
Maintainer: Daniel W. Heck <heck@uni-mannheim.de>

Diff between MCMCprecision versions 0.3.8 dated 2018-04-08 and 0.3.9 dated 2018-08-10

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Package libstableR updated to version 1.0.2 with previous version 1.0.1 dated 2018-08-05

Title: Fast and Accurate Evaluation, Random Number Generation and Parameter Estimation of Skew Stable Distributions
Description: Tools for fast and accurate evaluation of skew stable distributions (CDF, PDF and quantile functions), random number generation and parameter estimation.
Author: Javier Royuela del Val [aut, cre], Federico Simmross-Wattenberg [aut], Carlos Alberola López [aut], Bob Rudis [ctb] (Several bugs fixed and added macOS compatibility), Bruce Swihart [ctb] (Several bugs fixed and added macOS compatibility)
Maintainer: Javier Royuela del Val <jroyval@lpi.tel.uva.es>

Diff between libstableR versions 1.0.1 dated 2018-08-05 and 1.0.2 dated 2018-08-10

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Package RcmdrPlugin.SCDA updated to version 1.1.1 with previous version 1.1 dated 2015-01-27

Title: Rcmdr Plugin for Designing and Analyzing Single-Case Experiments
Description: Provides a GUI for the SCVA, SCRT and SCMA packages as described in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package is written as an Rcmdr plugin.
Author: Isis Bulte and Patrick Onghena
Maintainer: Tamal Kumar De <tamalkumar.de@kuleuven.be>

Diff between RcmdrPlugin.SCDA versions 1.1 dated 2015-01-27 and 1.1.1 dated 2018-08-10

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Package ps updated to version 1.1.0 with previous version 1.0.0 dated 2018-07-22

Title: List, Query, Manipulate System Processes
Description: List, query and manipulate all system processes, on 'Windows', 'Linux' and 'macOS'.
Author: Jay Loden [aut], Dave Daeschler [aut], Giampaolo Rodola' [aut], Gábor Csárdi [aut, cre], RStudio [cph]
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

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Package BrailleR updated to version 0.29.1 with previous version 0.28.0 dated 2018-06-05

Title: Improved Access for Blind Users
Description: Blind users do not have access to the graphical output from R without printing the content of graphics windows to an embosser of some kind. This is not as immediate as is required for efficient access to statistical output. The functions here are created so that blind people can make even better use of R. This includes the text descriptions of graphs, convenience functions to replace the functionality offered in many GUI front ends, and experimental functionality for optimising graphical content to prepare it for embossing as tactile images.
Author: A. Jonathan R. Godfrey [aut, cre], Debra Warren [aut], Donal Fitzpatrick [ctb], Duncan Murdoch [ctb], Greg Snow [ctb], Henrik Bengtsson [ctb], James Curtis [ctb], JooYoung Seo [ctb], Marshall Flax [ctb], Paul Murrell [aut], Timothy Bilton [aut], Tony Hirst [ctb], Tsan-Kuang Lee [ctb], Volker Sorge [aut], Yihui Xie [ctb]
Maintainer: A. Jonathan R. Godfrey <a.j.godfrey@massey.ac.nz>

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Package abn updated to version 1.2 with previous version 1.0.2 dated 2016-11-09

Title: Modelling Multivariate Data with Additive Bayesian Networks
Description: Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. 'abn' provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data. The additive formulation of these models is equivalent to multivariate generalised linear modelling (including mixed models with iid random effects). The usual term to describe this model selection process is structure discovery. The core functionality is concerned with model selection - determining the most robust empirical model of data from interdependent variables. Laplace approximations are used to estimate goodness of fit metrics and model parameters, and wrappers are also included to the INLA package which can be obtained from <http://www.r-inla.org>. A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website.
Author: Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>), Fraser Ian Lewis [aut], Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>), Marta Pittavino [ctb] (<https://orcid.org/0000-0002-1232-1034>)
Maintainer: Gilles Kratzer <gilles.kratzer@math.uzh.ch>

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Package spGARCH updated to version 0.1.6 with previous version 0.1.5 dated 2018-06-29

Title: Spatial ARCH and GARCH Models (spGARCH)
Description: A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) <arXiv:1609.00711>: simulation of spatial ARCH-type processes (spARCH, exponential spARCH, complex spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations.
Author: Philipp Otto [cre, aut] (<https://orcid.org/0000-0002-9796-6682>)
Maintainer: Philipp Otto <potto@europa-uni.de>

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Package photobiologyInOut updated to version 0.4.15 with previous version 0.4.14 dated 2018-04-03

Title: Read Spectral and Logged Data from Foreign Files
Description: Functions for reading, and in some cases writing, foreign files containing spectral data from spectrometers and their associated software, output from daylight simulation models in common use, and some spectral data repositories. As well as functions for exchange of spectral data with other R packages. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Author: Pedro J. Aphalo [aut, cre] (<https://orcid.org/0000-0003-3385-972X>), Titta K. Kotilainen [ctb] (<https://orcid.org/0000-0002-2822-9734>), Glenn Davis [ctb]
Maintainer: Pedro J. Aphalo <pedro.aphalo@helsinki.fi>

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Package mirtCAT updated to version 1.7.1 with previous version 1.7 dated 2018-03-11

Title: Computerized Adaptive Testing with Multidimensional Item Response Theory
Description: Provides tools to generate an HTML interface for creating adaptive and non-adaptive educational and psychological tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests which contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying the behavior of computerized adaptive test banks.
Author: Phil Chalmers [aut, cre]
Maintainer: Phil Chalmers <rphilip.chalmers@gmail.com>

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Package SnakeCharmR updated to version 1.0.7.1 with previous version 1.0.7 dated 2017-10-07

Title: R and Python Integration
Description: Run 'Python' code, make function calls, assign and retrieve variables, etc. from R. A fork from 'rPython' which uses 'jsonlite', 'Rcpp' and has several fixes and improvements.
Author: Alexandre Sieira, forked off of rPython by Carlos J. Gil Bellosta
Maintainer: Alexandre Sieira <alexandre.sieira@gmail.com>

Diff between SnakeCharmR versions 1.0.7 dated 2017-10-07 and 1.0.7.1 dated 2018-08-10

 DESCRIPTION    |    6 +++---
 MD5            |    4 ++--
 src/python.cpp |    3 +++
 3 files changed, 8 insertions(+), 5 deletions(-)

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Package smoothSurv updated to version 2.0.1 with previous version 2.0 dated 2017-09-24

Title: Survival Regression with Smoothed Error Distribution
Description: Contains, as a main contribution, a function to fit a regression model with possibly right, left or interval censored observations and with the error distribution expressed as a mixture of G-splines. Core part of the computation is done in compiled C++ written using the Scythe Statistical Library Version 0.3.
Author: Arnošt Komárek <arnost.komarek@mff.cuni.cz>
Maintainer: ORPHANED

Diff between smoothSurv versions 2.0 dated 2017-09-24 and 2.0.1 dated 2018-08-10

 DESCRIPTION        |   11 +++++++----
 MD5                |    4 ++--
 src/Scythe_IDE.cpp |   26 +++++++++++++-------------
 3 files changed, 22 insertions(+), 19 deletions(-)

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Package RSNNS updated to version 0.4-10.1 with previous version 0.4-10 dated 2017-12-10

Title: Neural Networks using the Stuttgart Neural Network Simulator (SNNS)
Description: The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.
Author: Christoph Bergmeir [aut, cre, cph], José M. Benítez [ths], Andreas Zell [ctb] (Part of original SNNS development team), Niels Mache [ctb] (Part of original SNNS development team), Günter Mamier [ctb] (Part of original SNNS development team), Michael Vogt [ctb] (Part of original SNNS development team), Sven Döring [ctb] (Part of original SNNS development team), Ralf Hübner [ctb] (Part of original SNNS development team), Kai-Uwe Herrmann [ctb] (Part of original SNNS development team), Tobias Soyez [ctb] (Part of original SNNS development team), Michael Schmalzl [ctb] (Part of original SNNS development team), Tilman Sommer [ctb] (Part of original SNNS development team), Artemis Hatzigeorgiou [ctb] (Part of original SNNS development team), Dietmar Posselt [ctb] (Part of original SNNS development team), Tobias Schreiner [ctb] (Part of original SNNS development team), Bernward Kett [ctb] (Part of original SNNS development team), Martin Reczko [ctb] (Part of original SNNS external contributors), Martin Riedmiller [ctb] (Part of original SNNS external contributors), Mark Seemann [ctb] (Part of original SNNS external contributors), Marcus Ritt [ctb] (Part of original SNNS external contributors), Jamie DeCoster [ctb] (Part of original SNNS external contributors), Jochen Biedermann [ctb] (Part of original SNNS external contributors), Joachim Danz [ctb] (Part of original SNNS development team), Christian Wehrfritz [ctb] (Part of original SNNS development team), Patrick Kursawe [ctb] (Contributors to SNNS Version 4.3), Andre El-Ama [ctb] (Contributors to SNNS Version 4.3)
Maintainer: Christoph Bergmeir <c.bergmeir@decsai.ugr.es>

Diff between RSNNS versions 0.4-10 dated 2017-12-10 and 0.4-10.1 dated 2018-08-10

 DESCRIPTION                |    6 
 MD5                        |   62 +++----
 src/arttr_f.cpp            |   26 +-
 src/arttr_f.h              |    2 
 src/cc_glob.cpp            |    6 
 src/cc_learn.cpp           |   12 -
 src/func_mac.h             |    6 
 src/init_f.cpp             |  220 ++++++++++++-------------
 src/kernel.cpp             |  112 ++++++------
 src/kr_JordElm.cpp         |    2 
 src/kr_amap.cpp            |  136 +++++++--------
 src/kr_art.cpp             |    8 
 src/kr_art1.cpp            |   60 +++---
 src/kr_art2.cpp            |   98 +++++------
 src/kr_inversion.cpp       |   16 -
 src/kr_io.cpp              |   24 +-
 src/kr_mem.cpp             |   18 +-
 src/kr_newpattern.cpp      |   36 ++--
 src/kr_pat_scan.cpp        |   42 ++--
 src/kr_td.cpp              |   58 +++---
 src/kr_ui.cpp              |   32 +--
 src/learn_f.cpp            |  392 ++++++++++++++++++++++-----------------------
 src/matrix.cpp             |   30 +--
 src/prun_f.cpp             |   60 +++---
 src/remap_f.cpp            |   28 +--
 src/scaled_conj_grad.cpp   |    8 
 src/stochastic_learn_f.cpp |   34 +--
 src/tacoma_learn.cpp       |    6 
 src/tbl_func.cpp           |    4 
 src/trans_f.cpp            |  146 ++++++++--------
 src/trans_f.h              |    8 
 src/update_f.cpp           |   82 ++++-----
 32 files changed, 890 insertions(+), 890 deletions(-)

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Package RANN.L1 updated to version 2.5.1 with previous version 2.5 dated 2015-05-04

Title: Fast Nearest Neighbour Search (Wraps ANN Library) Using L1 Metric
Description: Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L1 (Manhattan, taxicab) metric. Please see package 'RANN' for the same functionality using the L2 (Euclidean) metric.
Author: Sunil Arya and David Mount (for ANN), Samuel E. Kemp, Gregory Jefferis, Kirill Müller
Maintainer: ORPHANED

Diff between RANN.L1 versions 2.5 dated 2015-05-04 and 2.5.1 dated 2018-08-10

 DESCRIPTION               |   11 +++++++----
 MD5                       |   16 ++++++++--------
 src/ANN.cpp               |    6 +++---
 src/kd_fix_rad_search.cpp |   10 +++++-----
 src/kd_pr_search.cpp      |   12 ++++++------
 src/kd_search.cpp         |   12 ++++++------
 src/kd_util.cpp           |   10 +++++-----
 src/pr_queue.h            |   10 +++++-----
 src/pr_queue_k.h          |    2 +-
 9 files changed, 46 insertions(+), 43 deletions(-)

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Package FNN updated to version 1.1.2.1 with previous version 1.1.2 dated 2018-08-02

Title: Fast Nearest Neighbor Search Algorithms and Applications
Description: Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications including KNN classification, regression and information measures are implemented.
Author: Alina Beygelzimer, Sham Kakadet and John Langford (cover tree library), Sunil Arya and David Mount (ANN library 1.1.2 for the kd-tree approach), Shengqiao Li
Maintainer: ORPHANED

Diff between FNN versions 1.1.2 dated 2018-08-02 and 1.1.2.1 dated 2018-08-10

 DESCRIPTION               |   11 +++++++----
 MD5                       |   24 ++++++++++++------------
 src/ANN.cpp               |    6 +++---
 src/KNN_ANN.cpp           |    2 +-
 src/KNN_cover_tree.cpp    |    2 +-
 src/cover_tree.h          |    2 +-
 src/kd_fix_rad_search.cpp |   10 +++++-----
 src/kd_pr_search.cpp      |   12 ++++++------
 src/kd_search.cpp         |   12 ++++++------
 src/kd_util.cpp           |   10 +++++-----
 src/label_point.cpp       |    6 +++---
 src/pr_queue.h            |   10 +++++-----
 src/pr_queue_k.h          |    2 +-
 13 files changed, 56 insertions(+), 53 deletions(-)

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Package TransferEntropy (with last version 1.4) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2016-04-26 1.4
2016-01-01 1.2
2015-12-30 1.1
2015-12-26 1.0

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Package KODAMA (with last version 1.4) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2017-01-16 1.4
2016-12-19 1.3
2016-12-13 1.2
2016-10-19 1.1
2016-10-12 1.0
2014-11-25 0.0.1

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Package msr (with last version 0.4.4) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2015-11-12 0.4.4
2012-07-11 0.4.1
2012-04-05 0.4
2011-11-13 0.3
2011-02-18 0.0
2011-02-18 0.1

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Package ltsk (with last version 1.0.6) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2018-07-26 1.0.6
2018-01-03 1.0.5
2015-09-11 1.0.4
2015-01-26 1.0.3
2015-01-22 1.0.2
2014-02-06 1.0.1

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Package NPBayesImpute (with last version 0.6) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2016-02-09 0.6
2015-03-23 0.5

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Package pubh updated to version 0.4.1 with previous version 0.3.0 dated 2018-07-11

Title: A Toolbox for Public Health and Epidemiology
Description: A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand lattice plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.
Author: Josie Athens [aut, cre], Deepayan Sarkar [ctb], Mark Stevenson [ctb], Simon Firestone [ctb], Cord Heuer [ctb], Jim Robison-Cox [ctb], Frank Harell [ctb], John Fox [ctb], Kazuki Yoshida [ctb], Tomas Aragon [ctb], Johan Larsson [ctb], R-Core [ctb]
Maintainer: Josie Athens <josie.athens@otago.ac.nz>

Diff between pubh versions 0.3.0 dated 2018-07-11 and 0.4.1 dated 2018-08-10

 DESCRIPTION                                           |   10 
 MD5                                                   |   32 
 NAMESPACE                                             |    3 
 R/data.R                                              |    2 
 R/display_functions.R                                 |  620 ++++---
 R/plot_functions.R                                    |   16 
 inst/doc/introduction.R                               |   21 
 inst/doc/introduction.Rmd                             |   23 
 inst/doc/introduction.html                            |  913 ++++++----
 inst/doc/regression.R                                 |   57 
 inst/doc/regression.Rmd                               |   59 
 inst/doc/regression.html                              | 1536 ++++++------------
 inst/rmarkdown/templates/report/skeleton/skeleton.Rmd |    1 
 man/Thall.Rd                                          |    2 
 man/glm_coef.Rd                                       |   24 
 vignettes/introduction.Rmd                            |   23 
 vignettes/regression.Rmd                              |   59 
 17 files changed, 1677 insertions(+), 1724 deletions(-)

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