Mon, 13 Jan 2020

Package cmpprocess updated to version 1.1 with previous version 1.0 dated 2017-03-12

Title: Flexible Modeling of Count Processes
Description: A toolkit for flexible modeling of count processes where data (over- or under-) dispersion exists. Estimations can be obtained under two data constructs where one has: (1) data on number of events in an s-unit time interval, or (2) only wait-time data. This package is supplementary to the work set forth in Zhu et al. (2016) <doi:10.1080/00031305.2016.1234976>.
Author: Li Zhu [aut], Kimberly Sellers [aut], Darcy Morris [aut], Galit Shmueli [aut], Diag Davenport [aut, cre]
Maintainer: Diag Davenport <diag.davenport@gmail.com>

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More information about cmpprocess at CRAN
Permanent link

Package ypr updated to version 0.4.0 with previous version 0.3.1 dated 2019-04-26

Title: Yield Per Recruit
Description: An implementation of equilibrium-based yield per recruit methods. Yield per recruit methods can used to estimate the optimal yield for a fish population as described by Walters and Martell (2004) <isbn:0-691-11544-3>. The yield can be based on the number of fish caught (or harvested) or biomass caught for all fish or just large (trophy) individuals.
Author: Joe Thorley [aut, cre] (<https://orcid.org/0000-0002-7683-4592>), Poisson Consulting [cph, fnd]
Maintainer: Joe Thorley <joe@poissonconsulting.ca>

Diff between ypr versions 0.3.1 dated 2019-04-26 and 0.4.0 dated 2020-01-13

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Permanent link

Package SubgrPlots updated to version 0.1.2 with previous version 0.1.0 dated 2018-07-10

Title: Graphical Displays for Subgroup Analysis in Clinical Trials
Description: Provides functions for obtaining a variety of graphical displays that may be useful in the subgroup analysis setting. An example with a prostate cancer dataset is provided. The graphical techniques considered include level plots, mosaic plots, contour plots, bar charts, Venn diagrams, tree plots, forest plots, Galbraith plots, L'Abbé plots, the subpopulation treatment effect pattern plot, alluvial plots, circle plots and UpSet plots.
Author: Nicolas Ballarini [aut, cre], Yi-Da Chiu [aut], Jake Conway [ctb], Nils Gehlenborg [ctb], Michal Bojanowski [ctb], Robin Edwards [ctb]
Maintainer: Nicolas Ballarini <nicoballarini@gmail.com>

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New package tidyREDCap with initial version 0.1.0
Package: tidyREDCap
Title: Helper Functions for Working with REDCap Data
Version: 0.1.0
Authors@R: c(person(given = "Raymond", family = "Balise", role = c("aut", "cre"), email = "balise@miami.edu", comment = c(ORCID = "0000-0002-9856-5901")), person(given = "Gabriel", family = "Odom", role = "aut", email = "gabriel.odom@fiu.edu", comment = c(ORCID = "0000-0003-1341-4555")) )
Description: Helper functions for processing REDCap data in R. 'REDCap' (Research Electronic Data CAPture; <https://projectredcap.org>) is a web-enabled application for building and managing surveys and databases developed at Vanderbilt University.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Depends: R (>= 3.5.0)
Imports: purrr, stringr, tibble, magrittr
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, dplyr, redcapAPI, janitor
VignetteBuilder: knitr
URL: https://raymondbalise.github.io/tidyREDCap/index.html
BugReports: https://github.com/RaymondBalise/tidyREDCap/issues
NeedsCompilation: no
Packaged: 2020-01-10 15:53:57 UTC; rrb28
Author: Raymond Balise [aut, cre] (<https://orcid.org/0000-0002-9856-5901>), Gabriel Odom [aut] (<https://orcid.org/0000-0003-1341-4555>)
Maintainer: Raymond Balise <balise@miami.edu>
Repository: CRAN
Date/Publication: 2020-01-13 23:40:03 UTC

More information about tidyREDCap at CRAN
Permanent link

Package RJafroc updated to version 1.3.1 with previous version 1.2.0 dated 2019-07-31

Title: Analyzing Diagnostic Observer Performance Studies
Description: Implements software for assessing medical imaging systems, radiologists or computer aided detection algorithms. Models of observer performance are implemented, including the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). The software and applications are described in a book - Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples. Taylor-Francis LLC; 2017 - and its vignettes <https://dpc10ster.github.io/RJafroc/>. Observer performance data collection paradigms are the receiver operating characteristic (ROC) and its location specific extensions, primarily free-response ROC (FROC) and the location ROC (LROC). ROC data consists of single ratings per images. A rating is the perceived confidence level that the image is that of a diseased patient. FROC data consists of a variable number (including zero) of mark-rating pairs per image, where a mark is the location of a clinically reportable suspicious region and the rating is the corresponding confidence level that it is a real lesion. LROC data consists of a rating and a forced localization of the most suspicious region on every image. RJafroc supersedes the Windows version of JAFROC software V4.2.1, <http://www.devchakraborty.com>:. Package functions are organized as follows. Data file related function names are preceded by Df, curve fitting functions by Fit, included data sets by dataset, plotting functions by Plot, significance testing functions by St, sample size related functions by Ss, data simulation functions by Simulate and utility functions by Util. Implemented are figures of merit (FOMs) for quantifying performance, functions for visualizing empirical operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC (wAFROC) curves. Four maximum likelihood curve-fitting algorithms are implemented: the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM) and the radiological search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM predict "proper" ROC curves that do not cross the chance diagonal. RSM fitting additionally yields measures of search and lesion-classification performances. Search performance is the ability to find lesions while avoiding finding non-lesions. Lesion-classification performance is the ability to correctly classify found lesions from found non-lesions. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is implemented via both Dorfman-Berbaum-Metz and the Obuchowski-Rockette methods, including Hillis' extensions. Also implemented are single treatment analyses, which allow comparison of performance of a group of radiologists to a specified value, or comparison to CAD to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed treatment factors and the desire is to determined performance in each treatment factor averaged over all levels of the other factor. Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study to predict required numbers of readers and cases in a pivotal study to achieve a desired power. Utility and data file manipulation functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be viewed in text or Excel output files. The methods are illustrated with several included datasets from the author's international collaborations. This version corrects a few bugs noticed by users and extends the Excel file input format for greater flexibility in handling non-crossed datasets and the sample size routines have been rewritten for ease of use.
Author: Dev Chakraborty [cre, aut, cph], Peter Philips [aut], Xuetong Zhai [aut], Lucy D'Agostino McGowan [ctb], Alejandro RodriguezRuiz [ctb]
Maintainer: Dev Chakraborty <dpc10ster@gmail.com>

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More information about RJafroc at CRAN
Permanent link

Package bench updated to version 1.1.1 with previous version 1.1.0 dated 2020-01-09

Title: High Precision Timing of R Expressions
Description: Tools to accurately benchmark and analyze execution times for R expressions.
Author: Jim Hester [aut, cre], Drew Schmidt [ctb] (read_proc_file implementation)
Maintainer: Jim Hester <james.f.hester@gmail.com>

Diff between bench versions 1.1.0 dated 2020-01-09 and 1.1.1 dated 2020-01-13

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More information about bench at CRAN
Permanent link

Package vroom updated to version 1.2.0 with previous version 1.0.2 dated 2019-06-28

Title: Read and Write Rectangular Text Data Quickly
Description: The goal of 'vroom' is to read and write data (like 'csv', 'tsv' and 'fwf') quickly. When reading it uses a quick initial indexing step, then reads the values lazily , so only the data you actually use needs to be read. The writer formats the data in parallel and writes to disk asynchronously from formatting.
Author: Jim Hester [aut, cre] (<https://orcid.org/0000-0002-2739-7082>), Hadley Wickham [aut] (<https://orcid.org/0000-0003-4757-117X>), https://github.com/mandreyel/ [cph] (mio library), R Core Team [ctb] (localtime.c code adapted from R), Jukka Jylänki [cph] (grisu3 implementation), Mikkel Jørgensen [cph] (grisu3 implementation), RStudio [cph, fnd]
Maintainer: Jim Hester <james.f.hester@gmail.com>

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 vroom-1.2.0/vroom/src/vroom_dttm.cc                    |    2 
 vroom-1.2.0/vroom/src/vroom_dttm.h                     |    2 
 vroom-1.2.0/vroom/src/vroom_fct.cc                     |    6 
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 vroom-1.2.0/vroom/src/vroom_time.cc                    |    2 
 vroom-1.2.0/vroom/src/vroom_time.h                     |    2 
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 vroom-1.2.0/vroom/tests/testthat/test-col_types.R      |    7 
 vroom-1.2.0/vroom/tests/testthat/test-connection.R     |   17 
 vroom-1.2.0/vroom/tests/testthat/test-datetime.R       |  254 +--
 vroom-1.2.0/vroom/tests/testthat/test-dbl.R            |only
 vroom-1.2.0/vroom/tests/testthat/test-factor.R         |   12 
 vroom-1.2.0/vroom/tests/testthat/test-int.R            |   10 
 vroom-1.2.0/vroom/tests/testthat/test-path.R           |    8 
 vroom-1.2.0/vroom/tests/testthat/test-select.R         |   13 
 vroom-1.2.0/vroom/tests/testthat/test-vroom.R          |   80 -
 vroom-1.2.0/vroom/tests/testthat/test-vroom_fwf.R      |   86 +
 vroom-1.2.0/vroom/tests/testthat/test-vroom_write.R    |   30 
 vroom-1.2.0/vroom/vignettes/benchmarks.Rmd             |  179 +-
 vroom-1.2.0/vroom/vignettes/vroom.Rmd                  |  146 +-
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Package text2speech updated to version 0.2.9 with previous version 0.2.5 dated 2019-06-24

Title: Text to Speech
Description: Unifies different text to speech engines, such as Google, Microsoft, and Amazon. Text synthesis can be done in any engine with a simple switch of an argument denoting the service requested.
Author: John Muschelli [aut, cre] (<https://orcid.org/0000-0001-6469-1750>)
Maintainer: John Muschelli <muschellij2@gmail.com>

Diff between text2speech versions 0.2.5 dated 2019-06-24 and 0.2.9 dated 2020-01-13

 DESCRIPTION                  |   14 
 MD5                          |   39 
 NAMESPACE                    |    3 
 NEWS.md                      |   13 
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 23 files changed, 702 insertions(+), 1453 deletions(-)

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New package lncDIFF with initial version 1.0.0
Package: lncDIFF
Title: Long Non-Coding RNA Differential Expression Analysis
Version: 1.0.0
Authors@R: person("Qian", "Li", email = "qian.li10000@gmail.com", role = c("aut", "cre"))
Description: We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) <doi:10.1186/s12864-019-5926-4>.
Depends: R (>= 3.5.0)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.0
NeedsCompilation: no
Packaged: 2020-01-10 03:36:50 UTC; liq
Author: Qian Li [aut, cre]
Maintainer: Qian Li <qian.li10000@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 22:20:08 UTC

More information about lncDIFF at CRAN
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New package ced with initial version 1.0.0
Package: ced
Type: Package
Title: The Compact Encoding Detector
Description: R bindings of the Google Compact Encoding Detection library (<https://github.com/google/compact_enc_det>). The library takes as input a source buffer of raw text bytes and probabilistically determines the most likely encoding for that text. It was designed with accuracy, robustness, small size, and speed in mind.
Version: 1.0.0
Authors@R: c( person(given = "Artem", family = "Klevtsov", role = c("aut", "cre"), email = "a.a.klevtsov@gmail.com", comment = c(ORCID = "0000-0003-0492-6647")), person(given = "Philipp", family = "Upravitelev", role = "ctb", email = "upravitelev@gmail.com"), person("Google Inc.", role = "cph") )
License: GPL-2
Copyright: file COPYRIGHTS
URL: https://artemklevtsov.gitlab.io/ced, https://gitlab.com/artemklevtsov/ced
BugReports: https://gitlab.com/artemklevtsov/ced/issues
Depends: R (>= 3.5.0)
Imports: Rcpp
Suggests: tinytest, curl
LinkingTo: Rcpp
Encoding: UTF-8
NeedsCompilation: yes
ByteCompile: yes
RoxygenNote: 7.0.2
SystemRequirements: C++11
Packaged: 2020-01-10 06:31:12.922 UTC; unikum
Author: Artem Klevtsov [aut, cre] (<https://orcid.org/0000-0003-0492-6647>), Philipp Upravitelev [ctb], Google Inc. [cph]
Maintainer: Artem Klevtsov <a.a.klevtsov@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 23:00:02 UTC

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Package sentometrics updated to version 0.8.0 with previous version 0.7.6 dated 2019-11-01

Title: An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction
Description: Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2019) <doi:10.2139/ssrn.3067734>.
Author: Samuel Borms [aut, cre] (<https://orcid.org/0000-0001-9533-1870>), David Ardia [aut] (<https://orcid.org/0000-0003-2823-782X>), Keven Bluteau [aut] (<https://orcid.org/0000-0003-2990-4807>), Kris Boudt [aut] (<https://orcid.org/0000-0002-1000-5142>), Jeroen Van Pelt [ctb], Andres Algaba [ctb]
Maintainer: Samuel Borms <samuel.borms@unine.ch>

Diff between sentometrics versions 0.7.6 dated 2019-11-01 and 0.8.0 dated 2020-01-13

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 NAMESPACE                                   |    4 
 NEWS.md                                     |   49 ++--
 R/deprecated.R                              |    2 
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 R/sentocorpus.R                             |   35 ++-
 R/sentolexicons.R                           |    3 
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 man/subset.sento_measures.Rd                |  147 ++++++-------
 man/usnews.Rd                               |   72 +++---
 man/weights_almon.Rd                        |   69 +++---
 man/weights_beta.Rd                         |   80 +++----
 man/weights_exponential.Rd                  |   62 ++---
 src/SentimentScorerBigrams.h                |    4 
 src/SentimentScorerClusters.h               |    4 
 src/SentimentScorerOnegrams.h               |    2 
 src/SentimentScorerSentences.h              |    2 
 src/utils.h                                 |   69 +++---
 tests/testthat/test_aggregation.R           |   16 -
 tests/testthat/test_corpus_building.R       |   23 +-
 tests/testthat/test_modelling.R             |    4 
 tests/testthat/test_sentiment_computation.R |   35 ++-
 68 files changed, 2874 insertions(+), 2777 deletions(-)

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Package bdsmatrix updated to version 1.3-4 with previous version 1.3-3 dated 2017-12-21

Title: Routines for Block Diagonal Symmetric Matrices
Description: This is a special case of sparse matrices, used by coxme.
Author: Terry Therneau
Maintainer: Terry Therneau <therneau@mayo.edu>

Diff between bdsmatrix versions 1.3-3 dated 2017-12-21 and 1.3-4 dated 2020-01-13

 DESCRIPTION             |    8 ++++----
 MD5                     |    6 +++---
 R/bdsmatrix.reconcile.R |    2 +-
 inst/NEWS.Rd            |    6 ++++++
 4 files changed, 14 insertions(+), 8 deletions(-)

More information about bdsmatrix at CRAN
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Package LinRegInteractive updated to version 0.3-2 with previous version 0.3-1 dated 2015-04-23

Title: Interactive Interpretation of Linear Regression Models
Description: Interactive visualization of effects, response functions and marginal effects for different kinds of regression models. In this version linear regression models, generalized linear models, generalized additive models and linear mixed-effects models are supported. Major features are the interactive approach and the handling of the effects of categorical covariates: if two or more factors are used as covariates every combination of the levels of each factor is treated separately. The automatic calculation of marginal effects and a number of possibilities to customize the graphical output are useful features as well.
Author: Martin Meermeyer
Maintainer: Martin Meermeyer <m.meermeyer@gmail.com>

Diff between LinRegInteractive versions 0.3-1 dated 2015-04-23 and 0.3-2 dated 2020-01-13

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 21 files changed, 197 insertions(+), 187 deletions(-)

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New package vegtable with initial version 0.1.5
Package: vegtable
Version: 0.1.5
Encoding: UTF-8
Title: Handling Vegetation Data Sets
Authors@R: person("Miguel", "Alvarez", email="kamapu78@gmail.com", role=c("aut","cre"), comment=c(ORCID="0000-0003-1500-1834"))
Depends: R(>= 3.0.0), taxlist
Imports: foreign, methods, plotKML, qdapRegex, sp, stringi, vegdata
Suggests: devtools, vegan
LazyData: true
Description: Import and handling data from vegetation-plot databases, especially data stored in 'Turboveg' (<https://www.synbiosys.alterra.nl/turboveg>). Also import/export routines for exchange of data with 'Juice' (<http://www.sci.muni.cz/botany/juice>) are implemented.
License: GPL (>= 2)
URL: https://github.com/kamapu/vegtable
BugReports: https://github.com/kamapu/vegtable/issues
Collate: 'NULLing.R' 'coverconvert-class.R''vegtable-class.R''shaker-class.R' 'cross2db.R''clean.R''as.list.R''merge_taxa.R''add_releves.R' 'header.R''Extract.R''veg_relation.R''relation2header.R' 'vegtable_stat.R''df2vegtable.R''used_synonyms.R' 'subset.R' 'coverconvert-methods.R''names.R' 'tv2coverconvert.R''tv2vegtable.R' 'crosstable.R''aggregate.R''write_juice.R''read_juice.R''vegtable2kml.R' 'layers2samples.R' 'shaker-methods.R''make_cocktail.R' 'summary.R''match_names.R' 'taxa2samples.R''count_taxa.R' 'trait_proportion.R''trait_stats.R' 'StartMessage.R'
NeedsCompilation: no
Packaged: 2020-01-12 06:21:27 UTC; m_alvarez
Author: Miguel Alvarez [aut, cre] (<https://orcid.org/0000-0003-1500-1834>)
Maintainer: Miguel Alvarez <kamapu78@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 19:30:02 UTC

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Package survELtest updated to version 2.0.1 with previous version 2.0.0 dated 2020-01-08

Title: Comparing Multiple Survival Functions with Crossing Hazards
Description: Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.
Author: Hsin-wen Chang [aut, cre] <hwchang@stat.sinica.edu.tw>
Maintainer: Guo-You Lan <jj6020770416jj@gmail.com>

Diff between survELtest versions 2.0.0 dated 2020-01-08 and 2.0.1 dated 2020-01-13

 DESCRIPTION |   12 ++++++------
 MD5         |    2 +-
 2 files changed, 7 insertions(+), 7 deletions(-)

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Package SQUAREM updated to version 2020.1 with previous version 2017.10-1 dated 2017-10-07

Title: Squared Extrapolation Methods for Accelerating EM-Like Monotone Algorithms
Description: Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent acceleration scheme. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("SQUAREM").
Author: Ravi Varadhan
Maintainer: Ravi Varadhan <ravi.varadhan@jhu.edu>

Diff between SQUAREM versions 2017.10-1 dated 2017-10-07 and 2020.1 dated 2020-01-13

 DESCRIPTION          |    8 
 MD5                  |   16 
 NEWS                 |    2 
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 build/vignette.rds   |binary
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Package SimJoint updated to version 0.3.5 with previous version 0.3.4 dated 2020-01-10

Title: Simulate Joint Distribution
Description: Simulate multivariate correlated data given nonparametric marginals and their covariance structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both outperform various CRAN packages to date by substantial margins. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. This heuristic demonstrated not only strong capability of cost reduction, but also good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator from <http://www.pcg-random.org>.
Author: Charlie Wusuo Liu
Maintainer: Charlie Wusuo Liu <liuwusuo@gmail.com>

Diff between SimJoint versions 0.3.4 dated 2020-01-10 and 0.3.5 dated 2020-01-13

 DESCRIPTION               |    6 +--
 MD5                       |   24 ++++++-------
 inst/NEWS.Rd              |    9 ++++-
 src/decor.cpp             |    2 +
 src/hpp/LHSsorted.hpp     |    2 +
 src/hpp/dnyTasking.hpp    |    2 +
 src/jointSimulation.cpp   |    2 +
 src/lhsPMF.cpp            |    2 +
 src/pcg/parallelSeeds.hpp |    2 +
 src/pcg/pcg_extras.hpp    |    1 
 src/pcg/pcg_random.hpp    |   79 +++++++++++++++++++++++-----------------------
 src/pcg/pcg_uint128.hpp   |    1 
 src/pcg/toSeed.hpp        |    1 
 13 files changed, 78 insertions(+), 55 deletions(-)

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New package RSCABS with initial version 0.9.4
Package: RSCABS
Version: 0.9.4
Date: 2020-1-13
Title: Rao-Scott Cochran-Armitage by Slices Trend Test
Authors@R: c(person("Joe", "Swintek", role = c("aut", "cre"), email = "swintek.joe@epa.gov"), person("Kevin", "Flynn", role = "ctb", email = "Flynn.Kevin@epa.gov") )
Depends: R (>= 3.1.0)
SystemRequirements: Cairo (>= 1.0.0), ATK (>= 1.10.0), Pango (>= 1.10.0), GTK+ (>= 2.8.0), GLib (>= 2.8.0)
Imports: gWidgets, RGtk2, R2HTML, gWidgetsRGtk2, methods
LazyLoad: yes
LazyData: yes
Description: Performs the Rao-Scott Cochran-Armitage by Slices trend test (RSCABS) used in analysis of histopathological endpoints, built to be used with either a GUI or by a command line. The RSCABS method is detailed in "Statistical analysis of histopathological endpoints" by John Green et. al. (2014) <doi:10.1002/etc.2530>.
License: CC0
URL: https://CRAN.R-project.org/package=RSCABS
Author: Joe Swintek [aut, cre], Kevin Flynn [ctb]
Maintainer: Joe Swintek <swintek.joe@epa.gov>
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-01-13 19:24:13 UTC; jswintek
Repository: CRAN
Date/Publication: 2020-01-13 20:00:02 UTC

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Package RaceID updated to version 0.1.7 with previous version 0.1.6 dated 2019-11-21

Title: Identification of Cell Types and Inference of Lineage Trees from Single-Cell RNA-Seq Data
Description: Application of 'RaceID' allows inference of cell types and prediction of lineage trees by he StemID2 algorithm. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Author: Dominic Grün <dominic.gruen@gmail.com>
Maintainer: Dominic Grün <dominic.gruen@gmail.com>

Diff between RaceID versions 0.1.6 dated 2019-11-21 and 0.1.7 dated 2020-01-13

 DESCRIPTION          |    8 +++---
 MD5                  |   12 +++++-----
 R/VarID_functions.R  |   52 +++++++++++++++++++++++++++++++++++---------
 inst/doc/RaceID.R    |   10 ++++----
 inst/doc/RaceID.Rmd  |   19 ++++++++--------
 inst/doc/RaceID.html |   59 +++++++++++++++++++++++++--------------------------
 vignettes/RaceID.Rmd |   19 ++++++++--------
 7 files changed, 106 insertions(+), 73 deletions(-)

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Package nproc updated to version 2.1.5 with previous version 2.1.4 dated 2018-11-16

Title: Neyman-Pearson (NP) Classification Algorithms and NP Receiver Operating Characteristic (NP-ROC) Curves
Description: In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers.
Author: Yang Feng [aut, cre], Jessica Li [aut], Xin Tong [aut], Ye Tian [ctb]
Maintainer: Yang Feng <yangfengstat@gmail.com>

Diff between nproc versions 2.1.4 dated 2018-11-16 and 2.1.5 dated 2020-01-13

 DESCRIPTION               |   19 -
 MD5                       |   14 -
 R/npfuns.R                |  606 +++++++++++++++++++++++-----------------------
 build/vignette.rds        |binary
 man/npc.Rd                |    9 
 man/nproc.Rd              |    6 
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 8 files changed, 368 insertions(+), 550 deletions(-)

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Package FarmTest updated to version 2.0.0 with previous version 1.0.3 dated 2018-05-29

Title: Factor Adjusted Robust Multiple Testing
Description: Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a series of adaptive Huber methods combined with fast data-drive tuning schemes to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package also contains functions that compute adaptive Huber mean and covariance matrix estimators that are of independent interest.
Author: Xiaoou Pan, Yuan Ke and Wen-Xin Zhou
Maintainer: Xiaoou Pan <xip024@ucsd.edu>

Diff between FarmTest versions 1.0.3 dated 2018-05-29 and 2.0.0 dated 2020-01-13

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More information about FarmTest at CRAN
Permanent link

Package adehabitatMA updated to version 0.3.14 with previous version 0.3.13 dated 2019-04-07

Title: Tools to Deal with Raster Maps
Description: A collection of tools to deal with raster maps.
Author: Clement Calenge, contributions from Mathieu Basille
Maintainer: Clement Calenge <clement.calenge@ofb.gouv.fr>

Diff between adehabitatMA versions 0.3.13 dated 2019-04-07 and 0.3.14 dated 2020-01-13

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Permanent link

Package adehabitatHS updated to version 0.3.15 with previous version 0.3.14 dated 2019-04-07

Title: Analysis of Habitat Selection by Animals
Description: A collection of tools for the analysis of habitat selection.
Author: Clement Calenge, contributions from Mathieu Basille
Maintainer: Clement Calenge <clement.calenge@ofb.gouv.fr>

Diff between adehabitatHS versions 0.3.14 dated 2019-04-07 and 0.3.15 dated 2020-01-13

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Package rdmulti updated to version 0.4 with previous version 0.3 dated 2019-10-29

Title: Analysis of RD Designs with Multiple Cutoffs or Scores
Description: The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdmulti' package provides tools to analyze RD designs with multiple cutoffs or scores: rdmc() estimates pooled and cutoff specific effects for multi-cutoff designs, rdmcplot() draws RD plots for multi-cutoff designs and rdms() estimates effects in cumulative cutoffs or multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://sites.google.com/site/rdpackages/rdmulti/Cattaneo-Titiunik-VazquezBare_2019_rdmulti.pdf> for further methodological details.
Author: Matias D. Cattaneo, Rocio Titiunik, Gonzalo Vazquez-Bare
Maintainer: Gonzalo Vazquez-Bare <gvazquez@econ.ucsb.edu>

Diff between rdmulti versions 0.3 dated 2019-10-29 and 0.4 dated 2020-01-13

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More information about rdmulti at CRAN
Permanent link

Package AlphaSimR updated to version 0.11.1 with previous version 0.11.0 dated 2019-10-11

Title: Breeding Program Simulations
Description: The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].
Author: Chris Gaynor [aut, cre] (<https://orcid.org/0000-0003-0558-6656>), Gregor Gorjanc [ctb] (<https://orcid.org/0000-0001-8008-2787>), David Wilson [ctb], Daniel Money [ctb] (<https://orcid.org/0000-0001-5151-3648>), John Hickey [ctb] (<https://orcid.org/0000-0001-5675-3974>)
Maintainer: Chris Gaynor <gaynor.robert@hotmail.com>

Diff between AlphaSimR versions 0.11.0 dated 2019-10-11 and 0.11.1 dated 2020-01-13

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More information about AlphaSimR at CRAN
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Package tigris updated to version 0.9.1 with previous version 0.8.2 dated 2019-06-12

Title: Load Census TIGER/Line Shapefiles
Description: Download TIGER/Line shapefiles from the United States Census Bureau (<https://www.census.gov/geo/maps-data/data/tiger-line.html>) and load into R as 'SpatialDataFrame' or 'sf' objects.
Author: Kyle Walker [aut, cre], Bob Rudis [ctb]
Maintainer: Kyle Walker <kyle.walker@tcu.edu>

Diff between tigris versions 0.8.2 dated 2019-06-12 and 0.9.1 dated 2020-01-13

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New package s2net with initial version 1.0
Package: s2net
Type: Package
Title: The Generalized Semi-Supervised Elastic-Net
Version: 1.0
Date: 2020-01-08
Authors@R: c(person("Juan C.", "Laria",, role = c("aut", "cre"), email = "juank.laria@gmail.com", comment = c(ORCID = "0000-0001-7734-9647")), person("Line H.", "Clemmensen",, role = c("aut"), email = "lkhc@dtu.dk"))
Description: Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.
License: GPL (>= 2)
Imports: Rcpp, methods, MASS
Depends: stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, glmnet, Metrics, testthat
VignetteBuilder: knitr
URL: https://github.com/jlaria/s2net
BugReports: https://github.com/jlaria/s2net/issues
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-01-11 13:17:07 UTC; juank
Author: Juan C. Laria [aut, cre] (<https://orcid.org/0000-0001-7734-9647>), Line H. Clemmensen [aut]
Maintainer: Juan C. Laria <juank.laria@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 17:10:06 UTC

More information about s2net at CRAN
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Package ModelMetrics updated to version 1.2.2.1 with previous version 1.2.2 dated 2018-11-03

Title: Rapid Calculation of Model Metrics
Description: Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.
Author: Tyler Hunt [aut, cre]
Maintainer: Tyler Hunt <thunt@snapfinance.com>

Diff between ModelMetrics versions 1.2.2 dated 2018-11-03 and 1.2.2.1 dated 2020-01-13

 DESCRIPTION      |    6 +++---
 MD5              |    6 +++---
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 data/testDF.rda  |binary
 4 files changed, 8 insertions(+), 8 deletions(-)

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New package hrcomprisk with initial version 0.1.0
Package: hrcomprisk
Type: Package
Title: Nonparametric Assessment Between Competing Risks Hazard Ratios
Version: 0.1.0
Author: Daniel Antiporta <dantiporta@jhu.edu>; Matthew Matheson <mmathes4@jhu.edu>; Derek Ng <dng@jhu.edu>; Alvaro Munoz <amunoz@jhu.edu>
Maintainer: Daniel Antiporta <dantiporta@jhu.edu>
Description: Nonparametric cumulative-incidence based estimation of the ratios of sub-hazard ratios to cause-specific hazard ratios using the approach from Ng et al. (2020).
Depends: R (>= 3.0.0)
Imports: survival
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
URL: https://github.com/AntiportaD/hrcomprisk
BugReports: https://github.com/AntiportaD/hrcomprisk/issues
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-11 17:48:09 UTC; antiporta
Repository: CRAN
Date/Publication: 2020-01-13 17:30:02 UTC

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New package cuRe with initial version 1.0.0
Package: cuRe
Type: Package
Title: Parametric Cure Model Estimation
Version: 1.0.0
Date: 2020-01-07
Authors@R: person(given = "Lasse Hjort Jakobsen", role = c("aut", "cre"), email = "lahja@dcm.aau.dk")
Maintainer: Lasse Hjort Jakobsen <lahja@dcm.aau.dk>
Description: Contains functions for estimating generalized parametric mixture and non-mixture cure models, loss of lifetime, mean residual lifetime, and crude event probabilities.
URL: http://github.com/LasseHjort/cuRe
BugReports: http://github.com/LasseHjort/cuRe/issues
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 3.5.0), survival, rstpm2, date
Imports: numDeriv, rootSolve, statmod, relsurv
LazyData: Yes
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-01-13 08:36:07 UTC; sw1y
Author: Lasse Hjort Jakobsen [aut, cre]
Repository: CRAN
Date/Publication: 2020-01-13 17:30:06 UTC

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New package cSEM with initial version 0.1.0
Package: cSEM
Title: Composite-Based Structural Equation Modeling
Version: 0.1.0
Date: 2020-01-07
Authors@R: c(person(given = "Manuel", family = "Rademaker", role = c("aut", "cre"), email = "manuel.rademaker@uni-wuerzburg.de", comment = c(ORCID = "0000-0002-8902-3561")), person(given = "Florian", family = "Schuberth", role = c("aut"), email = "f.schuberth@utwente.nl", comment = c(ORCID = "0000-0002-2110-9086")), person(given = "Tamara", family = "Schamberger", role = c("ctb"), email = "tamara.schamberger@uni-wuerzburg.de"), person(given = "Michael", family = "Klesel", role = c("ctb"), email = ""), person(given = "Theo K.", family = "Dijkstra", role = c("ctb"), email = ""))
Maintainer: Manuel Rademaker <manuel.rademaker@uni-wuerzburg.de>
Depends: R (>= 3.5.0)
Description: Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.).
BugReports: https://github.com/M-E-Rademaker/cSEM/issues
URL: https://github.com/M-E-Rademaker/cSEM, https://m-e-rademaker.github.io/cSEM/
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: abind, alabama, cli, crayon, expm, future.apply, future, lavaan, magrittr, MASS, Matrix, matrixcalc, matrixStats, polycor, psych, purrr, Rdpack, stats, symmoments, utils
RdMacros: Rdpack
RoxygenNote: 7.0.2
Suggests: dplyr, tidyr, knitr, nnls, prettydoc, rmarkdown, listviewer, testthat, ggplot2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-11 15:23:39 UTC; mas55yq
Author: Manuel Rademaker [aut, cre] (<https://orcid.org/0000-0002-8902-3561>), Florian Schuberth [aut] (<https://orcid.org/0000-0002-2110-9086>), Tamara Schamberger [ctb], Michael Klesel [ctb], Theo K. Dijkstra [ctb]
Repository: CRAN
Date/Publication: 2020-01-13 17:20:02 UTC

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New package Boptbd with initial version 1.0.5
Package: Boptbd
Version: 1.0.5
Date: 2020-01-11
Title: Bayesian Optimal Block Designs
Authors@R: c(person("Dibaba Bayisa", "Gemechu", role = c("aut", "cre"), email = "dgemechu@nust.na"), person("Legesse Kassa", "Debusho", role = "aut", email = "debuslk@unisa.ac.za"), person("Linda", "Haines", role = "aut", email = "linda.haines@uct.ac.za"))
Author: Dibaba Bayisa Gemechu [aut, cre], Legesse Kassa Debusho [aut], Linda Haines [aut]
Maintainer: Dibaba Bayisa Gemechu <dgemechu@nust.na>
Depends: R (>= 3.4.0), MASS, Matrix, igraph, tcltk
Description: Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
License: GPL-2
NeedsCompilation: no
Packaged: 2020-01-11 15:27:36 UTC; Dibaba B Gemechu
Repository: CRAN
Date/Publication: 2020-01-13 17:20:07 UTC

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Package dataone (with last version 2.1.2) was removed from CRAN

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

2019-01-29 2.1.2
2018-06-28 2.1.1
2017-08-18 2.1.0
2016-08-29 2.0.1
2016-05-27 2.0.0

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Package phmm (with last version 0.7-11) was removed from CRAN

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

2019-03-08 0.7-11

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Package turboEM updated to version 2020.1 with previous version 2018.1 dated 2018-05-06

Title: A Suite of Convergence Acceleration Schemes for EM, MM and Other Fixed-Point Algorithms
Description: Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM and MM algorithms. It can be used to accelerate any smooth, linearly convergent acceleration scheme. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("turboEM").
Author: Jennifer F. Bobb [aut], Ravi Varadhan [aut, cre], Hui Zhao [ctb]
Maintainer: Ravi Varadhan <ravi.varadhan@jhu.edu>

Diff between turboEM versions 2018.1 dated 2018-05-06 and 2020.1 dated 2020-01-13

 DESCRIPTION          |    6 
 MD5                  |   12 -
 NEWS                 |    6 
 R/turboem.R          |   53 +++----
 build/vignette.rds   |binary
 inst/doc/turboEM.pdf |binary
 man/turboem.Rd       |  379 +++++++++++++++++++++++++--------------------------
 7 files changed, 231 insertions(+), 225 deletions(-)

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New package survParamSim with initial version 0.1.0
Package: survParamSim
Type: Package
Title: Parametric Survival Simulation with Parameter Uncertainty
Version: 0.1.0
Authors@R: c( person("Kenta", "Yoshida", , "6.kurabupasu@gmail.com", c("aut", "cre"), comment = c(ORCID = "0000-0003-4967-3831")), person("Laurent", "Claret", , , c("aut")))
Description: Perform survival simulation with parametric survival model generated from 'survreg' function in 'survival' package. In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to capture uncertainty in model parameters. Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0),
Imports: magrittr (>= 1.5), mvtnorm, rlang, purrr, forcats, dplyr, tidyr, tibble, ggplot2, survival (>= 2.43), methods
Suggests: testthat (>= 2.1.0), vdiffr, knitr, rmarkdown, survminer
RoxygenNote: 7.0.2
Language: en-US
URL: https://github.com/yoshidk6/survParamSim
BugReports: https://github.com/yoshidk6/survParamSim/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-11 05:29:55 UTC; rokuk
Author: Kenta Yoshida [aut, cre] (<https://orcid.org/0000-0003-4967-3831>), Laurent Claret [aut]
Maintainer: Kenta Yoshida <6.kurabupasu@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 17:00:02 UTC

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New package relgam with initial version 1.0
Package: relgam
Type: Package
Title: Reluctant Generalized Additive Models
Version: 1.0
Author: Kenneth Tay, Robert Tibshirani
Maintainer: Kenneth Tay <kjytay@stanford.edu>
Description: A method for fitting the entire regularization path of the reluctant generalized additive model (RGAM) for linear regression, logistic, Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019) <arXiv:1912.01808> for details.
URL: https://arxiv.org/abs/1912.01808
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: glmnet, foreach
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-08 23:53:21 UTC; kjytay
Repository: CRAN
Date/Publication: 2020-01-13 16:10:02 UTC

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Package probably updated to version 0.0.4 with previous version 0.0.3 dated 2019-07-07

Title: Tools for Post-Processing Class Probability Estimates
Description: Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations.
Author: Max Kuhn [aut], Davis Vaughan [aut, cre], RStudio [cph]
Maintainer: Davis Vaughan <davis@rstudio.com>

Diff between probably versions 0.0.3 dated 2019-07-07 and 0.0.4 dated 2020-01-13

 DESCRIPTION                      |   11 +
 MD5                              |   25 ++--
 NEWS.md                          |    6 +
 inst/doc/equivocal-zones.R       |   14 +-
 inst/doc/equivocal-zones.html    |   10 +
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 inst/doc/where-to-use.Rmd        |    1 
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 man/append_class_pred.Rd         |   10 +
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 man/threshold_perf.Rd            |    3 
 tests/testthat/output            |only
 tests/testthat/test-class-pred.R |    8 +
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 14 files changed, 187 insertions(+), 158 deletions(-)

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New package PAMA with initial version 0.1.0
Package: PAMA
Title: Rank Aggregation with Partition Mallows Model
Version: 0.1.0
Authors@R: person(given = "Wanchuang", family = "Zhu", role = c("cre","aut"), email = "andy.chou.sub@gmail.com")
Description: Rank aggregation aims to achieve a better ranking list given multiple observations. 'PAMA' implements Partition-Mallows model for rank aggregation. Both Bayesian inference and Maximum likelihood estimation (MLE) are provided. It can handle partial list as well. When covariates information is available, this package can make inference by incorporating the covariate information. More information can be found in the paper "Integrated Partition-Mallows Model and Its Inference for Rank Aggregation". The paper is not yet published.
Depends: R (>= 3.1.0), PerMallows, mc2d, stats
Imports:
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.1
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2020-01-09 22:11:11 UTC; wanchuangzhu
Author: Wanchuang Zhu [cre, aut]
Maintainer: Wanchuang Zhu <andy.chou.sub@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 16:20:05 UTC

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New package optimalThreshold with initial version 1.0
Package: optimalThreshold
Type: Package
Title: Bayesian Methods for Optimal Threshold Estimation
Version: 1.0
SystemRequirements: JAGS 4.x.y
Depends: R (>= 3.1.2)
Date: 2020-01-09
Authors@R: person("Yoann", "Blangero", email = "yblangero@gmail.com", role = c("aut", "cre"))
Description: Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.
License: GPL (>= 2.0)
Collate: Package.R import_package.R scaledT.R ClassUnions.R ClassFitNormalDist.R ClassFitLogNormalDist.R ClassFitGammaDist.R ClassFitStudentDist.R ClassFitLogisticDist.R ClassFitUserDefinedDist.R ClassUndefined.R ClassNormalDist.R ClassLogNormalDist.R ClassGammaDist.R ClassStudentDist.R ClassLogisticDist.R ClassUnionsDist.R ClassCompoundDist.R ClassTrtSelOptThresh.R ClassTrtSelRelUtility.R ClassDiagOptThresh.R ClassDiagRelUtility.R cdf.R gradient.R hessian.R ARS.R samplePosteriorDist.R global.R
RoxygenNote: 6.1.1
Imports: ars, rjags, HDInterval, mgcv, utils, coda, grDevices, methods, stats, graphics
NeedsCompilation: no
Packaged: 2020-01-09 21:51:17 UTC; yblan
Author: Yoann Blangero [aut, cre]
Maintainer: Yoann Blangero <yblangero@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 16:10:05 UTC

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New package nlraa with initial version 0.53
Package: nlraa
Version: 0.53
Authors@R: c(person("Fernando", "Miguez", email = "femiguez@iastate.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4627-8329")), person("José", "Pinheiro", role = c("ctb","cph"), comment = "author of nlme::nlsList"), person("Douglas", "Bates", role = c("ctb","cph"), comment = "author of nlme::nlsList"), person("R-core", email = "R-core@R-project.org", role = c("ctb", "cph")))
Title: Nonlinear Regression for Agricultural Applications
Description: Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Eighteen new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function 'nlsLMList' uses nlsLM for fitting, but it is otherwise almost identical to 'nlme::nlsList'. One of the main benefits is that these functions can be integrated in the modeling framework of the 'nlme' package. It also provides three vignettes with extended examples.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
VignetteBuilder: knitr
BugReports: https://github.com/femiguez/nlraa/issues
Imports: knitr, nlme, stats
Suggests: bbmle, emmeans, ggplot2, HydroMe, lattice, minpack.lm, nlstools, nls2, segmented
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-01-09 21:39:02 UTC; fernandomiguez
Author: Fernando Miguez [aut, cre] (<https://orcid.org/0000-0002-4627-8329>), José Pinheiro [ctb, cph] (author of nlme::nlsList), Douglas Bates [ctb, cph] (author of nlme::nlsList), R-core [ctb, cph]
Maintainer: Fernando Miguez <femiguez@iastate.edu>
Repository: CRAN
Date/Publication: 2020-01-13 16:20:02 UTC

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Package cder updated to version 0.2-1 with previous version 0.2-0 dated 2019-08-07

Title: Interface to the California Data Exchange Center
Description: Connect to the California Data Exchange Center (CDEC) Web Service <http://cdec.water.ca.gov/>. 'CDEC' provides a centralized database to store, process, and exchange real-time hydrologic information gathered by various cooperators throughout California. The 'CDEC' Web Service <http://cdec.water.ca.gov/dynamicapp/wsSensorData> provides a data download service for accessing historical records.
Author: Michael Koohafkan [aut, cre]
Maintainer: Michael Koohafkan <michael.koohafkan@gmail.com>

Diff between cder versions 0.2-0 dated 2019-08-07 and 0.2-1 dated 2020-01-13

 DESCRIPTION              |   14 +--
 MD5                      |   33 ++++-----
 NAMESPACE                |    2 
 NEWS.md                  |only
 R/query.r                |   20 ++---
 inst/doc/quickstart.html |  168 ++++++++++++++++++++++-------------------------
 inst/doc/quickstart.rmd  |   89 +++++++++++-------------
 man/Deprecated.Rd        |   44 ++++++------
 man/basic_query.Rd       |   34 ++++-----
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 man/cdec_meta.Rd         |   30 ++++----
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 man/cdec_query_group.Rd  |   70 +++++++++----------
 man/cder.Rd              |   66 +++++++++---------
 man/cder_handle.Rd       |   22 +++---
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 man/station.spec.Rd      |   26 +++----
 vignettes/quickstart.rmd |   89 +++++++++++-------------
 18 files changed, 425 insertions(+), 450 deletions(-)

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New package BRL with initial version 0.1.0
Package: BRL
Title: Beta Record Linkage
Version: 0.1.0
Authors@R: person("Mauricio", "Sadinle", email = "msadinle@uw.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7092-3877"))
Description: Implementation of the record linkage methodology proposed by Sadinle (2017) <doi:10.1080/01621459.2016.1148612>. It handles the bipartite record linkage problem, where two duplicate-free datafiles are to be merged.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: utils
RoxygenNote: 7.0.2
URL: https://github.com/msadinle/BRL
BugReports: https://github.com/msadinle/BRL/issues
NeedsCompilation: yes
Packaged: 2020-01-11 01:12:16 UTC; Mauricio Sadinle
Author: Mauricio Sadinle [aut, cre] (<https://orcid.org/0000-0002-7092-3877>)
Maintainer: Mauricio Sadinle <msadinle@uw.edu>
Repository: CRAN
Date/Publication: 2020-01-13 16:50:06 UTC

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New package BinaryDosage with initial version 1.0.0
Package: BinaryDosage
Title: Creates, Merges, and Reads Binary Dosage Files
Version: 1.0.0
Authors@R: c(person(given = "John", family = "Morrison", role = c("aut", "cre"), email = "jmorr@usc.edu"), person(given = "NIEHS", role = "fnd", comment = "P01 CA196559"), person(given = "NIEHS", role = "fnd", comment = "R01 CA201407"), person(given = "NIEHS", role = "fnd", comment = "P30 ES007048"), person(given = "NIEHS", role = "fnd", comment = "P01 HL115606"))
Description: Tools to create binary dosage from either VCF or GEN files, merge binary dosage files, and read binary dosage files.
License: GPL-3
Encoding: UTF-8
LazyData: true
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), covr
VignetteBuilder: knitr
RoxygenNote: 7.0.2
LinkingTo: Rcpp
Imports: Rcpp, digest, prodlim
NeedsCompilation: yes
Packaged: 2020-01-09 23:00:33 UTC; jmorr
Author: John Morrison [aut, cre], NIEHS [fnd] (P01 CA196559), NIEHS [fnd] (R01 CA201407), NIEHS [fnd] (P30 ES007048), NIEHS [fnd] (P01 HL115606)
Maintainer: John Morrison <jmorr@usc.edu>
Repository: CRAN
Date/Publication: 2020-01-13 16:20:08 UTC

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New package atakrig with initial version 0.9.5
Package: atakrig
Type: Package
Title: Area-to-Area Kriging
Version: 0.9.5
Authors@R: c( person("Maogui", "Hu", role = c("aut", "cre"), email = "humg@lreis.ac.cn"), person("Yanwei", "Huang", role = "ctb"), person("Roger", "Bivand", role = "ctb"))
Description: Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) <doi: 10.1007/s11004-007-9129-1>; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging.
Imports: gstat, sp, rgeos, foreach, doSNOW, snow, FNN, methods, MASS, Rcpp
Suggests: raster, rgdal, rtop
License: GPL (>= 2.0)
NeedsCompilation: yes
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp
Packaged: 2020-01-11 04:57:34 UTC; humg
Author: Maogui Hu [aut, cre], Yanwei Huang [ctb], Roger Bivand [ctb]
Maintainer: Maogui Hu <humg@lreis.ac.cn>
Repository: CRAN
Date/Publication: 2020-01-13 16:50:02 UTC

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New package themis with initial version 0.1.0
Package: themis
Title: Extra Recipes Steps for Dealing with Unbalanced Data
Version: 0.1.0
Authors@R: person(given = "Emil", family = "Hvitfeldt", role = c("aut", "cre"), email = "emilhhvitfeldt@gmail.com", comment = c(ORCID = "0000-0002-0679-1945"))
Description: A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 <arXiv:1106.1813>, BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008 <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the number of majority cases using NearMiss 2003 <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
License: MIT + file LICENSE
Suggests: testthat (>= 2.1.0), covr, ggplot2, modeldata
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Depends: R (>= 2.10), recipes (>= 0.1.4)
Imports: tibble, purrr, withr, generics, dplyr, rlang, tidyselect (>= 0.2.5), ROSE, unbalanced, RANN, dials
URL: https://github.com/tidymodels/themis
BugReports: https://github.com/tidymodels/themis/issues
NeedsCompilation: no
Packaged: 2020-01-09 16:41:29 UTC; emilhvitfeldthansen
Author: Emil Hvitfeldt [aut, cre] (<https://orcid.org/0000-0002-0679-1945>)
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 15:30:02 UTC

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New package outForest with initial version 0.1.0
Type: Package
Package: outForest
Title: Multivariate Outlier Detection and Replacement
Version: 0.1.0
Date: 2020-01-09
Authors@R: person(given = "Michael", family = "Mayer", role = c("aut", "cre"), email = "mayermichael79@gmail.com")
Maintainer: Michael Mayer <mayermichael79@gmail.com>
Description: Provides a random forest based implementation of the method described in Chapter 7.1.2 (Regression model based anomaly detection) of Chandola et al. (2009) <doi.acm.org/10.1145/1541880.1541882>. It works as follows: Each numeric variable is regressed onto all other variables by a random forest. If the scaled absolute difference between observed value and out-of-bag prediction of the corresponding random forest is suspiciously large, then a value is considered an outlier. The package offers different options to replace such outliers, e.g. by realistic values found via predictive mean matching. Once the method is trained on a reference data, it can be applied to new data.
License: GPL (>= 2)
URL: https://github.com/mayer79/outForest
BugReports: https://github.com/mayer79/outForest/issues
Depends: R (>= 3.5.0)
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
Imports: stats, graphics, FNN, ranger, missRanger (>= 2.1.0)
Suggests: dplyr, knitr
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-01-09 18:20:39 UTC; Michael
Author: Michael Mayer [aut, cre]
Repository: CRAN
Date/Publication: 2020-01-13 15:50:03 UTC

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New package extRC with initial version 1.0
Package: extRC
Type: Package
Title: Extended RC Models for Contingency Tables
Version: 1.0
Date: 2019-12-24
Author: Francesco Bartolucci, Antonio Forcina
Maintainer: Francesco Bartolucci <francesco.bartolucci@unipg.it>
Description: Maximum likelihood estimation of an extended class of row-column (RC) association models for two-dimensional contingency tables, which are formulated by a condition of reduced rank on a matrix of extended association parameters; see Forcina (2019) <arXiv:1910.13848>. These parameters are defined by choosing the logit type for the row and column variables among four different options and a transformation derived from suitable divergence measures.
License: GPL (>= 2)
Imports: MASS
NeedsCompilation: no
Packaged: 2020-01-09 15:10:07 UTC; francescobartolucci
Repository: CRAN
Date/Publication: 2020-01-13 15:40:02 UTC

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Package econullnetr updated to version 0.2.0 with previous version 0.1.0.1 dated 2019-06-09

Title: Null Model Analysis for Ecological Networks
Description: Tools for using null models to analyse ecological networks (e.g. food webs, flower-visitation networks, seed-dispersal networks) and detect resource preferences or non-random interactions among network nodes. Tools are provided to run null models, test for and plot preferences, plot and analyse bipartite networks, and export null model results in a form compatible with other network analysis packages. The underlying null model was developed by Agusti et al. (2003) Molecular Ecology <doi:10.1046/j.1365-294X.2003.02014.x> and the full application to ecological networks by Vaughan et al. (2018) econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods in Ecology & Evolution, <doi.org/10.1111/2041-210X.12907>.
Author: Ian Vaughan
Maintainer: Ian Vaughan <vaughanip@cardiff.ac.uk>

Diff between econullnetr versions 0.1.0.1 dated 2019-06-09 and 0.2.0 dated 2020-01-13

 econullnetr-0.1.0.1/econullnetr/tests/testthat/gl_test                                                |only
 econullnetr-0.1.0.1/econullnetr/tests/testthat/nl_test                                                |only
 econullnetr-0.1.0.1/econullnetr/tests/testthat/sl_test                                                |only
 econullnetr-0.2.0/econullnetr/DESCRIPTION                                                             |   19 
 econullnetr-0.2.0/econullnetr/MD5                                                                     |  142 -
 econullnetr-0.2.0/econullnetr/NAMESPACE                                                               |    3 
 econullnetr-0.2.0/econullnetr/NEWS.md                                                                 |    9 
 econullnetr-0.2.0/econullnetr/R/WelshStreams.R                                                        |   15 
 econullnetr-0.2.0/econullnetr/R/WelshStreams_fl.R                                                     |    7 
 econullnetr-0.2.0/econullnetr/R/WelshStreams_order.R                                                  |    7 
 econullnetr-0.2.0/econullnetr/R/WelshStreams_prey.R                                                   |    7 
 econullnetr-0.2.0/econullnetr/R/bipartite_stats.R                                                     |   22 
 econullnetr-0.2.0/econullnetr/R/econullnetr.R                                                         |    6 
 econullnetr-0.2.0/econullnetr/R/expanding_interaction_matrix.R                                        |only
 econullnetr-0.2.0/econullnetr/R/generate_edgelist.R                                                   |   56 
 econullnetr-0.2.0/econullnetr/R/null_model.R                                                          |   31 
 econullnetr-0.2.0/econullnetr/R/plot_bipartite.R                                                      |    8 
 econullnetr-0.2.0/econullnetr/R/plot_preferences.R                                                    |    8 
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Package discoveR updated to version 1.2.4 with previous version 1.1.0 dated 2019-03-03

Title: Exploratory Data Analysis System
Description: Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.
Author: Oldemar Rodriguez R. with contributions from Diego Jimenez A. and Andres Navarro D.
Maintainer: Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr>

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Package caTools updated to version 1.17.1.4 with previous version 1.17.1.3 dated 2019-11-30

Title: Tools: moving window statistics, GIF, Base64, ROC AUC, etc
Description: Contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc.
Author: Jarek Tuszynski <jaroslaw.w.tuszynski@saic.com>
Maintainer: ORPHANED

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New package apyramid with initial version 0.1.0
Package: apyramid
Title: Visualize Population Pyramids Aggregated by Age
Version: 0.1.0
Authors@R: c( person(given = "Zhian N.", family = "Kamvar", role = c("aut", "cre"), email = "zkamvar@gmail.com", comment = c(ORCID = "0000-0003-1458-7108")), person(given = "Alex", family = "Spina", role = c("ctb")))
Description: Provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a 'ggplot' object, allowing the user to further customize the output. This package is part of the 'R4Epis' project <https://r4epis.netlify.com>.
License: GPL-3
Depends: R (>= 3.2.0)
URL: https://github.com/R4EPI/apyramid, https://r4epis.netlify.com
BugReports: https://github.com/R4EPI/apyramid/issues
Imports: ggplot2 (>= 3.0.0), tidyselect, rlang, forcats, dplyr, scales, glue
Suggests: testthat (>= 2.1.0), survey, srvyr, vdiffr, covr, outbreaks, knitr, rmarkdown
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-09 14:58:36 UTC; zkamvar
Author: Zhian N. Kamvar [aut, cre] (<https://orcid.org/0000-0003-1458-7108>), Alex Spina [ctb]
Maintainer: Zhian N. Kamvar <zkamvar@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 15:50:06 UTC

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Package rtk updated to version 0.2.5.8 with previous version 0.2.5.7 dated 2018-12-08

Title: Rarefaction Tool Kit
Description: Rarefy data, calculate diversity and plot the results.
Author: Paul Saary, Falk Hildebrand
Maintainer: Paul Saary <rtk@paulsaary.de>

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Package PHEindicatormethods updated to version 1.2.0 with previous version 1.1.5 dated 2019-09-11

Title: Common Public Health Statistics and their Confidence Intervals
Description: Functions to calculate commonly used public health statistics and their confidence intervals using methods approved for use in the production of Public Health England indicators such as those presented via Fingertips (<http://fingertips.phe.org.uk/>). It provides functions for the generation of proportions, crude rates, means, directly standardised rates, indirectly standardised rates, standardised mortality ratios, slope and relative index of inequality and life expectancy. Statistical methods are referenced in the following publications. Breslow NE, Day NE (1987) <doi:10.1002/sim.4780080614>. Dobson et al (1991) <doi:10.1002/sim.4780100317>. Armitage P, Berry G (2002) <doi:10.1002/9780470773666>. Wilson EB. (1927) <doi:10.1080/01621459.1927.10502953>. Altman DG et al (2000, ISBN: 978-0-727-91375-3). Chiang CL. (1968, ISBN: 978-0-882-75200-6). Newell C. (1994, ISBN: 978-0-898-62451-9). Eayres DP, Williams ES (2004) <doi:10.1136/jech.2003.009654>. Silcocks PBS et al (2001) <doi:10.1136/jech.55.1.38>. Low and Low (2004) <doi:10.1093/pubmed/fdh175>.
Author: Anderson Georgina [aut, cre], Fox Sebastian [ctb], Francis Matthew [ctb], Fryers Paul [ctb], Clegg Emma [ctb]
Maintainer: Anderson Georgina <georgina.anderson@phe.gov.uk>

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Package TraMineR updated to version 2.0-14 with previous version 2.0-13 dated 2019-11-19

Title: Trajectory Miner: a Toolbox for Exploring and Rendering Sequences
Description: Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Author: Alexis Gabadinho [aut, cph], Matthias Studer [aut, cph] (<https://orcid.org/0000-0002-6269-1412>), Nicolas Müller [aut], Reto Bürgin [aut], Pierre-Alexandre Fonta [aut], Gilbert Ritschard [aut, cre, cph] (<https://orcid.org/0000-0001-7776-0903>)
Maintainer: Gilbert Ritschard <gilbert.ritschard@unige.ch>

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Package SqlRender updated to version 1.6.3 with previous version 1.6.2 dated 2019-06-24

Title: Rendering Parameterized SQL and Translation to Dialects
Description: A rendering tool for parameterized SQL that also translates into different SQL dialects. These dialects include 'Microsoft Sql Server', 'Oracle', 'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', and 'SQLite'.
Author: Martijn Schuemie [aut, cre], Marc Suchard [aut]
Maintainer: Martijn Schuemie <schuemie@ohdsi.org>

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Package spcosa updated to version 0.3-9 with previous version 0.3-8 dated 2018-03-30

Title: Spatial Coverage Sampling and Random Sampling from Compact Geographical Strata
Description: Spatial coverage sampling and random sampling from compact geographical strata created by k-means. See Walvoort et al. (2010) <doi:10.1016/j.cageo.2010.04.005> for details.
Author: Dennis Walvoort [aut, cre, cph], Dick Brus [aut, cph], Jaap de Gruijter [aut, cph]
Maintainer: Dennis Walvoort <dennis.Walvoort@wur.nl>

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Package modelStudio updated to version 0.2.0 with previous version 0.1.9 dated 2019-11-03

Title: Interactive Studio with Explanations for ML Predictive Models
Description: Automate explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of a serverless HTML site. It combines 'R' with 'D3.js' to produce plots and descriptions for various local and global explanations. Tools for model exploration unite with tools for EDA (Exploratory Data Analysis) to give a broad overview of the model behaviour. 'modelStudio' is a fast and condensed way to get all the answers without much effort. Break down your model and look into its ingredients with only a few lines of code.
Author: Hubert Baniecki [aut, cre] (<https://orcid.org/0000-0001-6661-5364>), Przemyslaw Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>)
Maintainer: Hubert Baniecki <hbaniecki@gmail.com>

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Package packager (with last version 1.0.0) was removed from CRAN

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

2020-01-13 1.0.0

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Package unrtf updated to version 1.4 with previous version 1.3 dated 2018-11-08

Title: Extract Text from Rich Text Format (RTF) Documents
Description: Wraps the 'unrtf' utility to extract text from RTF files. Supports document conversion to HTML, LaTeX or plain text. Output in HTML is recommended because 'unrtf' has limited support for converting between character encodings.
Author: Jeroen Ooms [aut, cre], Free Software Foundation, Inc [cph]
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

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New package packager with initial version 1.0.0
Package: packager
Title: Create, Build and Maintain Packages
Version: 1.0.0
Authors@R: person(given = "Andreas Dominik", family = "Cullmann", role = c("aut", "cre"), email = "fvafrcu@mailbox.org")
Description: Helper functions for package creation, building and maintenance. Designed to work with a build system such as 'GNU make' or package 'fakemake' to help you to conditionally work through the stages of package development (such as spell checking, linting, testing, before building and checking a package).
License: BSD_2_clause + file LICENSE
URL: https://gitlab.com/fvafrCU/packager
Depends: R (>= 3.3.0)
Imports: callr, checkmate, codetools, crayon, cyclocomp, desc, devtools, fakemake, git2r, httr, methods, pkgbuild, pkgload, rcmdcheck, remotes, rprojroot, tools, usethis, utils, whisker, whoami, withr
Suggests: cleanr, covr, digest, knitr, lintr, rmarkdown, roxygen2, RUnit, spelling, testthat
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-01-09 07:42:19 UTC; qwer
Author: Andreas Dominik Cullmann [aut, cre]
Maintainer: Andreas Dominik Cullmann <fvafrcu@mailbox.org>
Repository: CRAN
Date/Publication: 2020-01-13 11:10:06 UTC

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New package serpstatr with initial version 0.0.1
Package: serpstatr
Type: Package
Title: 'Serpstat' API Wrapper
Version: 0.0.1
Authors@R: person('Alex', 'Danilin', email = 'alexnikdanilin@gmail.com', role = c('aut', 'cre'))
URL: https://serpstat.com/api/
Description: The primary goal of 'Serpstat' API <https://serpstat.com/api/> is to reduce manual SEO (search engine optimization) and PPC (pay-per-click) tasks. You can automate your keywords research or competitors analysis with this API wrapper.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: httr (>= 1.4.1)
RoxygenNote: 7.0.2
Suggests: testthat
NeedsCompilation: no
Packaged: 2020-01-09 07:39:13 UTC; mrbubu
Author: Alex Danilin [aut, cre]
Maintainer: Alex Danilin <alexnikdanilin@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-13 10:30:02 UTC

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Package R2BEAT updated to version 1.0.1 with previous version 1.0.0 dated 2019-07-18

Title: Multistage Allocation
Description: Multivariate optimal allocation for different domains in one and two stages stratified sample design. R2BEAT extends the Neyman (1934) <doi:10.2307/2342192> – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). R2BEAT develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples.
Author: Stefano Falorsi <stfalors@istat.it>, Andrea Fasulo <fasulo@istat.it>, Alessio Guandalini <alessio.guandalini@istat.it>, Daniela Pagliuca <pagliuca@istat.it>, Marco Dionisio Terribili <terribili@istat.it>
Maintainer: Andrea Fasulo <fasulo@istat.it>

Diff between R2BEAT versions 1.0.0 dated 2019-07-18 and 1.0.1 dated 2020-01-13

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Package protolite updated to version 2.1 with previous version 2.0 dated 2019-10-10

Title: Highly Optimized Protocol Buffer Serializers
Description: Pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. Currently supports 'rexp.proto' for serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, hence the entire serialization is optimized at compile time. The 'RProtoBuf' package on the other hand uses the protobuf runtime library to provide a general- purpose toolkit for reading and writing arbitrary protocol-buffer data in R.
Author: Jeroen Ooms
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between protolite versions 2.0 dated 2019-10-10 and 2.1 dated 2020-01-13

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Package checkpoint updated to version 0.4.8 with previous version 0.4.7 dated 2019-09-08

Title: Install Packages from Snapshots on the Checkpoint Server for Reproducibility
Description: The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint's checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) Microsoft refreshes the Austria CRAN mirror on the "Microsoft R Archived Network" server (<https://mran.microsoft.com/>). Immediately after completion of the rsync mirror process, the process takes a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17.
Author: Hong Ooi [aut, cre], Andrie de Vries [aut], Microsoft [aut, cph]
Maintainer: Hong Ooi <hongooi@microsoft.com>

Diff between checkpoint versions 0.4.7 dated 2019-09-08 and 0.4.8 dated 2020-01-13

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Package xfun updated to version 0.12 with previous version 0.11 dated 2019-11-12

Title: Miscellaneous Functions by 'Yihui Xie'
Description: Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.
Author: Yihui Xie [aut, cre, cph] (<https://orcid.org/0000-0003-0645-5666>), Daijiang Li [ctb], Xianying Tan [ctb], Salim Brüggemann [ctb]
Maintainer: Yihui Xie <xie@yihui.name>

Diff between xfun versions 0.11 dated 2019-11-12 and 0.12 dated 2020-01-13

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Package trend updated to version 1.1.2 with previous version 1.1.1 dated 2018-07-30

Title: Non-Parametric Trend Tests and Change-Point Detection
Description: The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test.
Author: Thorsten Pohlert [aut, cre] (<https://orcid.org/0000-0003-3855-3025>)
Maintainer: Thorsten Pohlert <thorsten.pohlert@gmx.de>

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Package ssdtools updated to version 0.1.0 with previous version 0.0.3 dated 2018-11-25

Title: Species Sensitivity Distributions
Description: Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al. (2001) <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to fit distributions such as the log-normal, gamma, burr Type-III, log-logistic, log-Gumbel, Gompertz and Weibull. The user can provide custom distributions. Multiple distributions can be averaged using Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by parametric bootstrapping.
Author: Joe Thorley [aut, cre, ctr] (<https://orcid.org/0000-0002-7683-4592>), Carl Schwarz [aut, ctr], Angeline Tillmanns [ctb], Ali Azizishirazi [ctb], Rebecca Fisher [ctb], David Fox [ctb], Kathleen McTavish [ctb], Heather Thompson [ctb], Andy Teucher [ctb], Emilie Doussantousse [ctb], Stephanie Hazlitt [ctb], Nan-Hung Hsieh [ctb], Sergio Ibarra Espinosa [ctb], Province of British Columbia [cph]
Maintainer: Joe Thorley <joe@poissonconsulting.ca>

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Package RPEGLMEN updated to version 1.0.1 with previous version 1.0 dated 2019-09-07

Title: Gamma and Exponential Generalized Linear Models with Elastic Net Penalty
Description: Implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) <arxiv:1804.07780>. An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.
Author: Anthony Christidis <anthony.christidis@stat.ubc.ca>, Xin Chen <chenx26@uw.edu>, Daniel Hanson <hansondj@uw.edu>
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>

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Package rmsfuns updated to version 0.0.0.3 with previous version 0.0.0.2 dated 2017-10-16

Title: Quickly View Data Frames in 'Excel', Build Folder Paths and Create Date Vectors
Description: Contains several useful navigation helper functions, including easily building folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the console prompt to reflect time.
Author: Nico Katzke [aut, cre]
Maintainer: Nico Katzke <nfkatzke@gmail.com>

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Package mlt.docreg updated to version 1.0-4 with previous version 1.0-3 dated 2019-06-23

Title: Most Likely Transformations: Documentation and Regression Tests
Description: Additional documentation, a package vignette and regression tests for package mlt.
Author: Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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Package GBJ updated to version 0.5.3 with previous version 0.5.2 dated 2018-06-27

Title: Generalized Berk-Jones Test for Set-Based Inference in Genetic Association Studies
Description: Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests. The GBJ has been shown to outperform other tests in genetic association studies when signals are correlated and moderately sparse. Please see the vignette for a quickstart guide or the paper at <doi:10.1080/01621459.2019.1660170> for full details.
Author: Ryan Sun [aut, cre]
Maintainer: Ryan Sun <ryansun.work@gmail.com>

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Package EmpiricalCalibration updated to version 2.0.1 with previous version 2.0.0 dated 2019-07-08

Title: Routines for Performing Empirical Calibration of Observational Study Estimates
Description: Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls.
Author: Martijn Schuemie [aut, cre], Marc Suchard [aut]
Maintainer: Martijn Schuemie <schuemie@ohdsi.org>

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Package DramaAnalysis updated to version 3.0.1 with previous version 3.0.0 dated 2019-07-10

Title: Analysis of Dramatic Texts
Description: Analysis of preprocessed dramatic texts, with respect to literary research. The package provides functions to analyze and visualize information about characters, stage directions, the dramatic structure and the text itself. The dramatic texts are expected to be in CSV format, which can be installed from within the package, sample texts are provided. The package and the reasoning behind it are described in Reiter et al. (2017) <doi:10.18420/in2017_119>.
Author: Nils Reiter <nils.reiter@ims.uni-stuttgart.de>
Maintainer: Nils Reiter <nils.reiter@ims.uni-stuttgart.de>

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Package demu updated to version 0.3.0 with previous version 0.2.0 dated 2018-07-28

Title: Optimal Design Emulators via Point Processes
Description: Implements the Determinantal point process (DPP) based optimal design emulator described in Pratola, Lin and Craigmile (2018) <arXiv:1804.02089> for Gaussian process regression models. See <http://www.matthewpratola.com/software> for more information and examples.
Author: Matthew T. Pratola <mpratola@stat.osu.edu> [aut, cre, cph]
Maintainer: Matthew T. Pratola <mpratola@stat.osu.edu>

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Package cascsim updated to version 0.4 with previous version 0.3 dated 2019-06-04

Title: Casualty Actuarial Society Individual Claim Simulator
Description: It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. It generates individual insurance claims including open claims, reopened claims, incurred but not reported claims and future claims. It also includes claim data fitting functions to help set simulation assumptions. It is useful for claim level reserving analysis. Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.
Author: Robert Bear [aut], Kailan Shang [aut, cre], Hai You [aut], Brian Fannin [ctb]
Maintainer: Kailan Shang <klshang81@gmail.com>

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Package C50 updated to version 0.1.3 with previous version 0.1.2 dated 2018-05-22

Title: C5.0 Decision Trees and Rule-Based Models
Description: C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).
Author: Max Kuhn [aut, cre], Steve Weston [ctb], Mark Culp [ctb], Nathan Coulter [ctb], Ross Quinlan [aut] (Author of imported C code), RuleQuest Research [cph] (Copyright holder of imported C code), Rulequest Research Pty Ltd. [cph] (Copyright holder of imported C code)
Maintainer: Max Kuhn <mxkuhn@gmail.com>

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Package BGPhazard updated to version 2.0.0 with previous version 1.2.3 dated 2016-02-11

Title: Markov Beta and Gamma Processes for Modeling Hazard Rates
Description: Computes the hazard rate estimate as described by Nieto-Barajas and Walker (2002), Nieto-Barajas (2003) and Nieto-Barajas, L. E., & Yin, G. (2008).
Author: L. E. Nieto-Barajas, J. A. Garcia Bueno and E.A. Morones Ishikawa
Maintainer: Emilio Akira Morones Ishikawa <emiliomorones@gmail.com>

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Package vcd updated to version 1.4-5 with previous version 1.4-4 dated 2017-12-06

Title: Visualizing Categorical Data
Description: Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).
Author: David Meyer [aut, cre], Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Kurt Hornik [aut], Florian Gerber [ctb], Michael Friendly [ctb]
Maintainer: David Meyer <David.Meyer@R-project.org>

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Package sparsio updated to version 1.0.1 with previous version 1.0.0 dated 2017-06-28

Title: I/O Operations with Sparse Matrices
Description: Fast 'SVMlight' reader and writer. 'SVMlight' is most commonly used format for storing sparse matrices (possibly with some target variable) on disk. For additional information about 'SVMlight' format see <http://svmlight.joachims.org/>.
Author: Dmitriy Selivanov [aut, cre], Felix Riedel [aut]
Maintainer: Dmitriy Selivanov <selivanov.dmitriy@gmail.com>

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Package nLTT updated to version 1.4.3 with previous version 1.4.1 dated 2019-05-18

Title: Calculate the NLTT Statistic
Description: Provides functions to calculate the normalised Lineage-Through- Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures the difference between two Lineage-Through-Time curves, where each curve is normalised both in time and in number of lineages.
Author: Thijs Janzen [aut, cre], Richel Bilderbeek [aut]
Maintainer: Thijs Janzen <thijsjanzen@gmail.com>

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Package bit updated to version 1.1-15 with previous version 1.1-14 dated 2018-05-29

Title: A Class for Vectors of 1-Bit Booleans
Description: True boolean datatype (no NAs), coercion from and to logicals, integers and integer subscripts; fast boolean operators and fast summary statistics. With 'bit' vectors you can store true binary booleans {FALSE,TRUE} at the expense of 1 bit only, on a 32 bit architecture this means factor 32 less RAM and ~ factor 32 more speed on boolean operations. Due to overhead of R calls, actual speed gain depends on the size of the vector: expect gains for vectors of size > 10000 elements. Even for one-time boolean operations it can pay-off to convert to bit, the pay-off is obvious, when such components are used more than once. Reading from and writing to bit is approximately as fast as accessing standard logicals - mostly due to R's time for memory allocation. The package allows to work with pre-allocated memory for return values by calling .Call() directly: when evaluating the speed of C-access with pre-allocated vector memory, coping from bit to logical requires only 70% of the time for copying from logical to logical; and copying from logical to bit comes at a performance penalty of 150%. the package now contains further classes for representing logical selections: 'bitwhich' for very skewed selections and 'ri' for selecting ranges of values for chunked processing. All three index classes can be used for subsetting 'ff' objects (ff-2.1-0 and higher).
Author: Jens Oehlschlägel [aut, cre], Brian Ripley [ctb]
Maintainer: Jens Oehlschlägel <Jens.Oehlschlaegel@truecluster.com>

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Package aws.s3 updated to version 0.3.12.1 with previous version 0.3.12 dated 2018-05-25

Title: 'AWS S3' Client Package
Description: A simple client package for the Amazon Web Services ('AWS') Simple Storage Service ('S3') 'REST' 'API' <https://aws.amazon.com/s3/>.
Author: Thomas J. Leeper [aut, cre] (<https://orcid.org/0000-0003-4097-6326>), Boettiger Carl [ctb], Andrew Martin [ctb], Mark Thompson [ctb], Tyler Hunt [ctb], Steven Akins [ctb], Bao Nguyen [ctb], Thierry Onkelinx [ctb]
Maintainer: ORPHANED

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Package aws.polly updated to version 0.1.2.1 with previous version 0.1.2 dated 2016-12-08

Title: Client for AWS Polly
Description: A client for AWS Polly <http://aws.amazon.com/documentation/polly>, a speech synthesis service.
Author: Thomas J. Leeper [aut, cre]
Maintainer: ORPHANED

Diff between aws.polly versions 0.1.2 dated 2016-12-08 and 0.1.2.1 dated 2020-01-13

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Package shinyjs updated to version 1.1 with previous version 1.0 dated 2018-01-08

Title: Easily Improve the User Experience of Your Shiny Apps in Seconds
Description: Perform common useful JavaScript operations in Shiny apps that will greatly improve your apps without having to know any JavaScript. Examples include: hiding an element, disabling an input, resetting an input back to its original value, delaying code execution by a few seconds, and many more useful functions for both the end user and the developer. 'shinyjs' can also be used to easily call your own custom JavaScript functions from R.
Author: Dean Attali [aut, cre]
Maintainer: Dean Attali <daattali@gmail.com>

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Package qrmtools updated to version 0.0-12 with previous version 0.0-11 dated 2019-12-03

Title: Tools for Quantitative Risk Management
Description: Functions and data sets for reproducing selected results from the book "Quantitative Risk Management: Concepts, Techniques and Tools". Furthermore, new developments and auxiliary functions for Quantitative Risk Management practice.
Author: Marius Hofert [aut, cre], Kurt Hornik [aut], Alexander J. McNeil [aut]
Maintainer: Marius Hofert <marius.hofert@uwaterloo.ca>

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Package nvmix updated to version 0.0-3 with previous version 0.0-2 dated 2019-11-13

Title: Multivariate Normal Variance Mixtures
Description: Functions for working with multivariate normal variance mixture distributions (evaluation of distribution functions and densities, random number generation and parameter estimation), including Student's t distribution for non-integer degrees of freedom.
Author: Marius Hofert [aut, cre], Erik Hintz [aut], Christiane Lemieux [aut]
Maintainer: Marius Hofert <marius.hofert@uwaterloo.ca>

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Package aws.kms (with last version 0.1.2) was removed from CRAN

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

2018-08-01 0.1.2

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Package jabr updated to version 0.1.2 with previous version 0.1.1 dated 2019-12-15

Title: Easy Access for West Java Data
Description: Interface to browse, list, and fetch data about West Java.
Author: Muhammad Aswan Syahputra [aut, cre]
Maintainer: Muhammad Aswan Syahputra <muhammadaswansyahputra@gmail.com>

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Package fsdaR updated to version 0.4-8 with previous version 0.4-6 dated 2019-03-14

Title: Robust Data Analysis Through Monitoring and Dynamic Visualization
Description: Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis (FSDA)' which is a comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis.
Author: Valentin Todorov [aut, cre] (<https://orcid.org/0000-0003-4215-0245>), Emmanuele Sordini [aut], Aldo Corbellini [ctb], Francesca Torti [ctb], Marco Riani [ctb], Domenico Perrotta [ctb], Andrea Cerioli [ctb]
Maintainer: Valentin Todorov <valentin.todorov@chello.at>

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Package ddpcr updated to version 1.12 with previous version 1.11 dated 2019-01-03

Title: Analysis and Visualization of Droplet Digital PCR in R and on the Web
Description: An interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. This is the first non-proprietary software for analyzing two-channel ddPCR data. An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R.
Author: Dean Attali [aut, cre]
Maintainer: Dean Attali <daattali@gmail.com>

Diff between ddpcr versions 1.11 dated 2019-01-03 and 1.12 dated 2020-01-13

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 ddpcr-1.12/ddpcr/MD5                                                                              |  344 +++++-----
 ddpcr-1.12/ddpcr/NEWS.md                                                                          |    6 
 ddpcr-1.12/ddpcr/R/plate-print.R                                                                  |   12 
 ddpcr-1.12/ddpcr/R/utils.R                                                                        |   21 
 ddpcr-1.12/ddpcr/README.md                                                                        |  202 ++---
 ddpcr-1.12/ddpcr/build/vignette.rds                                                               |binary
 ddpcr-1.12/ddpcr/inst/doc/algorithm.html                                                          |   31 
 ddpcr-1.12/ddpcr/inst/doc/extend.html                                                             |  295 ++++----
 ddpcr-1.12/ddpcr/inst/doc/overview.Rmd                                                            |    9 
 ddpcr-1.12/ddpcr/inst/doc/overview.html                                                           |  276 ++++----
 ddpcr-1.12/ddpcr/inst/doc/technical_details.html                                                  |   13 
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 ddpcr-1.12/ddpcr/man/WELL_ID_REGEX.Rd                                                             |    1 
 ddpcr-1.12/ddpcr/man/analysis_complete.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/analyze.Rd                                                                   |    1 
 ddpcr-1.12/ddpcr/man/bind_df_ends.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/calc_negative_freq_simple.Rd                                                 |    1 
 ddpcr-1.12/ddpcr/man/calculate_concentration.Rd                                                   |    1 
 ddpcr-1.12/ddpcr/man/calculate_concentration_single.Rd                                            |    1 
 ddpcr-1.12/ddpcr/man/calculate_neg_freq_single.Rd                                                 |    1 
 ddpcr-1.12/ddpcr/man/calculate_negative_freqs.Rd                                                  |    1 
 ddpcr-1.12/ddpcr/man/capitalize.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/cat0.Rd                                                                      |    1 
 ddpcr-1.12/ddpcr/man/check_step.Rd                                                                |    3 
 ddpcr-1.12/ddpcr/man/classify_droplets.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/classify_droplets.pnpp_experiment.Rd                                         |    1 
 ddpcr-1.12/ddpcr/man/classify_droplets_single.Rd                                                  |    1 
 ddpcr-1.12/ddpcr/man/classify_droplets_single.pnpp_experiment.Rd                                  |    1 
 ddpcr-1.12/ddpcr/man/classify_thresholds.Rd                                                       |    1 
 ddpcr-1.12/ddpcr/man/cluster.Rd                                                                   |    1 
 ddpcr-1.12/ddpcr/man/cluster_name.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/clusters.Rd                                                                  |    1 
 ddpcr-1.12/ddpcr/man/col_to_num.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/custom_thresholds.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/ddpcr.Rd                                                                     |    1 
 ddpcr-1.12/ddpcr/man/ddpcr_plate.Rd                                                               |    1 
 ddpcr-1.12/ddpcr/man/define_clusters.Rd                                                           |    1 
 ddpcr-1.12/ddpcr/man/define_clusters.custom_thresholds.Rd                                         |    1 
 ddpcr-1.12/ddpcr/man/define_clusters.ddpcr_plate.Rd                                               |    1 
 ddpcr-1.12/ddpcr/man/define_clusters.pnpp_experiment.Rd                                           |    1 
 ddpcr-1.12/ddpcr/man/define_params.Rd                                                             |    1 
 ddpcr-1.12/ddpcr/man/define_params.custom_thresholds.Rd                                           |    1 
 ddpcr-1.12/ddpcr/man/define_params.ddpcr_plate.Rd                                                 |    1 
 ddpcr-1.12/ddpcr/man/define_params.fam_positive_pnpp.Rd                                           |    1 
 ddpcr-1.12/ddpcr/man/define_params.hex_positive_pnpp.Rd                                           |    1 
 ddpcr-1.12/ddpcr/man/define_params.pnpp_experiment.Rd                                             |    1 
 ddpcr-1.12/ddpcr/man/define_params.wildtype_mutant_pnpp.Rd                                        |    1 
 ddpcr-1.12/ddpcr/man/define_steps.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/define_steps.custom_thresholds.Rd                                            |    1 
 ddpcr-1.12/ddpcr/man/define_steps.ddpcr_plate.Rd                                                  |    1 
 ddpcr-1.12/ddpcr/man/define_steps.pnpp_experiment.Rd                                              |    1 
 ddpcr-1.12/ddpcr/man/diff.point2d.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/err_msg.Rd                                                                   |    1 
 ddpcr-1.12/ddpcr/man/fam_positive_pnpp.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/get_col.Rd                                                                   |    1 
 ddpcr-1.12/ddpcr/man/get_empty_cutoff.Rd                                                          |    1 
 ddpcr-1.12/ddpcr/man/get_empty_cutoff.ddpcr_plate.Rd                                              |    1 
 ddpcr-1.12/ddpcr/man/get_empty_cutoff.pnpp_experiment.Rd                                          |    1 
 ddpcr-1.12/ddpcr/man/get_filled_border.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/get_filled_drops.Rd                                                          |    1 
 ddpcr-1.12/ddpcr/man/get_outlier_cutoff.Rd                                                        |    1 
 ddpcr-1.12/ddpcr/man/get_outlier_cutoff.ddpcr_plate.Rd                                            |    1 
 ddpcr-1.12/ddpcr/man/get_row.Rd                                                                   |    1 
 ddpcr-1.12/ddpcr/man/get_single_well.Rd                                                           |    1 
 ddpcr-1.12/ddpcr/man/get_wells_btwn.Rd                                                            |    1 
 ddpcr-1.12/ddpcr/man/grapes-btwn-grapes.Rd                                                        |    1 
 ddpcr-1.12/ddpcr/man/grapes-greater-than-grapes.Rd                                                |    1 
 ddpcr-1.12/ddpcr/man/has_signif_negative_cluster.Rd                                               |    1 
 ddpcr-1.12/ddpcr/man/has_step.Rd                                                                  |    1 
 ddpcr-1.12/ddpcr/man/hex_positive_pnpp.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/is_dir.Rd                                                                    |    1 
 ddpcr-1.12/ddpcr/man/is_dirty.Rd                                                                  |    1 
 ddpcr-1.12/ddpcr/man/is_empty_plate.Rd                                                            |    1 
 ddpcr-1.12/ddpcr/man/is_file.Rd                                                                   |    1 
 ddpcr-1.12/ddpcr/man/is_range.Rd                                                                  |    1 
 ddpcr-1.12/ddpcr/man/is_well_success.Rd                                                           |    1 
 ddpcr-1.12/ddpcr/man/is_well_success.ddpcr_plate.Rd                                               |    1 
 ddpcr-1.12/ddpcr/man/is_well_success.pnpp_experiment.Rd                                           |    1 
 ddpcr-1.12/ddpcr/man/launch.Rd                                                                    |    1 
 ddpcr-1.12/ddpcr/man/load_plate.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/local_maxima.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/local_minima.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/lol_to_df.Rd                                                                 |    1 
 ddpcr-1.12/ddpcr/man/mark_clusters.Rd                                                             |    1 
 ddpcr-1.12/ddpcr/man/merge_dfs_overwrite_col.Rd                                                   |    1 
 ddpcr-1.12/ddpcr/man/meta_var_name.Rd                                                             |    1 
 ddpcr-1.12/ddpcr/man/move_back.Rd                                                                 |    1 
 ddpcr-1.12/ddpcr/man/move_front.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/msg.Rd                                                                       |    1 
 ddpcr-1.12/ddpcr/man/name.Rd                                                                      |    1 
 ddpcr-1.12/ddpcr/man/named_vec_to_df.Rd                                                           |    1 
 ddpcr-1.12/ddpcr/man/new_plate.Rd                                                                 |    2 
 ddpcr-1.12/ddpcr/man/next_step.Rd                                                                 |    1 
 ddpcr-1.12/ddpcr/man/normalize_to_rds.Rd                                                          |    1 
 ddpcr-1.12/ddpcr/man/num_to_col.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/num_to_row.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/other_dim.Rd                                                                 |    1 
 ddpcr-1.12/ddpcr/man/params.Rd                                                                    |    1 
 ddpcr-1.12/ddpcr/man/parent_plate_type.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/parent_plate_type.ddpcr_plate.Rd                                             |    1 
 ddpcr-1.12/ddpcr/man/parent_plate_type.default.Rd                                                 |    1 
 ddpcr-1.12/ddpcr/man/parent_plate_type.fam_positive_pnpp.Rd                                       |    1 
 ddpcr-1.12/ddpcr/man/parent_plate_type.hex_positive_pnpp.Rd                                       |    1 
 ddpcr-1.12/ddpcr/man/parent_plate_type.wildtype_mutant_pnpp.Rd                                    |    1 
 ddpcr-1.12/ddpcr/man/plate_data-set.Rd                                                            |    1 
 ddpcr-1.12/ddpcr/man/plate_data.Rd                                                                |    3 
 ddpcr-1.12/ddpcr/man/plate_meta-set.Rd                                                            |    1 
 ddpcr-1.12/ddpcr/man/plate_meta.Rd                                                                |    7 
 ddpcr-1.12/ddpcr/man/plate_types.Rd                                                               |    1 
 ddpcr-1.12/ddpcr/man/plot.custom_thresholds.Rd                                                    |    1 
 ddpcr-1.12/ddpcr/man/plot.ddpcr_plate.Rd                                                          |   24 
 ddpcr-1.12/ddpcr/man/plot.pnpp_experiment.Rd                                                      |    1 
 ddpcr-1.12/ddpcr/man/plot.wildtype_mutant_pnpp.Rd                                                 |    1 
 ddpcr-1.12/ddpcr/man/pnpp_experiment.Rd                                                           |only
 ddpcr-1.12/ddpcr/man/point2d.Rd                                                                   |    1 
 ddpcr-1.12/ddpcr/man/positive_dim.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/positive_dim_var.Rd                                                          |    1 
 ddpcr-1.12/ddpcr/man/positive_name.Rd                                                             |    5 
 ddpcr-1.12/ddpcr/man/print.ddpcr_plate.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/quiet.Rd                                                                     |    1 
 ddpcr-1.12/ddpcr/man/range_list_to_vec.Rd                                                         |    1 
 ddpcr-1.12/ddpcr/man/range_to_endpoints.Rd                                                        |    1 
 ddpcr-1.12/ddpcr/man/range_to_seq.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/reclassify_droplets.Rd                                                       |    1 
 ddpcr-1.12/ddpcr/man/reclassify_droplets.pnpp_experiment.Rd                                       |    1 
 ddpcr-1.12/ddpcr/man/reclassify_droplets_single.Rd                                                |    1 
 ddpcr-1.12/ddpcr/man/reclassify_droplets_single.pnpp_experiment.Rd                                |    1 
 ddpcr-1.12/ddpcr/man/remove_empty.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/remove_empty.ddpcr_plate.Rd                                                  |    1 
 ddpcr-1.12/ddpcr/man/remove_empty.pnpp_experiment.Rd                                              |    1 
 ddpcr-1.12/ddpcr/man/remove_failures.Rd                                                           |    1 
 ddpcr-1.12/ddpcr/man/remove_failures.ddpcr_plate.Rd                                               |    1 
 ddpcr-1.12/ddpcr/man/remove_outliers.Rd                                                           |    1 
 ddpcr-1.12/ddpcr/man/remove_outliers.ddpcr_plate.Rd                                               |    1 
 ddpcr-1.12/ddpcr/man/reset.Rd                                                                     |    1 
 ddpcr-1.12/ddpcr/man/row_to_num.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/sample_data.Rd                                                               |    3 
 ddpcr-1.12/ddpcr/man/save_plate.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/set_default_params.Rd                                                        |    1 
 ddpcr-1.12/ddpcr/man/status.Rd                                                                    |    3 
 ddpcr-1.12/ddpcr/man/step.Rd                                                                      |    1 
 ddpcr-1.12/ddpcr/man/step_begin.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/step_end.Rd                                                                  |    1 
 ddpcr-1.12/ddpcr/man/step_name.Rd                                                                 |    1 
 ddpcr-1.12/ddpcr/man/steps.Rd                                                                     |    1 
 ddpcr-1.12/ddpcr/man/subset.ddpcr_plate.Rd                                                        |    5 
 ddpcr-1.12/ddpcr/man/thresholds.Rd                                                                |    3 
 ddpcr-1.12/ddpcr/man/type.Rd                                                                      |    5 
 ddpcr-1.12/ddpcr/man/unanalyzed_clusters.Rd                                                       |    1 
 ddpcr-1.12/ddpcr/man/variable_dim.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/variable_dim_var.Rd                                                          |    1 
 ddpcr-1.12/ddpcr/man/warn_msg.Rd                                                                  |    1 
 ddpcr-1.12/ddpcr/man/well_info.Rd                                                                 |    1 
 ddpcr-1.12/ddpcr/man/wells_mutant.Rd                                                              |    1 
 ddpcr-1.12/ddpcr/man/wells_negative.Rd                                                            |    1 
 ddpcr-1.12/ddpcr/man/wells_positive.Rd                                                            |    1 
 ddpcr-1.12/ddpcr/man/wells_success.Rd                                                             |    3 
 ddpcr-1.12/ddpcr/man/wells_used.Rd                                                                |    1 
 ddpcr-1.12/ddpcr/man/wells_wildtype.Rd                                                            |    1 
 ddpcr-1.12/ddpcr/man/wildtype_mutant_pnpp.Rd                                                      |    1 
 ddpcr-1.12/ddpcr/man/x_threshold.Rd                                                               |    1 
 ddpcr-1.12/ddpcr/man/x_var.Rd                                                                     |    3 
 ddpcr-1.12/ddpcr/man/y_threshold.Rd                                                               |    1 
 ddpcr-1.12/ddpcr/vignettes/overview.Rmd                                                           |    9 
 174 files changed, 665 insertions(+), 767 deletions(-)

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