Wed, 10 Jul 2019

Package pbmcapply updated to version 1.5.0 with previous version 1.4.1 dated 2019-04-01

Title: Tracking the Progress of Mc*pply with Progress Bar
Description: A light-weight package helps you track and visualize the progress of parallel version of vectorized R functions (mc*apply). Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to the lack of fork() functionality, which is essential for mc*apply, on Windows.
Author: Kevin Kuang (aut), Quyu Kong (ctb), Francesco Napolitano (ctb)
Maintainer: Kevin kuang <kvn.kuang@mail.utoronto.ca>

Diff between pbmcapply versions 1.4.1 dated 2019-04-01 and 1.5.0 dated 2019-07-10

 DESCRIPTION    |    6 +++---
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 R/pbmcmapply.R |   50 +++++++++++++++++++++++++++++++++++---------------
 R/utils.R      |   32 ++++++++++----------------------
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Permanent link

Package ds updated to version 4.0 with previous version 3.0 dated 2014-09-02

Title: Descriptive Statistics
Description: Performs various analyzes of descriptive statistics, including correlations, graphics and tables.
Author: Emmanuel Arnhold
Maintainer: Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>

Diff between ds versions 3.0 dated 2014-09-02 and 4.0 dated 2019-07-10

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More information about ds at CRAN
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Package asymmetry updated to version 2.0.2 with previous version 2.0 dated 2018-10-15

Title: Multidimensional Scaling of Asymmetric Data
Description: Multidimensional scaling models and methods for the visualization for asymmetric data <doi:10.1111/j.2044-8317.1996.tb01078.x>. An asymmetric matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example is a student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides the slide-vector model <doi:10.1007/BF02294474>, a scaling model with unique dimensions and the asymscal model for asymmetric multidimensional scaling. Furthermore, a heat map for skew-symmetric data, and the decomposition of asymmetry are provided for the analysis of asymmetric tables.
Author: Berrie Zielman
Maintainer: Berrie Zielman <berrie.zielman@gmail.com>

Diff between asymmetry versions 2.0 dated 2018-10-15 and 2.0.2 dated 2019-07-10

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More information about asymmetry at CRAN
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Package retistruct updated to version 0.6.0 with previous version 0.5.12 dated 2017-08-09

Title: Retinal Reconstruction Program
Description: Reconstructs retinae by morphing a flat surface with cuts (a dissected flat-mount retina) onto a curvilinear surface (the standard retinal shape). It can estimate the position of a point on the intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates.
Author: David C. Sterratt [aut, cre, cph], Daniel Lyngholm [aut, cph]
Maintainer: David C. Sterratt <david.c.sterratt@ed.ac.uk>

Diff between retistruct versions 0.5.12 dated 2017-08-09 and 0.6.0 dated 2019-07-10

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Package VCA updated to version 1.4.0 with previous version 1.3.4 dated 2018-07-18

Title: Variance Component Analysis
Description: ANOVA and REML estimation of linear mixed models is implemented, once following Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package. The primary objective of this package is to perform a variance component analysis (VCA) according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement Procedures" (2014). There are plotting methods for visualization of an experimental design, plotting random effects and residuals. For ANOVA type estimation two methods for computing ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of variance components can be derived, which is used in estimating confidence intervals. Linear hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific values of covariables and with custom weighting schemes for factor variables. See ?VCA for a more comprehensive description of the features.
Author: Andre Schuetzenmeister [aut, cre], Florian Dufey [aut]
Maintainer: Andre Schuetzenmeister <andre.schuetzenmeister@roche.com>

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Package portalr updated to version 0.2.6 with previous version 0.2.5 dated 2019-06-22

Title: Create Useful Summaries of the Portal Data
Description: Download and generate summaries for the rodent, plant, ant, and weather data from the Portal Project. Portal is a long-term (and ongoing) experimental monitoring site in the Chihuahua desert. The raw data files can be found at <https://github.com/weecology/portaldata>.
Author: Glenda M. Yenni [aut, cre] (<https://orcid.org/0000-0001-6969-1848>), Hao Ye [aut] (<https://orcid.org/0000-0002-8630-1458>), Erica M. Christensen [aut] (<https://orcid.org/0000-0002-5635-2502>), Juniper L. Simonis [aut] (<https://orcid.org/0000-0001-9798-0460>), Ellen K. Bledsoe [aut] (<https://orcid.org/0000-0002-3629-7235>), Renata M. Diaz [aut] (<https://orcid.org/0000-0003-0803-4734>), Shawn D. Taylor [aut] (<https://orcid.org/0000-0002-6178-6903>), Ethan P, White [aut] (<https://orcid.org/0000-0001-6728-7745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000-0002-6026-8530>)
Maintainer: Glenda M. Yenni <glenda@weecology.org>

Diff between portalr versions 0.2.5 dated 2019-06-22 and 0.2.6 dated 2019-07-10

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Package jaatha updated to version 3.2.1 with previous version 3.2.0 dated 2016-05-13

Title: Simulation-Based Maximum Likelihood Parameter Estimation
Description: An estimation method that can use computer simulations to approximate maximum-likelihood estimates even when the likelihood function can not be evaluated directly. It can be applied whenever it is feasible to conduct many simulations, but works best when the data is approximately Poisson distributed. It was originally designed for demographic inference in evolutionary biology. It has optional support for conducting coalescent simulation using the 'coala' package.
Author: Paul Staab [aut, cre], Lisha Mathew [aut], Dirk Metzler [aut, ths]
Maintainer: Paul Staab <develop@paulstaab.de>

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Package GSED updated to version 2.3 with previous version 2.1 dated 2019-06-14

Title: Group Sequential Enrichment Design
Description: Provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.
Author: Marie-Karelle Riviere
Maintainer: Marie-Karelle Riviere <eldamjh@gmail.com>

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Package gets updated to version 0.19 with previous version 0.18 dated 2019-02-25

Title: General-to-Specific (GETS) Modelling and Indicator Saturation Methods
Description: Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean.
Author: Genaro Sucarrat [aut, cre], Felix Pretis [aut], James Reade [aut]
Maintainer: Genaro Sucarrat <genaro.sucarrat@bi.no>

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Package rayshader updated to version 0.11.4 with previous version 0.10.1 dated 2019-02-20

Title: Create and Visualize Hillshaded Maps from Elevation Matrices
Description: Uses a combination of raytracing, spherical texture mapping, lambertian reflectance, and ambient occlusion to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hillshading, 2D and 3D plotting options, and the ability to export 3D visualizations to a 3D printable format.
Author: Tyler Morgan-Wall
Maintainer: Tyler Morgan-Wall <tylermw@gmail.com>

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Package raster updated to version 2.9-22 with previous version 2.9-5 dated 2019-05-14

Title: Geographic Data Analysis and Modeling
Description: Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported. There is a also support for vector data operations such as intersections. See the manual and tutorials on <https://rspatial.org/> to get started.
Author: Robert J. Hijmans [cre, aut] (<https://orcid.org/0000-0001-5872-2872>), Jacob van Etten [ctb], Michael Sumner [ctb], Joe Cheng [ctb], Andrew Bevan [ctb], Roger Bivand [ctb], Lorenzo Busetto [ctb], Mort Canty [ctb], David Forrest [ctb], Aniruddha Ghosh [ctb], Duncan Golicher [ctb], Josh Gray [ctb], Jonathan A. Greenberg [ctb], Paul Hiemstra [ctb], Institute for Mathematics Applied Geosciences [cph], Charles Karney [ctb], Matteo Mattiuzzi [ctb], Steven Mosher [ctb], Jakub Nowosad [ctb], Edzer Pebesma [ctb], Oscar Perpinan Lamigueiro [ctb], Etienne B. Racine [ctb], Barry Rowlingson [ctb], Ashton Shortridge [ctb], Bill Venables [ctb], Rafael Wueest [ctb]
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

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More information about raster at CRAN
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Package merDeriv updated to version 0.1-7 with previous version 0.1-6 dated 2018-11-30

Title: Case-Wise and Cluster-Wise Derivatives for Mixed Effects Models
Description: Compute case-wise and cluster-wise derivative for mixed effects models with respect to fixed effects parameter, random effect (co)variances, and residual variance.
Author: Ting Wang [aut, cre], Edgar Merkle [aut] (<https://orcid.org/0000-0001-7158-0653>), Yves Rosseel [ctb]
Maintainer: Ting Wang <twb8d@mail.missouri.edu>

Diff between merDeriv versions 0.1-6 dated 2018-11-30 and 0.1-7 dated 2019-07-10

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New package idealstan with initial version 0.7.2
Package: idealstan
Type: Package
Title: Generalized IRT Ideal Point Models with 'Stan'
Version: 0.7.2
Date: 2019-07-08
Authors@R: c(person("Robert","Kubinec", role = c("aut","cre"), email = "rmk7@nyu.edu"), person("Jonah", "Gabry", role = "ctb"), person("Ben", "Goodrich", role = "ctb"), person("Trustees of Columbia University",role='cph'))
BugReports: https://github.com/saudiwin/idealstan/issues
Description: Offers item-response theory (IRT) ideal-point estimation for binary, ordinal, counts and continuous responses with time-varying and missing-data inference. Full and approximate Bayesian sampling with 'Stan' (<https://mc-stan.org/>).
License: GPL
Depends: methods, R (>= 3.4.0), Rcpp (>= 0.12.18)
Imports: rstan (>= 2.18.2), rstantools (>= 1.5.1), dplyr, svDialogs, scales, tidyr, stringr, bayesplot, ggplot2, lazyeval, rlang, shinystan, gghighlight, forcats, ggrepel
Suggests: pscl, loo, lubridate, R.rsp
LinkingTo: StanHeaders (>= 2.18.0), rstan (>= 2.18.2), BH (>= 1.66), Rcpp (>= 0.12.7), RcppEigen (>= 0.3.3.3.0)
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
SystemRequirements: GNU make
VignetteBuilder: R.rsp
RoxygenNote: 6.1.1
Packaged: 2019-07-10 13:37:13 UTC; rmk
Author: Robert Kubinec [aut, cre], Jonah Gabry [ctb], Ben Goodrich [ctb], Trustees of Columbia University [cph]
Maintainer: Robert Kubinec <rmk7@nyu.edu>
Repository: CRAN
Date/Publication: 2019-07-10 15:00:03 UTC

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Package graphTweets updated to version 0.5.2 with previous version 0.5.1 dated 2019-02-14

Title: Visualise Twitter Interactions
Description: Allows building an edge table from data frame of tweets, also provides function to build nodes and another create a temporal graph.
Author: John Coene [aut, cre]
Maintainer: John Coene <jcoenep@gmail.com>

Diff between graphTweets versions 0.5.1 dated 2019-02-14 and 0.5.2 dated 2019-07-10

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Package FractalParameterEstimation updated to version 1.1.2 with previous version 1.1.1 dated 2017-09-12

Title: Simulation and Parameter Estimation of Randomized Sierpinski Carpets using the p-p-p-q-Model
Description: The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) <doi:10.1002/sim.6497>.
Author: Philipp Hermann, Jozef Kiselak, Milan Stehlik
Maintainer: Philipp Hermann <philipp.hermann@jku.at>

Diff between FractalParameterEstimation versions 1.1.1 dated 2017-09-12 and 1.1.2 dated 2019-07-10

 DESCRIPTION |   10 +++++-----
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 3 files changed, 8 insertions(+), 8 deletions(-)

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Package dti updated to version 1.4.1 with previous version 1.4 dated 2019-05-09

Title: Analysis of Diffusion Weighted Imaging (DWI) Data
Description: Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.
Author: Karsten Tabelow [aut, cre], Joerg Polzehl [aut], Felix Anker [ctb]
Maintainer: Karsten Tabelow <karsten.tabelow@wias-berlin.de>

Diff between dti versions 1.4 dated 2019-05-09 and 1.4.1 dated 2019-07-10

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Package depmixS4 updated to version 1.4-0 with previous version 1.3-5 dated 2018-08-24

Title: Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4
Description: Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).
Author: Ingmar Visser <i.visser@uva.nl>, Maarten Speekenbrink <m.speekenbrink@ucl.ac.uk>
Maintainer: Ingmar Visser <i.visser@uva.nl>

Diff between depmixS4 versions 1.3-5 dated 2018-08-24 and 1.4-0 dated 2019-07-10

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Package dataMaid updated to version 1.3.1 with previous version 1.3.0 dated 2019-06-07

Title: A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process
Description: Data screening is an important first step of any statistical analysis. dataMaid auto generates a customizable data report with a thorough summary of the checks and the results that a human can use to identify possible errors. It provides an extendable suite of test for common potential errors in a dataset.
Author: Anne Helby Petersen [aut], Claus Thorn Ekstrøm [aut, cre]
Maintainer: Claus Thorn Ekstrøm <ekstrom@sund.ku.dk>

Diff between dataMaid versions 1.3.0 dated 2019-06-07 and 1.3.1 dated 2019-07-10

 DESCRIPTION                      |   12 ++++++------
 MD5                              |    6 +++---
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 4 files changed, 26 insertions(+), 24 deletions(-)

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New package twitterwidget with initial version 0.1.1
Package: twitterwidget
Type: Package
Title: Render a Twitter Status in R Markdown Pages
Version: 0.1.1
Authors@R: person("Guido", "Volpi", email="idrivefoxes@gmail.com", role=c("aut", "cre"))
URL: https://github.com/guivo/twitterwidget
BugReports: https://github.com/guivo/twitterwidget/issues
Maintainer: Guido Volpi <idrivefoxes@gmail.com>
Description: Include the Twitter status widgets in HTML pages created using R markdown. The package uses the Twitter javascript APIs to embed in your document Twitter cards associated to specific statuses. The main targets are regular HTML pages or dashboards.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: htmlwidgets
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-06-28 14:35:27 UTC; gvolpi
Author: Guido Volpi [aut, cre]
Repository: CRAN
Date/Publication: 2019-07-10 12:20:04 UTC

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New package shinyglide with initial version 0.1.1
Package: shinyglide
Type: Package
Title: Glide Component for Shiny Applications
Version: 0.1.1
Date: 2019-07-01
Authors@R: c( person("Julien", "Barnier", role=c("aut","cre"), email="julien.barnier@ens-lyon.fr") )
Maintainer: Julien Barnier <julien.barnier@ens-lyon.fr>
Description: Insert Glide JavaScript component into Shiny applications for carousel or assistant-like user interfaces.
License: GPL (>= 3)
VignetteBuilder: knitr
URL: https://juba.github.io/shinyglide/, https://github.com/juba/shinyglide
BugReports: https://github.com/juba/shinyglide/issues
Encoding: UTF-8
Imports: shiny (>= 1.2.0), htmltools
Suggests: knitr, rmarkdown
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-07-01 09:40:21 UTC; julien
Author: Julien Barnier [aut, cre]
Repository: CRAN
Date/Publication: 2019-07-10 12:20:40 UTC

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New package mlr3misc with initial version 0.1.0
Package: mlr3misc
Title: Helper Functions for 'mlr3'
Version: 0.1.0
Authors@R: person(given = "Michel", family = "Lang", role = c("cre", "aut"), email = "michellang@gmail.com", comment = c(ORCID = "0000-0001-9754-0393"))
Description: Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
License: LGPL-3
URL: https://mlr3misc.mlr-org.com
BugReports: https://github.com/mlr-org/mlr3misc/issues
Depends: R (>= 3.1.0)
Imports: backports, checkmate, data.table, R6
Suggests: testthat
Encoding: UTF-8
NeedsCompilation: no
RoxygenNote: 6.1.1
Packaged: 2019-07-09 16:39:53 UTC; michel
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>)
Maintainer: Michel Lang <michellang@gmail.com>
Repository: CRAN
Date/Publication: 2019-07-10 12:20:44 UTC

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New package MinBAR with initial version 1.1.0
Type: Package
Package: MinBAR
Title: Determining the Minimal Background Area for Species Distribution Models
Version: 1.1.0
Authors@R: c( person("Xavier", "Rotllan-Puig", email = "xavier.rotllan.puig@aster-projects.cat", role = c("aut", "cre")), person("Anna", "Traveset", email = "atraveset@imedea.uib-csic.es", role = c("aut")))
Description: A versatile tool that aims at (1) defining what is the minimum background extent necessary to fit good partial species distribution models and/or (2) determining if the background area used to fit a partial species distribution model is reliable enough to extract ecologically relevant conclusions from it. See Rotllan-Puig, X. & Traveset, A. (2019) <doi:10.1101/571182>.
Depends: R (>= 3.4.0)
Imports: raster, rgdal, sp, maxnet, dismo (>= 1.1-4), ecospat (>= 2.2.0), geosphere (>= 1.5-5), lattice, latticeExtra
Suggests: knitr, rmarkdown
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
URL: https://github.com/xavi-rp/MinBAR
BugReports: https://github.com/xavi-rp/MinBAR/issues
NeedsCompilation: no
Packaged: 2019-07-03 15:34:38 UTC; xavi_rp
Author: Xavier Rotllan-Puig [aut, cre], Anna Traveset [aut]
Maintainer: Xavier Rotllan-Puig <xavier.rotllan.puig@aster-projects.cat>
Repository: CRAN
Date/Publication: 2019-07-10 12:30:03 UTC

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New package DramaAnalysis with initial version 3.0.0
Package: DramaAnalysis
Type: Package
Title: Analysis of Dramatic Texts
Version: 3.0.0
Date: 2019-06-20
Author: Nils Reiter <nils.reiter@ims.uni-stuttgart.de>, Tim Strohmayer <st151528@stud.uni-stuttgart.de>
Maintainer: Nils Reiter <nils.reiter@ims.uni-stuttgart.de>
Description: Analysis of preprocessed dramatic texts, with respect to literary research. The package provides functions to analyse and visualise 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>.
URL: https://github.com/quadrama/DramaAnalysis
BugReports: https://github.com/quadrama/DramaAnalysis/issues
License: GPL (>= 3)
LazyData: TRUE
Imports: stats, utils, reshape2 (>= 1.4.2), readr (>= 1.1.1), data.table (>= 1.10.4), httr (>= 1.2.1), git2r (>= 0.24.0), xml2 (>= 1.2.0), tokenizers (>= 0.2.1), stringr (>= 1.4.0)
Depends: R (>= 3.3.0)
RoxygenNote: 6.1.1
Suggests: testthat, fmsb, knitr, magrittr, highcharter, rmarkdown (>= 1.8), igraph (>= 1.1.2)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-07-03 13:21:24 UTC; reiterns
Repository: CRAN
Date/Publication: 2019-07-10 12:30:07 UTC

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Package shapper updated to version 0.1.1 with previous version 0.1.0 dated 2019-03-02

Title: Wrapper of Python Library 'shap'
Description: Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
Author: Szymon Maksymiuk [aut, cre], Alicja Gosiewska [aut], Przemyslaw Biecek [aut], Mateusz Staniak [ctb], Michal Burdukiewicz [ctb]
Maintainer: Szymon Maksymiuk <sz.maksymiuk@gmail.com>

Diff between shapper versions 0.1.0 dated 2019-03-02 and 0.1.1 dated 2019-07-10

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 30 files changed, 1945 insertions(+), 1978 deletions(-)

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Package rbi.helpers updated to version 0.3.1 with previous version 0.3.0 dated 2019-02-22

Title: 'RBi' Helper Functions
Description: Contains a collection of helper functions to use with 'RBi', the R interface to 'LibBi', described in Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in 'LibBi' results and R time/dates.
Author: Sebastian Funk <sebastian.funk@lshtm.ac.uk>
Maintainer: Sebastian Funk <sebastian.funk@lshtm.ac.uk>

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New package cdcsis with initial version 2.0.3
Package: cdcsis
Type: Package
Title: Conditional Distance Correlation Based Feature Screening and Conditional Independence Inference
Description: Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <http://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.
Version: 2.0.3
Date: 2019-7-9
Author: Wenhao Hu, Mian Huang, Wenliang Pan, Xueqin Wang, Canhong Wen, Yuan Tian, Heping Zhang, Jin Zhu
Depends: R(>= 3.0.1)
Imports: ks (>= 1.8.0), mvtnorm, utils, Rcpp
Suggests: testthat
Maintainer: Jin Zhu <zhuj37@mail2.sysu.edu.cn>
License: GPL (>= 2)
NeedsCompilation: yes
Encoding: UTF-8
RoxygenNote: 6.1.1
LinkingTo: Rcpp
URL: https://github.com/Mamba413/cdcsis
BugReports: https://github.com/Mamba413/cdcsis/issues
Packaged: 2019-07-10 09:06:44 UTC; Mamba
Repository: CRAN
Date/Publication: 2019-07-10 11:30:03 UTC

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Package rbi updated to version 0.10.2 with previous version 0.10.1 dated 2019-01-08

Title: R Interface to LibBi
Description: Provides a complete R interface to LibBi, a library for Bayesian inference (see <http://libbi.org> and <doi:10.18637/jss.v067.i10> for more information). This includes functions for manipulating LibBi models, for reading and writing LibBi input/output files, for converting LibBi output to provide traces for use with the coda package, and for running LibBi from R.
Author: Pierre E. Jacob [aut], Anthony Lee [ctb], Lawrence M. Murray [ctb], Sebastian Funk [aut, cre], Sam Abbott [ctb]
Maintainer: Sebastian Funk <sebastian.funk@lshtm.ac.uk>

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Package RItools updated to version 0.1-17 with previous version 0.1-16.1 dated 2019-05-26

Title: Randomization Inference Tools
Description: Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.
Author: Jake Bowers <jwbowers@illinois.edu>, Mark Fredrickson <mark.m.fredrickson@gmail.com>, and Ben Hansen <ben.hansen@umich.edu>
Maintainer: Jake Bowers <jwbowers@illinois.edu>

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New package mpath with initial version 0.3-16
Package: mpath
Title: Regularized Linear Models
Version: 0.3-16
Date: 2019-06-19
Author: Zhu Wang, with contributions from Achim Zeileis, Simon Jackman, Brian Ripley, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Gil Chu and Patrick Breheny
Maintainer: Zhu Wang <wangz1@uthscsa.edu>
Description: Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP), and each possibly combining with L_2 penalty. See Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>.
Imports: MASS,glmnet,pscl,numDeriv, foreach, doParallel, bst
Depends: methods
Suggests: zic, R.rsp, knitr, gdata
VignetteBuilder: R.rsp, knitr
License: GPL-2
Packaged: 2019-07-09 22:37:41 UTC; zhu
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2019-07-10 08:40:03 UTC

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Package FLSSS updated to version 8.5.5 with previous version 8.5.2 dated 2019-01-11

Title: Mining Rigs for Specialized Subset Sum, Multi-Subset Sum, Multidimensional Subset Sum, Multidimensional Knapsack, Generalized Assignment Problems
Description: Specialized solvers for combinatorial optimization problems in the Subset Sum family. These solvers differ from the mainstream in the options of (i) restricting subset size, (ii) bounding subset elements, (iii) mining real-value sets with predefined subset sum errors, and (iv) finding one or more subsets in limited time. A novel algorithm for mining the one-dimensional Subset Sum induced algorithms for the multi-Subset Sum and the multidimensional Subset Sum. The latter decomposes the problem in a novel approach, and the multi-threaded framework offers exact algorithms to the multidimensional Knapsack and the Generalized Assignment problems. Package updates include (a) renewed implementation of the multi-Subset Sum, multidimensional Knapsack and Generalized Assignment solvers; (b) availability of bounding solution space in the multidimensional Subset Sum; (c) fundamental data structure and architectural changes for enhanced cache locality and better chance of SIMD vectorization; (d) an option of mapping real-domain problems to the integer domain with user-controlled precision loss, and those integers are further zipped non-uniformly in 64-bit buffers. Arithmetic on compressed integers is done by bit-manipulation and the design has virtually zero speed lag relative to normal integers arithmetic. The consequent reduction in dimensionality may yield substantial acceleration. Compilation with g++ '-Ofast' is recommended. See package vignette (<arXiv:1612.04484v3>) for details. Functions prefixed with 'aux' (auxiliary) are or will be implementations of existing foundational or cutting-edge algorithms for solving optimization problems of interest.
Author: Charlie Wusuo Liu
Maintainer: Charlie Wusuo Liu <liuwusuo@gmail.com>

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Package detzrcr updated to version 0.2.6 with previous version 0.2.5 dated 2019-03-27

Title: Compare Detrital Zircon Suites
Description: Compare detrital zircon suites by uploading univariate, U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based user-interface. Outputs publication quality figures using 'ggplot2', and tables of statistics currently in use in the detrital zircon geochronology community.
Author: Magnus Kristoffersen [aut, cre]
Maintainer: Magnus Kristoffersen <magnus.kristoffersen@geo.uio.no>

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Package pvaluefunctions updated to version 1.3.0 with previous version 1.2.0 dated 2019-06-13

Title: Creates and Plots P-Value Functions, S-Value Functions, Confidence Distributions and Confidence Densities
Description: Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; and Greenland S. (2019) <doi:10.1080/00031305.2018.1529625>.
Author: Denis Infanger [aut, cre] (<https://orcid.org/0000-0001-9028-7110>)
Maintainer: Denis Infanger <denis.infanger@unibas.ch>

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Package mice updated to version 3.6.0 with previous version 3.5.0 dated 2019-05-13

Title: Multivariate Imputation by Chained Equations
Description: Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Author: Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Alexander Robitzsch [ctb], Gerko Vink [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Rianne Schouten [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb]
Maintainer: Stef van Buuren <stef.vanbuuren@tno.nl>

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Package metaBMA updated to version 0.6.1 with previous version 0.3.9 dated 2017-08-04

Title: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis
Description: Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators.
Author: Daniel W. Heck [aut, cre], Quentin F. Gronau [ctb]
Maintainer: Daniel W. Heck <heck@uni-mannheim.de>

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Package IndexNumR updated to version 0.1.2 with previous version 0.1.1 dated 2019-01-22

Title: Index Number Calculation
Description: Computes bilateral and multilateral index numbers. It has support for several standard bilateral indices as well as the GEKS multilateral index number methods (see Ivancic, Diewert and Fox (2011) <doi:10.1016/j.jeconom.2010.09.003>) . It also supports updating of GEKS indexes using several splicing methods.
Author: Graham White
Maintainer: Graham White <g.white@unswalumni.com>

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Package concurve updated to version 2.0 with previous version 1.0.6 dated 2019-05-14

Title: Computes and Plots Consonance (Confidence) Intervals, P-Values, and S-Values to Form Consonance and Surprisal Functions
Description: Allows one to compute consonance (confidence) intervals for various statistical tests along with their corresponding P-values and S-values. The intervals can be plotted to create consonance and surprisal functions allowing one to see what effect sizes are compatible with the test model at various consonance levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) <doi:10.2105/AJPH.77.2.195>, Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>, Singh K, Xie M, Strawderman WE. (2007) <arXiv:0708.0976>, Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>, and Greenland S. (2019) <doi:10.1080/00031305.2018.1529625>.
Author: Zad R. Chow [aut, cre] (<https://orcid.org/0000-0003-1545-8199>), Andrew D. Vigotsky [aut] (<https://orcid.org/0000-0003-3166-0688>)
Maintainer: Zad R. Chow <zad@lesslikely.com>

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Package BSL updated to version 3.0.0 with previous version 2.0.0 dated 2019-01-16

Title: Bayesian Synthetic Likelihood
Description: Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of three methods (BSL, uBSL and semiBSL) and two shrinkage estimations (graphical lasso and Warton's estimation). uBSL (Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust to non-normal summary statistics. Shrinkage estimations can help to bring down the number of simulations when the dimension of the summary statistic is high (e.g., BSLasso, An et al. (2019) <doi:10.1080/10618600.2018.1537928>). Extensions to this package are planned.
Author: Ziwen An [aut, cre] (<https://orcid.org/0000-0002-9947-5182>), Leah F. South [aut] (<https://orcid.org/0000-0002-5646-2963>), Christopher C. Drovandi [aut] (<https://orcid.org/0000-0001-9222-8763>)
Maintainer: Ziwen An <ziwen.an@hdr.qut.edu.au>

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Package UCSCXenaShiny updated to version 0.2.0 with previous version 0.1.1 dated 2019-05-19

Title: A Shiny App for UCSC Xena Database
Description: Provides a web app for downloading, analyzing and visualizing datasets from UCSC Xena, which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Author: Shixiang Wang [aut, cre] (<https://orcid.org/0000-0001-9855-7357>), Yi Xiong [aut] (<https://orcid.org/0000-0002-4370-9824>), Longfei Zhao [aut] (<https://orcid.org/0000-0002-6277-0137>), Kai Gu [aut] (<https://orcid.org/0000-0002-0177-0774>), Yin Li [aut], Fei Zhao [aut]
Maintainer: Shixiang Wang <w_shixiang@163.com>

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Package propr updated to version 4.1.6 with previous version 4.1.1 dated 2018-11-19

Title: Calculating Proportionality Between Vectors of Compositional Data
Description: The bioinformatic evaluation of gene co-expression often begins with correlation-based analyses. However, correlation lacks validity when applied to relative data, including count data generated by next-generation sequencing. This package implements several metrics for proportionality, including phi [Lovell et al (2015) <DOI:10.1371/journal.pcbi.1004075>] and rho [Erb and Notredame (2016) <DOI:10.1007/s12064-015-0220-8>]. This package also implements several metrics for differential proportionality. Unlike correlation, these measures give the same result for both relative and absolute data.
Author: Thomas Quinn [aut, cre], David Lovell [aut], Ionas Erb [aut], Anders Bilgrau [ctb], Greg Gloor [ctb]
Maintainer: Thomas Quinn <contacttomquinn@gmail.com>

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Package PAutilities updated to version 0.2.0 with previous version 0.1.2 dated 2019-03-06

Title: Streamline Physical Activity Research
Description: A collection of utilities that are useful for a broad range of tasks that are common in physical activity research, including the following: creation of Bland-Altman plots, formatted descriptive statistics, metabolic calculations (e.g. basal metabolic rate predictions) and conversions, demographic calculations (age and age-for-body-mass-index percentile), bout analysis of moderate-to-vigorous intensity physical activity, and analysis of bout detection algorithm performance.
Author: Paul R. Hibbing [aut, cre], Centers for Disease Control and Prevention [ctb]
Maintainer: Paul R. Hibbing <paulhibbing@gmail.com>

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Package balance updated to version 0.2.4 with previous version 0.1.9 dated 2018-12-10

Title: Visualize Balances of Compositional Data
Description: Balances have become a cornerstone of compositional data analysis. However, conceptualizing balances is difficult, especially for high-dimensional data. Most often, investigators visualize balances with "balance dendrograms". However, this visualization tool does not scale well for large data. This package provides an alternative scheme for visualizing balances, described in [Quinn (2018) <DOI:10.12688/f1000research.15858.1>]. This package also provides a method for principal balance analysis.
Author: Thomas Quinn [aut, cre]
Maintainer: Thomas Quinn <contacttomquinn@gmail.com>

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