Mon, 20 Jan 2020

Package miceRanger updated to version 1.2.0 with previous version 1.1.0 dated 2020-01-19

Title: Multiple Imputation by Chained Equations with Random Forests
Description: Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <arXiv:1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.
Author: Sam Wilson [aut, cre]
Maintainer: Sam Wilson <samwilson303@gmail.com>

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Package imputeR updated to version 2.2 with previous version 2.1 dated 2018-10-14

Title: A General Multivariate Imputation Framework
Description: Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.
Author: Steffen Moritz [aut, cre] (<https://orcid.org/0000-0002-0085-1804>), Lingbing Feng [aut], Gen Nowak [ctb], Alan. H. Welsh [ctb], Terry. J. O'Neill [ctb]
Maintainer: Steffen Moritz <steffen.moritz10@gmail.com>

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Package blob updated to version 1.2.1 with previous version 1.2.0 dated 2019-07-09

Title: A Simple S3 Class for Representing Vectors of Binary Data ('BLOBS')
Description: R's raw vector is useful for storing a single binary object. What if you want to put a vector of them in a data frame? The 'blob' package provides the blob object, a list of raw vectors, suitable for use as a column in data frame.
Author: Hadley Wickham [aut], Kirill Müller [cre], RStudio [cph]
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

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Package threeBrain updated to version 0.1.5 with previous version 0.1.4 dated 2019-10-18

Title: 3D Brain Visualization
Description: In neuroscience, 'AFNI/SUMA' is a great tool to visualize 3D brain. However, it takes efforts to interact and share the viewer to others. In addition, 'AFNI/SUMA' doesn't support Windows platform. In the 'EEG/iEEG' field, it's hard to have multiple cortical electrodes mapped to a template brain for group analysis. Therefore this package is written aimed at providing a fast, stable, interactive and easy to share tool based on 'Three.js', a 'WebGL' engine to render 3D objects in the web browser such that we can display brain surfaces on webpage interactively. This package translates R objects to JavaScript objects via 'JSON' format, and provides 'R-Shiny' interface to manipulate geometries interactively. The visualizations can also serve as standalone widgets that can be easily shared across different platforms. Along with 'rave', another package developed by Beauchamp's lab at Baylor College Medicine, this package provides solutions to easily map surface electrodes from multiple subjects to one template 141 brain.
Author: Zhengjia Wang [aut, cre, cph], John Magnotti [aut], Brian Metzger [aut], Elizabeth Nesbitt [res], Michael Beauchamp [aut, dtc, fnd]
Maintainer: Zhengjia Wang <zhengjia.wang@rice.edu>

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Package emg updated to version 1.0.8 with previous version 1.0.7 dated 2018-05-17

Title: Exponentially Modified Gaussian (EMG) Distribution
Description: Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
Author: Shawn Garbett, Mark Kozdoba
Maintainer: Shawn Garbett <Shawn@Garbett.org>

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Package cpsurvsim updated to version 1.2.0 with previous version 1.1.0 dated 2019-06-03

Title: Simulating Survival Data from Change-Point Hazard Distributions
Description: Simulates time-to-event data with type I right censoring using two methods: the inverse CDF method and our proposed memoryless method. The latter method takes advantage of the memoryless property of survival and simulates a separate distribution between change-points. We include two parametric distributions: exponential and Weibull. Inverse CDF method draws on the work of Rainer Walke (2010), <https://www.demogr.mpg.de/papers/technicalreports/tr-2010-003.pdf>.
Author: Camille Hochheimer [aut, cre]
Maintainer: Camille Hochheimer <hochheimercj@vcu.edu>

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Package vip updated to version 0.2.1 with previous version 0.2.0 dated 2020-01-19

Title: Variable Importance Plots
Description: A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).
Author: Brandon Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>), Brad Boehmke [aut] (<https://orcid.org/0000-0002-3611-8516>), Bernie Gray [aut] (<https://orcid.org/0000-0001-9190-6032>)
Maintainer: Brandon Greenwell <greenwell.brandon@gmail.com>

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Package tsna updated to version 0.3.1 with previous version 0.3.0 dated 2019-05-29

Title: Tools for Temporal Social Network Analysis
Description: Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
Author: Skye Bender-deMoll [aut, cre], Martina Morris [aut], James Moody [ctb]
Maintainer: Skye Bender-deMoll <skyebend@uw.edu>

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Package rmarkdown updated to version 2.1 with previous version 2.0 dated 2019-12-12

Title: Dynamic Documents for R
Description: Convert R Markdown documents into a variety of formats.
Author: JJ Allaire [aut], Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>), Jonathan McPherson [aut], Javier Luraschi [aut], Kevin Ushey [aut], Aron Atkins [aut], Hadley Wickham [aut], Joe Cheng [aut], Winston Chang [aut], Richard Iannone [aut] (<https://orcid.org/0000-0003-3925-190X>), Andrew Dunning [ctb] (<https://orcid.org/0000-0003-0464-5036>), Atsushi Yasumoto [ctb] (<https://orcid.org/0000-0002-8335-495X>), Barret Schloerke [ctb], Christophe Dervieux [ctb], Frederik Aust [ctb] (<https://orcid.org/0000-0003-4900-788X>), Jeff Allen [ctb], JooYoung Seo [ctb] (<https://orcid.org/0000-0002-4064-6012>), Malcolm Barrett [ctb], Rob Hyndman [ctb], Romain Lesur [ctb], Roy Storey [ctb], Ruben Arslan [ctb], Sergio Oller [ctb], RStudio, Inc. [cph], jQuery Foundation [cph] (jQuery library), jQuery contributors [ctb, cph] (jQuery library; authors listed in inst/rmd/h/jquery-AUTHORS.txt), jQuery UI contributors [ctb, cph] (jQuery UI library; authors listed in inst/rmd/h/jqueryui-AUTHORS.txt), Mark Otto [ctb] (Bootstrap library), Jacob Thornton [ctb] (Bootstrap library), Bootstrap contributors [ctb] (Bootstrap library), Twitter, Inc [cph] (Bootstrap library), Alexander Farkas [ctb, cph] (html5shiv library), Scott Jehl [ctb, cph] (Respond.js library), Ivan Sagalaev [ctb, cph] (highlight.js library), Greg Franko [ctb, cph] (tocify library), John MacFarlane [ctb, cph] (Pandoc templates), Google, Inc. [ctb, cph] (ioslides library), Dave Raggett [ctb] (slidy library), W3C [cph] (slidy library), Dave Gandy [ctb, cph] (Font-Awesome), Ben Sperry [ctb] (Ionicons), Drifty [cph] (Ionicons), Aidan Lister [ctb, cph] (jQuery StickyTabs), Benct Philip Jonsson [ctb, cph] (pagebreak lua filter), Albert Krewinkel [ctb, cph] (pagebreak lua filter)
Maintainer: Yihui Xie <xie@yihui.name>

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New package iMRMC with initial version 1.2.1
Package: iMRMC
Type: Package
Title: Multi-Reader, Multi-Case Analysis Methods (ROC, Agreement, and Other Metrics)
Version: 1.2.1
Author: Brandon Gallas
Maintainer: Brandon Gallas <Brandon.Gallas@fda.hhs.gov>
Description: Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. The initial package analyzes the reader-average area under the receiver operating characteristic (ROC) curve with U-statistics according to Gallas, Bandos, Samuelson, and Wagner 2009 <doi:10.1080/03610920802610084>. Additional functions analyze other endpoints with U-statistics (binary performance and score differences) following the work by Gallas, Pennello, and Myers 2007 <doi:10.1364/JOSAA.24.000B70>. Package development and documentation is at <https://github.com/DIDSR/iMRMC/tree/master>.
License: CC0
Encoding: UTF-8
SystemRequirements: Java JDK 1.7 or higher
LazyData: true
Depends: R (>= 2.10)
RoxygenNote: 7.0.2
Imports: stats
Suggests: testthat
NeedsCompilation: no
Packaged: 2020-01-20 15:20:20 UTC; BDG
Repository: CRAN
Date/Publication: 2020-01-20 19:30:07 UTC

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Package fungible updated to version 1.95.2 with previous version 1.95 dated 2019-12-17

Title: Psychometric Functions from the Waller Lab
Description: Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications: Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>. Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>. Waller, N. G. (2016). Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568, <DOI:10.1080/00273171.2016.1178566>. Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>. Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.
Author: Niels Waller [aut, cre], Jeff Jones [ctb], Casey Giordano [ctb]
Maintainer: Niels Waller <nwaller@umn.edu>

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Package eplusr updated to version 0.11.0 with previous version 0.10.4 dated 2019-11-05

Title: A Toolkit for Using Whole Building Simulation Program 'EnergyPlus'
Description: A rich toolkit of using the whole building simulation program 'EnergyPlus'(<https://energyplus.net>), which enables programmatic navigation, modification of 'EnergyPlus' models and makes it less painful to do parametric simulations and analysis.
Author: Hongyuan Jia [aut, cre] (<https://orcid.org/0000-0002-0075-8183>)
Maintainer: Hongyuan Jia <hongyuan.jia@bears-berkeley.sg>

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More information about eplusr at CRAN
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New package deGradInfer with initial version 1.0.1
Package: deGradInfer
Title: Parameter Inference for Systems of Differential Equation
Version: 1.0.1
Authors@R: c(person("Benn", "Macdonald", email = "Benn.Macdonald@glasgow.ac.uk", role = c("aut")), person("Frank", "Dondelinger", email = "fdondelinger.work@gmail.com", role = c("aut", "cre")))
Description: Efficient Bayesian parameter inference for systems of ordinary differential equations. The inference is based on adaptive gradient matching (AGM, Dondelinger et al. 2013 <http://proceedings.mlr.press/v31/dondelinger13a.pdf>, Macdonald 2017 <http://theses.gla.ac.uk/7987/1/2017macdonaldphd.pdf>), which offers orders-of-magnitude improvements in computational efficiency over standard methods that require solving the differential equation system. Features of the package include flexible specification of custom ODE systems as R functions, support for missing variables, Bayesian inference via population MCMC.
Depends: R (>= 3.3.1)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: deSolve, gdata, gptk, graphics, stats
RoxygenNote: 7.0.2
Suggests: testthat, knitr, rmarkdown, ggplot2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-20 14:00:44 UTC; dondelin
Author: Benn Macdonald [aut], Frank Dondelinger [aut, cre]
Maintainer: Frank Dondelinger <fdondelinger.work@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-20 19:30:25 UTC

More information about deGradInfer at CRAN
Permanent link

Package bnclassify updated to version 0.4.4 with previous version 0.4.3 dated 2019-11-29

Title: Learning Discrete Bayesian Network Classifiers from Data
Description: State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.
Author: Mihaljevic Bojan [aut, cre], Bielza Concha [aut], Larranaga Pedro [aut], Wickham Hadley [ctb] (some code extracted from memoise package)
Maintainer: Mihaljevic Bojan <bmihaljevic@fi.upm.es>

Diff between bnclassify versions 0.4.3 dated 2019-11-29 and 0.4.4 dated 2020-01-20

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New package bomrang with initial version 0.7.0
Package: bomrang
Type: Package
Title: Australian Government Bureau of Meteorology ('BOM') Data Client
Version: 0.7.0
Authors@R: c(person("Adam H.", "Sparks", role = c("aut", "cre"), email = "adamhsparks@gmail.com", comment = c(ORCID = "0000-0002-0061-8359")), person("Jonathan", "Carroll", , role = "aut", email = "rpkg@jcarroll.com.au", comment = c(ORCID = "0000-0002-1404-5264")), person("James", "Goldie", role = "aut", email = "me@rensa.co", comment = c(ORCID = "0000-0002-5024-6207")), person("Dean", "Marchiori", , role = "aut", email = "deanmarchiori@gmail.com"), person("Paul", "Melloy", role = "aut", email = "paul@melloy.com.au", comment = c(ORCID = "0000-0003-4253-7167")), person("Mark", "Padgham", role = c("aut", "rev"), email = "mark.padgham@email.com", comment = c(ORCID = "0000-0003-2172-5265")), person("Hugh", "Parsonage", role = "aut", email = "hugh.parsonage@gmail.com", comment = c(ORCID = "0000-0003-4055-0835")), person("Keith", "Pembleton", role = "aut", email = "keith.pembleton@usq.edu.au", comment = c(ORCID = "0000-0002-1896-4516")), person("James", "Balamuta", role = "ctb", email = "balamut2@illinois.edu", comment = c(ORCID = "0000-0003-2826-8458")), person("Brooke", "Anderson", role = "rev", email = "brooke.anderson@colostate.edu", comment = c(ORCID = "0000-0002-5012-9035")))
Description: Provides functions to interface with Australian Government Bureau of Meteorology ('BOM') data, fetching data and returning a tidy data frame of precis forecasts, historical and current weather data from stations, agriculture bulletin data, 'BOM' 0900 or 1500 weather bulletins and downloading and importing radar and satellite imagery files. Data (c) Australian Government Bureau of Meteorology Creative Commons (CC) Attribution 3.0 licence or Public Access Licence (PAL) as appropriate. See <http://www.bom.gov.au/other/copyright.shtml> for further details.
URL: https://github.com/ropensci/bomrang, https://docs.ropensci.org/bomrang/
BugReports: https://github.com/ropensci/bomrang/issues
License: MIT + file LICENSE
Depends: R (>= 3.5.0)
Imports: curl (>= 2.8.1), crayon, data.table (>= 1.10.4), dplyr (>= 0.7.0), foreign, httr (>= 1.2.1), hoardr, janitor (>= 1.0.0), jsonlite (>= 1.5), lubridate, magrittr (>= 1.5), raster, rgdal, readr (>= 1.1.1), rvest, tidyr (>= 0.6.3), tools, utils, xml2 (>= 1.1.1)
Encoding: UTF-8
LazyData: true
Suggests: ASGS.foyer, covr, testthat, knitr, rmarkdown, ggplot2, ggthemes, grid, gridExtra, maps, mapproj, rappdirs, roxygen2 (>= 6.1.0)
Language: en-AU
RoxygenNote: 7.0.2
NeedsCompilation: no
ByteCompile: TRUE
VignetteBuilder: knitr
X-schema.org-applicationCategory: Tools
X-schema.org-keywords: bom, meteorological-data, weather-forecast, australia, weather, weather-data, meteorology, australia-bureau-of-meteorology
X-schema.org-isPartOf: https://ropensci.org
Packaged: 2020-01-20 04:03:47 UTC; adamsparks
Author: Adam H. Sparks [aut, cre] (<https://orcid.org/0000-0002-0061-8359>), Jonathan Carroll [aut] (<https://orcid.org/0000-0002-1404-5264>), James Goldie [aut] (<https://orcid.org/0000-0002-5024-6207>), Dean Marchiori [aut], Paul Melloy [aut] (<https://orcid.org/0000-0003-4253-7167>), Mark Padgham [aut, rev] (<https://orcid.org/0000-0003-2172-5265>), Hugh Parsonage [aut] (<https://orcid.org/0000-0003-4055-0835>), Keith Pembleton [aut] (<https://orcid.org/0000-0002-1896-4516>), James Balamuta [ctb] (<https://orcid.org/0000-0003-2826-8458>), Brooke Anderson [rev] (<https://orcid.org/0000-0002-5012-9035>)
Maintainer: Adam H. Sparks <adamhsparks@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-20 19:30:29 UTC

More information about bomrang at CRAN
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Package adegenet updated to version 2.1.2 with previous version 2.1.1 dated 2018-02-02

Title: Exploratory Analysis of Genetic and Genomic Data
Description: Toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure ('genind' class), alleles counts by populations ('genpop'), and genome-wide SNP data ('genlight'). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.
Author: Thibaut Jombart, Zhian N. Kamvar, Caitlin Collins, Roman Lustrik, Marie- Pauline Beugin, Brian J. Knaus, Peter Solymos, Vladimir Mikryukov, Klaus Schliep, Tiago Maié, Libor Morkovsky, Ismail Ahmed, Anne Cori, Federico Calboli, RJ Ewing, Frédéric Michaud, Rebecca DeCamp
Maintainer: Zhian N. Kamvar <zkamvar@gmail.com>

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Package uclust updated to version 0.2.0 with previous version 0.1.0 dated 2019-02-11

Title: Clustering and Classification Inference with U-Statistics
Description: Clustering and classification inference for high dimension low sample size (HDLSS) data with U-statistics. The package contains implementations of nonparametric statistical tests for sample homogeneity, group separation, clustering, and classification of multivariate data. The methods have high statistical power and are tailored for data in which the dimension L is much larger than sample size n. See Gabriela B. Cybis, Marcio Valk and Sílvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387> and Marcio Valk and Gabriela B. Cybis (2018) <arXiv:1805.12179>.
Author: Gabriela Cybis [aut, cre], Marcio Valk [aut], Kazuki Yokoyama [ctb]
Maintainer: Gabriela Cybis <gcybis@gmail.com>

Diff between uclust versions 0.1.0 dated 2019-02-11 and 0.2.0 dated 2020-01-20

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Package tidyquery updated to version 0.2.0 with previous version 0.1.0 dated 2019-09-19

Title: Query 'R' Data Frames with 'SQL'
Description: Use 'SQL' 'SELECT' statements to query 'R' data frames.
Author: Ian Cook [aut, cre], Cloudera [cph]
Maintainer: Ian Cook <ian@cloudera.com>

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Package SimDesign updated to version 2.0.1 with previous version 1.14 dated 2019-06-28

Title: Structure for Organizing Monte Carlo Simulation Designs
Description: Provides tools to help safely and efficiently organize Monte Carlo simulations in R. The package controls the structure and back-end of Monte Carlo simulations by utilizing a general generate-analyse-summarise strategy. The functions provided control common simulation issues such as re-simulating non-convergent results, support parallel back-end and MPI distributed computations, save and restore temporary files, aggregate results across independent nodes, and provide native support for debugging. For a pedagogical introduction to the package refer to Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>.
Author: Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>), Matthew Sigal [ctb], Ogreden Oguzhan [ctb]
Maintainer: Phil Chalmers <rphilip.chalmers@gmail.com>

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Package foreign updated to version 0.8-75 with previous version 0.8-74 dated 2019-12-26

Title: Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ...
Description: Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.
Author: R Core Team [aut, cph, cre], Roger Bivand [ctb, cph], Vincent J. Carey [ctb, cph], Saikat DebRoy [ctb, cph], Stephen Eglen [ctb, cph], Rajarshi Guha [ctb, cph], Swetlana Herbrandt [ctb], Nicholas Lewin-Koh [ctb, cph], Mark Myatt [ctb, cph], Michael Nelson [ctb], Ben Pfaff [ctb], Brian Quistorff [ctb], Frank Warmerdam [ctb, cph], Stephen Weigand [ctb, cph], Free Software Foundation, Inc. [cph]
Maintainer: R Core Team <R-core@R-project.org>

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Package sirus updated to version 0.2.1 with previous version 0.1.2 dated 2019-09-02

Title: Stable and Interpretable RUle Set
Description: A regression and classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with the predictivity of random forests for problems with low order interactions. The core aggregation principle of random forests is kept, but instead of aggregating predictions, SIRUS selects the most frequent nodes of the forest to form a stable rule ensemble model. The algorithm is fully described in the following article: Benard C., Biau G., da Veiga S., Scornet E. (2019) <arXiv:1908.06852>. This R package is a fork from the project ranger (<https://github.com/imbs-hl/ranger>).
Author: Clement Benard [aut, cre], Marvin N. Wright [ctb, cph]
Maintainer: Clement Benard <clement.benard@safrangroup.com>

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Package ToxicoGx updated to version 0.1.1 with previous version 0.1.0 dated 2020-01-14

Title: Analysis of Large-Scale Toxico-Genomic Data
Description: Contains a set of functions to perform large-scale analysis of toxico-genomic data. A member of the BHK lab's Gx suite of software, this package provides a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxico-genomic data. This package is in early release, and more features will be added over the next few months.
Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut], Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>

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Package statgenGWAS updated to version 1.0.2 with previous version 1.0.1 dated 2020-01-19

Title: Genome Wide Association Studies
Description: Fast single trait Genome Wide Association Studies (GWAS). One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.
Author: Bart-Jan van Rossum [aut, cre], Willem Kruijer [aut] (<https://orcid.org/0000-0001-7179-1733>), Fred van Eeuwijk [ctb] (<https://orcid.org/0000-0003-3672-2921>), Martin Boer [ctb], Marcos Malosetti [ctb] (<https://orcid.org/0000-0002-8150-1397>), Daniela Bustos-Korts [ctb] (<https://orcid.org/0000-0003-3827-6726>), Emilie Millet [ctb] (<https://orcid.org/0000-0002-2913-4892>), Joao Paulo [ctb] (<https://orcid.org/0000-0002-4180-0763>), Maikel Verouden [ctb] (<https://orcid.org/0000-0002-4893-3323>), Ron Wehrens [ctb] (<https://orcid.org/0000-0002-8798-5599>), Choazhi Zheng [ctb] (<https://orcid.org/0000-0001-6030-3933>)
Maintainer: Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>

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Package opentripplanner updated to version 0.2.0.4 with previous version 0.2.0.3 dated 2020-01-19

Title: Setup and connect to 'OpenTripPlanner'
Description: Setup and connect to 'OpenTripPlanner' (OTP) <http://www.opentripplanner.org/>. OTP is an open source platform for multi-modal and multi-agency journey planning written in 'Java'. The package allows you to manage a local version or connect to remote OTP server. This package has been peer-reviewed by rOpenSci (v. 0.2.0.0).
Author: Malcolm Morgan [aut, cre] (<https://orcid.org/0000-0002-9488-9183>), Marcus Young [aut] (<https://orcid.org/0000-0003-4627-1116>), Robin Lovelace [aut] (<https://orcid.org/0000-0001-5679-6536>), Layik Hama [ctb] (<https://orcid.org/0000-0003-1912-4890>)
Maintainer: Malcolm Morgan <m.morgan1@leeds.ac.uk>

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Package lumberjack updated to version 1.1.3 with previous version 1.1.2 dated 2020-01-09

Title: Track Changes in Data
Description: A framework that allows for easy logging of changes in data. Main features: start tracking changes by adding a single line of code to an existing script. Track changes in multiple datasets, using multiple loggers. Add custom-built loggers or use loggers offered by other packages.
Author: Mark van der Loo [aut, cre] (<https://orcid.org/0000-0002-9807-4686>)
Maintainer: Mark van der Loo <mark.vanderloo@gmail.com>

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Package landscapemetrics updated to version 1.4.2 with previous version 1.4.1 dated 2020-01-12

Title: Landscape Metrics for Categorical Map Patterns
Description: Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.umass.edu/landeco/research/fragstats/fragstats.html>) and new ones from the current literature on landscape metrics. This package supports 'raster' spatial objects and takes RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer from the 'raster' package as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.
Author: Maximillian H.K. Hesselbarth [aut, cre] (<https://orcid.org/0000-0003-1125-9918>), Marco Sciaini [aut] (<https://orcid.org/0000-0002-3042-5435>), Jakub Nowosad [aut] (<https://orcid.org/0000-0002-1057-3721>), Sebastian Hanss [aut] (<https://orcid.org/0000-0002-3990-4897>), Laura J. Graham [ctb] (Input on package structure), Jeffrey Hollister [ctb] (Input on package structure), Kimberly A. With [ctb] (Input on package structure), Florian Privé [ctb] (Original author of underlying C++ code for get_nearestneighbour() function), Matt Strimas-Mackey [ctb] (Bugfix in sample_metrics())
Maintainer: Maximillian H.K. Hesselbarth <maximilian.hesselbarth@uni-goettingen.de>

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Package geonapi updated to version 0.3-1 with previous version 0.3 dated 2019-11-08

Title: 'GeoNetwork' API R Interface
Description: Provides an R interface to the 'GeoNetwork' API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a 'GeoNetwork' web-application and exposte it to OGC CSW.
Author: Emmanuel Blondel [aut, cre] (<https://orcid.org/0000-0002-5870-5762>)
Maintainer: Emmanuel Blondel <emmanuel.blondel1@gmail.com>

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Package depmixS4 updated to version 1.4-2 with previous version 1.4-1 dated 2020-01-09

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>

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Package theiaR updated to version 0.3.0 with previous version 0.2.0 dated 2019-03-26

Title: Download and Manage Data from Theia
Description: Provides a simple interface to search available data provided by Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded based on a search result or from a cart file downloaded from Theia website.
Author: Xavier Laviron [aut, cre] (<https://orcid.org/0000-0002-9882-3253>)
Maintainer: Xavier Laviron <xavier.laviron@gmx.fr>

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Package LMest updated to version 2.6.0 with previous version 2.5.1 dated 2019-07-30

Title: Generalized Latent Markov Models
Description: Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.
Author: Francesco Bartolucci, Silvia Pandolfi, Fulvia Pennoni, Alessio Farcomeni, Alessio Serafini
Maintainer: Francesco Bartolucci <bart@stat.unipg.it>

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Package BIGL updated to version 1.4.2 with previous version 1.4.1 dated 2019-06-21

Title: Biochemically Intuitive Generalized Loewe Model
Description: Response surface methods for drug synergy analysis. Available methods include generalized and classical Loewe formulations as well as Highest Single Agent methodology. Response surfaces can be plotted in an interactive 3-D plot and formal statistical tests for presence of synergistic effects are available. Implemented methods and tests are described in the article "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism" by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) <doi:10.1038/s41598-017-18068-5>.
Author: Heather Turner, Annelies Tourny, Olivier Thas, Maxim Nazarov, Rytis Bagdziunas
Maintainer: Maxim Nazarov <maxim.nazarov@openanalytics.eu>

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Package anytime updated to version 0.3.7 with previous version 0.3.6 dated 2019-08-29

Title: Anything to 'POSIXct' or 'Date' Converter
Description: Convert input in any one of character, integer, numeric, factor, or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package tidycensus updated to version 0.9.5 with previous version 0.9.2 dated 2019-06-12

Title: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames
Description: An integrated R interface to the decennial US Census and American Community Survey APIs and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for many geographies.
Author: Kyle Walker [aut, cre], Kris Eberwein [ctb], Matt Herman [ctb]
Maintainer: Kyle Walker <kyle.walker@tcu.edu>

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Package rmcfs updated to version 1.3.1 with previous version 1.3.0 dated 2019-05-12

Title: The MCFS-ID Algorithm for Feature Selection and Interdependency Discovery
Description: MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.
Author: Michal Draminski [aut, cre], Jacek Koronacki [aut], Julian Zubek [ctb]
Maintainer: Michal Draminski <michal.draminski@ipipan.waw.pl>

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 R/rmcfs.io.R        |    4 +-
 R/utils.R           |   21 ++++++++++++++
 build/vignette.rds  |binary
 inst/doc/v85i12.pdf |binary
 9 files changed, 70 insertions(+), 64 deletions(-)

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Package dani updated to version 0.1-1 with previous version 0.1-0 dated 2019-05-03

Title: Design and Analysis of Non-Inferiority Trials
Description: Provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to design a trial with the methods described in Quartagno et al. (2019) <arXiv:1905.00241>.
Author: Matteo Quartagno
Maintainer: Matteo Quartagno <m.quartagno@ucl.ac.uk>

Diff between dani versions 0.1-0 dated 2019-05-03 and 0.1-1 dated 2020-01-20

 DESCRIPTION                   |    8 ++++----
 MD5                           |    4 ++--
 R/simulations.modify.margin.R |    4 ++--
 3 files changed, 8 insertions(+), 8 deletions(-)

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Package RIdeogram updated to version 0.2.2 with previous version 0.2.1 dated 2019-12-02

Title: Drawing SVG Graphics to Visualize and Map Genome-Wide Data on Idiograms
Description: For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species.
Author: Zhaodong Hao [aut, cre], Dekang Lv [aut], Ying Ge [aut], Jisen Shi [aut], Weijers Dolf [aut], Guangchuang Yu [aut], Jinhui Chen [aut]
Maintainer: Zhaodong Hao <haozd1992@163.com>

Diff between RIdeogram versions 0.2.1 dated 2019-12-02 and 0.2.2 dated 2020-01-20

 DESCRIPTION             |    6 -
 MD5                     |   12 +-
 R/ideogram.R            |  244 +++++++++++++++++++++++++++++++++---------------
 README.md               |    4 
 inst/doc/RIdeogram.Rmd  |    2 
 inst/doc/RIdeogram.html |    6 -
 vignettes/RIdeogram.Rmd |    2 
 7 files changed, 183 insertions(+), 93 deletions(-)

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New package vampyr with initial version 1.0.5
Package: vampyr
Type: Package
Title: Factor Analysis Controlling the Effects of Response Bias
Version: 1.0.5
Date: 2020-01-15
Author: David Navarro-Gonzalez, Andreu Vigil-Colet, Pere J. Ferrando, Urbano Lorenzo-Seva, Jorge N. Tendeiro
Maintainer: David Navarro-Gonzalez <david.navarro@urv.cat>
Description: Vampirize the response biases from a dataset! Performs factor analysis controlling the effects of social desirability and acquiescence using the method described in Ferrando, Lorenzo-Seva & Chico (2009) <doi:10.1080/10705510902751374>.
Depends: R (>= 2.10)
Imports: stats, optimbase, psych, EFA.MRFA, GPArotation, PCovR, moments, corpcor, lavaan, fungible
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2020-01-15 08:10:02 UTC; 39928973-F
Repository: CRAN
Date/Publication: 2020-01-20 10:30:02 UTC

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New package abdiv with initial version 0.2.0
Package: abdiv
Title: Alpha and Beta Diversity Measures
Version: 0.2.0
Authors@R: person("Kyle", "Bittinger", email = "kylebittinger@gmail.com", role = c("aut", "cre"))
Description: A collection of measures for measuring ecological diversity. Ecological diversity comes in two flavors: alpha diversity measures the diversity within a single site or sample, and beta diversity measures the diversity across two sites or samples. This package overlaps considerably with other R packages such as 'vegan', 'gUniFrac', 'betapart', and 'fossil'. We also include a wide range of functions that are implemented in software outside the R ecosystem, such as 'scipy', 'Mothur', and 'scikit-bio'. The implementations here are designed to be basic and clear to the reader.
URL: https://github.com/kylebittinger/abdiv
BugReports: https://github.com/kylebittinger/abdiv/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: testthat (>= 2.1.0), vegan
Imports: ape
NeedsCompilation: no
Packaged: 2020-01-15 02:25:11 UTC; bittingerk
Author: Kyle Bittinger [aut, cre]
Maintainer: Kyle Bittinger <kylebittinger@gmail.com>
Repository: CRAN
Date/Publication: 2020-01-20 10:50:02 UTC

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New package vamc with initial version 0.1.1
Package: vamc
Type: Package
Title: A Monte Carlo Valuation Framework for Variable Annuities
Version: 0.1.1
Authors@R: c( person("Hengxin", "Li", email = "h343li@uwaterloo.ca", role = c("aut", "cph")), person("Ben", "Feng", email = "ben.feng@uwaterloo.ca", role = c("aut", "cph")), person("Mingyi", "Jiang", email = "m64jiang@uwaterloo.ca", role = c("aut" , "cph", "cre")), person("GuoJun", "Gan", email = "Guojun.Gan@uconn.edu", role = c("ctb")))
Description: Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of variable annuities, which reflect realistic features of common annuity contracts in practice. It aims to facilitate the development and dissemination of research related to the efficient valuation of a portfolio of large variable annuities. The main valuation methodology was proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.
Depends: R (>= 3.3.0)
License: GPL-2
LazyData: true
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
RoxygenNote: 7.0.2
Imports: stats (>= 3.3.0), utils (>= 3.3.0), Rdpack (>= 0.4)
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2020-01-19 18:19:53 UTC; Iris
Encoding: UTF-8
Repository: CRAN
Author: Hengxin Li [aut, cph], Ben Feng [aut, cph], Mingyi Jiang [aut, cph, cre], GuoJun Gan [ctb]
Maintainer: Mingyi Jiang <m64jiang@uwaterloo.ca>
Date/Publication: 2020-01-20 09:30:02 UTC

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New package shinySIR with initial version 0.1.0
Package: shinySIR
Type: Package
Title: Interactive Plotting for Mathematical Models of Infectious Disease Spread
Version: 0.1.0
Date: 2020-01-04
Authors@R: c(person("Sinead E.", "Morris", role = c("aut", "cre"), email = "sinead.morris@columbia.edu", comment = c(ORCID = "0000-0001-8626-1698")), person("Ottar N.", "Bjornstad", role = "ctb"))
Maintainer: Sinead E. Morris <sinead.morris@columbia.edu>
Description: Provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows 'shinySIR' to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) <doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: dplyr (>= 0.8.0.1), tidyr (>= 0.8.3), ggplot2 (>= 3.1.1), shiny (>= 1.3.2), deSolve (>= 1.2.1)
Suggests: knitr (>= 1.22), rmarkdown (>= 1.12), testthat (>= 2.2.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-14 20:45:02 UTC; sineadmorris
Author: Sinead E. Morris [aut, cre] (<https://orcid.org/0000-0001-8626-1698>), Ottar N. Bjornstad [ctb]
Repository: CRAN
Date/Publication: 2020-01-20 09:30:05 UTC

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New package provGraphR with initial version 1.0
Package: provGraphR
Title: Creates Adjacency Matrices for Lineage Searches
Version: 1.0
Authors@R: c( person("Orenna", "Brand", email = "o.brand@columbia.edu", role = "aut"), person("Joseph", "Wonsil", email = "jwonsil@carthage.edu", role = "aut"), person("Emery", "Boose", email = "boose@fas.harvard.edu", role = "aut"), person("Barbara", "Lerner", email = "blerner@mtholyoke.edu", role = "cre") )
Copyright: President and Fellows of Harvard College, Trustees of Mount Holyoke College
Description: Creates and manages a provenance graph corresponding to the provenance created by the 'rdtLite' package, which collects provenance from R scripts. 'rdtLite' is available on CRAN. The provenance format is an extension of the W3C PROV JSON format (<https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>). The extended JSON provenance format is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
Depends: R (>= 3.5.0)
Imports: igraph, Matrix, methods, provParseR (>= 0.2)
Suggests: testthat
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-01-14 19:05:16 UTC; blerner
Author: Orenna Brand [aut], Joseph Wonsil [aut], Emery Boose [aut], Barbara Lerner [cre]
Maintainer: Barbara Lerner <blerner@mtholyoke.edu>
Repository: CRAN
Date/Publication: 2020-01-20 09:30:09 UTC

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Package ISwR updated to version 2.0-8 with previous version 2.0-7 dated 2015-06-07

Title: Introductory Statistics with R
Description: Data sets and scripts for text examples and exercises in P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
Author: Peter Dalgaard
Maintainer: Peter Dalgaard <pd.mes@cbs.dk>

Diff between ISwR versions 2.0-7 dated 2015-06-07 and 2.0-8 dated 2020-01-20

 DESCRIPTION                  |    8 ++---
 MD5                          |   10 +++----
 tests/allexercises.R         |    2 +
 tests/allexercises.Rout.save |   58 ++++++++++++++++++++-----------------------
 tests/allscripts.R           |    5 +++
 tests/allscripts.Rout.save   |   49 +++++++++++++++++++-----------------
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New package Disequilibrium with initial version 1.0
Package: Disequilibrium
Type: Package
Title: Disequilibrium Models
Version: 1.0
Author: Nate Latshaw [aut, cre], Michael Guggisberg [aut]
Maintainer: Nate Latshaw <dcms2015@gmail.com>
Description: Estimate, summarize, and perform predictions with the market in disequilibrium model, as found in Gourieroux, C. (2000) <doi:10.1017/CBO9780511805608> and Maddala, G. (1983) <doi:10.1017/CBO9780511810176>. The parameters are estimated with maximum likelihood.
License: GPL-3
Encoding: UTF-8
Imports: optimr, Formula, numDeriv
Suggests: MASS
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-01-14 18:01:40 UTC; nlatshaw
Repository: CRAN
Date/Publication: 2020-01-20 09:10:02 UTC

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New package clustermole with initial version 1.0.0
Type: Package
Package: clustermole
Title: Unbiased Cell Type Identification of Single-Cell Transcriptomic Data
Version: 1.0.0
Authors@R: person(given = "Igor", family = "Dolgalev", role = c("aut", "cre"), email = "igor.dolgalev@nyumc.org")
Description: A typical computational pipeline to process single-cell RNA sequencing (scRNA-seq) data involves clustering of cells. Assignment of cell type labels to those clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging if you are not familiar with all the captured subpopulations or have unexpected contaminants. 'clustermole' provides a comprehensive meta collection of cell identity markers for thousands of human and mouse cell types sourced from a variety of databases as well as methods to query them.
License: MIT + file LICENSE
URL: https://github.com/igordot/clustermole
BugReports: https://github.com/igordot/clustermole/issues
Depends: R (>= 3.4)
Imports: dplyr, GSVA (>= 1.26.0), magrittr, methods, rlang (>= 0.1.2), tibble, tidyr, utils
Suggests: covr, roxygen2, testthat (>= 2.1.0), knitr, rmarkdown
biocViews:
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-01-15 00:50:36 UTC; id460
Author: Igor Dolgalev [aut, cre]
Maintainer: Igor Dolgalev <igor.dolgalev@nyumc.org>
Repository: CRAN
Date/Publication: 2020-01-20 10:00:02 UTC

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Package XML updated to version 3.99-0.3 with previous version 3.99-0.2 dated 2020-01-18

Title: Tools for Parsing and Generating XML Within R and S-Plus
Description: Many approaches for both reading and creating XML (and HTML) documents (including DTDs), both local and accessible via HTTP or FTP. Also offers access to an 'XPath' "interpreter".
Author: CRAN Team [ctb, cre] (de facto maintainer since 2013), Duncan Temple Lang [aut] (<https://orcid.org/0000-0003-0159-1546>), Tomas Kalibera [ctb]
Maintainer: CRAN Team <CRAN@r-project.org>

Diff between XML versions 3.99-0.2 dated 2020-01-18 and 3.99-0.3 dated 2020-01-20

 DESCRIPTION   |   11 ++++-------
 MD5           |    4 ++--
 configure.win |    2 --
 3 files changed, 6 insertions(+), 11 deletions(-)

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Package MVTests (with last version 1.1) was removed from CRAN

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

2019-07-30 1.1

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Package BAS (with last version 1.5.4) was removed from CRAN

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

2020-01-19 1.5.4

Permanent link
Package sfheaders updated to version 0.1.0 with previous version 0.0.2 dated 2019-11-07

Title: Converts Between R Objects and Simple Feature Objects
Description: Converts between R and Simple Feature 'sf' objects, without depending on the Simple Feature library. Conversion functions are available at both the R level, and through 'Rcpp'.
Author: David Cooley [aut, cre]
Maintainer: David Cooley <david.cooley.au@gmail.com>

Diff between sfheaders versions 0.0.2 dated 2019-11-07 and 0.1.0 dated 2020-01-20

 sfheaders-0.0.2/sfheaders/R/scratch.R                                                        |only
 sfheaders-0.1.0/sfheaders/DESCRIPTION                                                        |   12 
 sfheaders-0.1.0/sfheaders/MD5                                                                |  113 
 sfheaders-0.1.0/sfheaders/NAMESPACE                                                          |    3 
 sfheaders-0.1.0/sfheaders/NEWS.md                                                            |    7 
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 sfheaders-0.1.0/sfheaders/README.md                                                          |   28 
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 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sf/multipoint/sf_multipoint.hpp             |  251 +
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 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sf/point/sf_point.hpp                       |  196 +
 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sf/polygon/sf_polygon.hpp                   |  313 ++
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 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sfc/m_range.hpp                             |   38 
 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sfc/multilinestring/sfc_multilinestring.hpp | 1315 +++++-----
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 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sfc/point/sfc_point.hpp                     |    7 
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 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sfc/z_range.hpp                             |   13 
 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sfg/multipolygon/sfg_multipolygon.hpp       |   17 
 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sfg/point/sfg_point.hpp                     |   24 
 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/sfg/polygon/close_polygon.hpp               |   18 
 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/shapes/list_list_mat/list_list_mat.hpp      |   10 
 sfheaders-0.1.0/sfheaders/inst/include/sfheaders/shapes/list_mat/list_mat.hpp                |   15 
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 sfheaders-0.1.0/sfheaders/man/sf_linestring.Rd                                               |   18 
 sfheaders-0.1.0/sfheaders/man/sf_multilinestring.Rd                                          |   23 
 sfheaders-0.1.0/sfheaders/man/sf_multipoint.Rd                                               |   16 
 sfheaders-0.1.0/sfheaders/man/sf_multipolygon.Rd                                             |   18 
 sfheaders-0.1.0/sfheaders/man/sf_point.Rd                                                    |   21 
 sfheaders-0.1.0/sfheaders/man/sf_polygon.Rd                                                  |   17 
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 sfheaders-0.1.0/sfheaders/src/RcppExports.cpp                                                |  103 
 sfheaders-0.1.0/sfheaders/src/to_df.cpp                                                      |only
 sfheaders-0.1.0/sfheaders/src/to_sf.cpp                                                      |   24 
 sfheaders-0.1.0/sfheaders/src/to_sfc.cpp                                                     |   12 
 sfheaders-0.1.0/sfheaders/tests/testthat/test-df.R                                           |only
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 sfheaders-0.1.0/sfheaders/tests/testthat/test-sfc_linestring.R                               |   16 
 sfheaders-0.1.0/sfheaders/tests/testthat/test-sfc_multipoint.R                               |    2 
 sfheaders-0.1.0/sfheaders/tests/testthat/test-sfc_point.R                                    |   29 
 sfheaders-0.1.0/sfheaders/tests/testthat/test-sfg_multipolygon.R                             |   39 
 sfheaders-0.1.0/sfheaders/tests/testthat/test-sfg_point.R                                    |   15 
 sfheaders-0.1.0/sfheaders/tests/testthat/test-sfg_polygon.R                                  |   10 
 63 files changed, 3788 insertions(+), 1255 deletions(-)

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Package rPAex updated to version 1.0.1 with previous version 1.0 dated 2019-12-22

Title: Automatic Detection of Experimental Unit in Precision Agriculture
Description: A part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop.
Author: Felipe de Mendiburu [aut, cre], David Mauricio [aut], Rodrigo Morales [aut], Roberto Quiroz [aut]
Maintainer: Felipe de Mendiburu <fmendiburu@lamolina.edu.pe>

Diff between rPAex versions 1.0 dated 2019-12-22 and 1.0.1 dated 2020-01-20

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 18 files changed, 444 insertions(+), 444 deletions(-)

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Package dipsaus updated to version 0.0.4 with previous version 0.0.3 dated 2019-11-18

Title: A Dipping Sauce for Data Analysis and Visualizations
Description: Works as "add-ons" to packages like 'shiny', 'future', as well as 'rlang', and provides utility functions. Just like dipping sauce adding flavors to potato chips or pita bread, 'dipsaus' for data analysis and visualizations adds handy functions and enhancements to popular packages. The goal is to provide simple solutions that are frequently asked for online, such as how to synchronize 'shiny' inputs without freezing the app, or how to get memory size on 'Linux' or 'MacOS' system. The enhancements roughly fall into these four categories: 1. 'shiny' input widgets; 2. high-performance computing using 'RcppParallel' and 'future' package; 3. modify R calls and convert among numbers, strings, and other objects. 4. utility functions to get system information such like CPU chipset, memory limit, etc.
Author: Zhengjia Wang [aut, cre]
Maintainer: Zhengjia Wang <zhengjia.wang@rice.edu>

Diff between dipsaus versions 0.0.3 dated 2019-11-18 and 0.0.4 dated 2020-01-20

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 dipsaus-0.0.4/dipsaus/NEWS.md                                           |   15 
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 dipsaus-0.0.4/dipsaus/R/map-rds.R                                       |   32 
 dipsaus-0.0.4/dipsaus/R/map-redis.R                                     |   26 
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 85 files changed, 15020 insertions(+), 462 deletions(-)

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