Thu, 19 Mar 2020

Package provenance updated to version 2.4 with previous version 2.3 dated 2019-06-06

Title: Statistical Toolbox for Sedimentary Provenance Analysis
Description: Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances, and point-counting data with the chi-square distance. Also included are tools to plot compositional and point-counting data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.
Author: Pieter Vermeesch [aut, cre]
Maintainer: Pieter Vermeesch <p.vermeesch@ucl.ac.uk>

Diff between provenance versions 2.3 dated 2019-06-06 and 2.4 dated 2020-03-19

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More information about provenance at CRAN
Permanent link

Package mfbvar updated to version 0.5.3 with previous version 0.5.2 dated 2020-01-09

Title: Mixed-Frequency Bayesian VAR Models
Description: Estimation of mixed-frequency Bayesian vector autoregressive (VAR) models. The package implements a state space-based VAR model that handles mixed frequencies of the data. The model is estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution. Prior distributions that can be used include normal-inverse Wishart and normal-diffuse priors as well as steady-state priors. Stochastic volatility can be handled by common or factor stochastic volatility models.
Author: Sebastian Ankargren [cre, aut] (<https://orcid.org/0000-0003-4415-8734>), Yukai Yang [aut] (<https://orcid.org/0000-0002-2623-8549>), Gregor Kastner [ctb] (<https://orcid.org/0000-0002-8237-8271>)
Maintainer: Sebastian Ankargren <sebastian.ankargren@statistics.uu.se>

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More information about mfbvar at CRAN
Permanent link

Package metafor updated to version 2.4-0 with previous version 2.1-0 dated 2019-05-14

Title: Meta-Analysis Package for R
Description: A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.
Author: Wolfgang Viechtbauer [aut, cre] (<https://orcid.org/0000-0003-3463-4063>)
Maintainer: Wolfgang Viechtbauer <wvb@metafor-project.org>

Diff between metafor versions 2.1-0 dated 2019-05-14 and 2.4-0 dated 2020-03-19

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 metafor-2.4-0/metafor/R/zzz.r                                                   |    3 
 metafor-2.4-0/metafor/README.md                                                 |   18 
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 metafor-2.4-0/metafor/inst/CITATION                                             |    4 
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 metafor-2.4-0/metafor/man/hc.Rd                                                 |    2 
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More information about metafor at CRAN
Permanent link

Package hablar updated to version 0.3.0 with previous version 0.2.0 dated 2019-06-09

Title: Non-Astonishing Results in R
Description: Simple tools for converting columns to new data types. Intuitive functions for columns with missing values.
Author: David Sjoberg
Maintainer: David Sjoberg <dav.sjob@gmail.com>

Diff between hablar versions 0.2.0 dated 2019-06-09 and 0.3.0 dated 2020-03-19

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Package DVHmetrics updated to version 0.3.10 with previous version 0.3.9 dated 2019-07-15

Title: Analyze Dose-Volume Histograms and Check Constraints
Description: Functionality for analyzing dose-volume histograms (DVH) in radiation oncology: Read DVH text files, calculate DVH metrics as well as generalized equivalent uniform dose (gEUD), biologically effective dose (BED), equivalent dose in 2 Gy fractions (EQD2), normal tissue complication probability (NTCP), and tumor control probability (TCP). Show DVH diagrams, check and visualize quality assurance constraints for the DVH. Includes web-based graphical user interface.
Author: Daniel Wollschlaeger [aut, cre], Heiko Karle [aut], Heinz Schmidberger [ctb]
Maintainer: Daniel Wollschlaeger <wollschlaeger@uni-mainz.de>

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Package batchtools updated to version 0.9.13 with previous version 0.9.12 dated 2020-01-10

Title: Tools for Computation on Batch Systems
Description: As a successor of the packages 'BatchJobs' and 'BatchExperiments', this package provides a parallel implementation of the Map function for high performance computing systems managed by schedulers 'IBM Spectrum LSF' (<https://www.ibm.com/us-en/marketplace/hpc-workload-management>), 'OpenLava' (<http://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid Engine' (<http://www.univa.com/>), 'Slurm' (<http://slurm.schedmd.com/>), 'TORQUE/PBS' (<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>), or 'Docker Swarm' (<https://docs.docker.com/swarm/>). A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [aut], Dirk Surmann [ctb] (<https://orcid.org/0000-0003-0873-137X>)
Maintainer: Michel Lang <michellang@gmail.com>

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New package augmentedRCBD with initial version 0.1.2
Package: augmentedRCBD
Title: Analysis of Augmented Randomised Complete Block Designs
Version: 0.1.2
Authors@R: c( person(given = "J.", family = "Aravind", email = "j.aravind@icar.gov.in", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4791-442X")), person(given = "S.", family = "Mukesh Sankar", email = "mukeshsankar@gmail.com", role = "aut"), person(given = c("Dhammaprakash", "Pandhari"), family = "Wankhede", email = "d.wankhede@icar.gov.in", role = "aut"), person(given = "Vikender", family = "Kaur", email = "vikender.kaur@icar.gov.in", role = "aut"), person("ICAR-NBGPR", role = c("cph"), comment = c(url = "www.nbpgr.ernet.in")))
Description: Functions for analysis of data generated from experiments in augmented randomised complete block design according to Federer, W.T. (1961) <doi:10.2307/2527837>. Computes analysis of variance, adjusted means, descriptive statistics, genetic variability statistics etc. Further includes data visualization and report generation functions.
Copyright: 2015-2018, ICAR-NBPGR
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0.1)
VignetteBuilder: knitr
RoxygenNote: 7.0.2
URL: https://github.com/aravind-j/augmentedRCBD https://CRAN.R-project.org/package=augmentedRCBD https://aravind-j.github.io/augmentedRCBD https://doi.org/10.5281/zenodo.1310011
BugReports: https://github.com/aravind-j/augmentedRCBD/issues
Imports: emmeans, dplyr, flextable, ggplot2, grDevices, methods, moments, multcomp, multcompView, Rdpack, stats, stringi, officer, reshape2, utils
Suggests: knitr, rmarkdown, pander, testthat
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2020-03-19 17:44:43 UTC; J. Aravind
Author: J. Aravind [aut, cre] (<https://orcid.org/0000-0002-4791-442X>), S. Mukesh Sankar [aut], Dhammaprakash Pandhari Wankhede [aut], Vikender Kaur [aut], ICAR-NBGPR [cph] (www.nbpgr.ernet.in)
Maintainer: J. Aravind <j.aravind@icar.gov.in>
Repository: CRAN
Date/Publication: 2020-03-19 22:50:02 UTC

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Package qfasar updated to version 1.2.1 with previous version 1.2.0 dated 2017-01-09

Title: Quantitative Fatty Acid Signature Analysis in R
Description: An implementation of Quantitative Fatty Acid Signature Analysis (QFASA) in R. QFASA is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing the composition of fatty acids within lipids. Signature data from at least one predator and from samples of all potential prey types are required. Calibration coefficients, which adjust for the differential metabolism of individual fatty acids by predators, are also required. Given those data inputs, a predator signature is modeled as a mixture of prey signatures and its diet estimate is obtained as the mixture that minimizes a measure of distance between the observed and modeled signatures. A variety of estimation options and simulation capabilities are implemented. Please refer to the vignette for additional details and references.
Author: Jeffrey F. Bromaghin [aut, cre]
Maintainer: Jeffrey F. Bromaghin <jbromaghin@usgs.gov>

Diff between qfasar versions 1.2.0 dated 2017-01-09 and 1.2.1 dated 2020-03-19

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Package tm1r updated to version 1.1.4 with previous version 1.1.3 dated 2019-09-28

Title: The Integration Between 'IBM COGNOS TM1' and R
Description: Useful functions to connect to 'TM1' <https://www.ibm.com/uk-en/marketplace/planning-and-analytics> instance from R via REST API. With the functions in the package, data can be imported from 'TM1' via mdx view or native view, data can be sent to 'TM1', processes and chores can be executed, and cube and dimension metadata information can be taken.
Author: Muhammed Ali Onder
Maintainer: Muhammed Ali Onder <muhammedalionder@gmail.com>

Diff between tm1r versions 1.1.3 dated 2019-09-28 and 1.1.4 dated 2020-03-19

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Package bwsTools updated to version 1.1.0 with previous version 1.0.1 dated 2019-12-04

Title: Tools for Case 1 Best-Worst Scaling (MaxDiff) Designs
Description: Tools to design best-worst scaling designs (i.e., balanced incomplete block designs) and to analyze data from these designs, using aggregate and individual methods such as: difference scores, Louviere, Lings, Islam, Gudergan, & Flynn (2013) <doi:10.1016/j.ijresmar.2012.10.002>; analytical estimation, Lipovetsky & Conklin (2014) <doi:10.1016/j.jocm.2014.02.001>; empirical Bayes, Lipovetsky & Conklin (2015) <doi:10.1142/S1793536915500028>; Elo, Hollis (2018) <doi:10.3758/s13428-017-0898-2>; and network-based measures.
Author: Mark White [aut, cre]
Maintainer: Mark White <markhwhiteii@gmail.com>

Diff between bwsTools versions 1.0.1 dated 2019-12-04 and 1.1.0 dated 2020-03-19

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Package compareGroups updated to version 4.4.1 with previous version 4.2.0 dated 2019-09-19

Title: Descriptive Analysis by Groups
Description: Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
Author: Isaac Subirana [aut, cre] (<https://orcid.org/0000-0003-1676-0197>), Joan Salvador [ctb]
Maintainer: Isaac Subirana <isubirana@imim.es>

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Package tokenbrowser updated to version 0.1.4 with previous version 0.1.2 dated 2020-01-28

Title: Create Full Text Browsers from Annotated Token Lists
Description: Create browsers for reading full texts from a token list format. Information obtained from text analyses (e.g., topic modeling, word scaling) can be used to annotate the texts.
Author: Kasper Welbers and Wouter van Atteveldt
Maintainer: Kasper Welbers <kasperwelbers@gmail.com>

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Package sfheaders updated to version 0.2.1 with previous version 0.1.0 dated 2020-01-20

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>

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New package GmAMisc with initial version 1.1.1
Package: GmAMisc
Title: 'Gianmarco Alberti' Miscellaneous
Version: 1.1.1
Authors@R: person("Gianmarco", "Alberti", email = "gianmarcoalberti@gmail.com", role = c("aut", "cre"))
Description: Contains many functions useful for univariate outlier detection, permutation-based t-test, permutation-based chi-square test, visualization of residuals, and bootstrap 'Cramer V', plotting of the results of the 'Mann-Whitney' and 'Kruskal-Wallis' test, calculation of 'Brainerd-Robinson' similarity coefficient and subsequent clustering, validation of logistic regression models, optimism-corrected AUC, robust 'Bland-Altman' plot, calculation of posterior probability for different chronological relationships between two Bayesian radiocarbon phases, point pattern analysis, clustering of spatial features.
Depends: R (>= 3.4.0), maptools (>= 0.9-2)
Imports: caTools (>= 1.17.1), classInt (>= 0.2-3), cluster (>= 2.0.7-1), coin (>= 1.2-2), corrplot (>= 0.84), DescTools (>= 0.99.24), dismo (>= 1.1-4), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), graphics (>= 3.4.3), grDevices (>= 3.4.3), gridExtra (>= 2.3), Hmisc (>= 4.1-1), InPosition (>= 0.12.7), kimisc (>= 0.4), lsr (>= 0.5), methods (>= 3.4.3), plyr (>= 1.8.4), pROC (>= 1.12.1), raster (>= 2.6-7), RcmdrMisc (>= 1.0-10), rgdal (>= 1.3-3), rgeos (>= 0.3-28), sp (>= 1.3-1), spatstat (>= 1.56-0), stats (>= 3.4.3), utils (>= 3.4.3)
Suggests: archdata (>= 1.2), gdistance (>= 1.2-2), resample (>= 0.4)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-03-19 17:27:14 UTC; gianmarcoalberti
Author: Gianmarco Alberti [aut, cre]
Maintainer: Gianmarco Alberti <gianmarcoalberti@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-19 18:10:02 UTC

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New package sentryR with initial version 1.1.0
Package: sentryR
Type: Package
Title: Send Errors and Messages to 'Sentry'
Version: 1.1.0
Authors@R: c( person("Joao", "Santiago", email = "santiago@billie.io", role = c("aut", "cre")), person("Daniel", "Kirsch", role = "aut", email = "daniel@billie.io"), # copyright holders person("Billie", role = c("cph", "fnd")))
Description: Unofficial client for 'Sentry' <https://sentry.io>, a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the 'Plumber' package.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
ByteCompile: true
URL: https://github.com/ozean12/sentryR
BugReports: https://github.com/ozean12/sentryR/issues
RoxygenNote: 7.0.2
Imports: httr, jsonlite, stringr, stats, tibble, uuid
Suggests: covr, httptest, mockery, testthat (>= 2.1.0)
NeedsCompilation: no
Packaged: 2020-03-16 07:32:17 UTC; santiago
Author: Joao Santiago [aut, cre], Daniel Kirsch [aut], Billie [cph, fnd]
Maintainer: Joao Santiago <santiago@billie.io>
Repository: CRAN
Date/Publication: 2020-03-19 17:10:02 UTC

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New package rMVP with initial version 0.99.18
Package: rMVP
Type: Package
Title: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool
Version: 0.99.18
Date: 2020-03-02
Authors@R: c( person("Lilin", "Yin", role = "aut"), person("Haohao", "Zhang", role = "aut"), person("Zhenshuang", "Tang", role = "aut"), person("Jingya", "Xu", role = "aut"), person("Dong", "Yin", role = "aut"), person("Zhiwu", "Zhang", role = "aut"), person("Xiaohui", "Yuan", role = "aut"), person("Mengjin", "Zhu", role = "aut"), person("Shuhong", "Zhao", role = "aut"), person("Xinyun", "Li", role = "aut"), person("Qishan", "Wang", role = "ctb"), person("Feng", "Tian", role = "ctb"), person("Hyunmin", "Kang", role = "ctb"), person("Xiang", "Zhou", role = "ctb"), person("Xiaolei", "Liu", role = c("cre", "aut", "cph"), email = "xiaoleiliu@mail.hzau.edu.cn"))
Description: A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and 'SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).
License: Apache License 2.0
SystemRequirements: C++11
URL: https://github.com/XiaoleiLiuBio/rMVP
BugReports: https://github.com/XiaoleiLiuBio/rMVP/issues
Imports: utils, stats, methods, graphics, grDevices, MASS, Rcpp
Depends: R (>= 3.3), bigmemory
LinkingTo: Rcpp, RcppEigen, RcppProgress, BH, bigmemory
NeedsCompilation: yes
Suggests: knitr, testthat, rmarkdown
RoxygenNote: 6.1.1
Maintainer: Xiaolei Liu <xiaoleiliu@mail.hzau.edu.cn>
Packaged: 2020-03-15 08:46:25 UTC; hyacz
Author: Lilin Yin [aut], Haohao Zhang [aut], Zhenshuang Tang [aut], Jingya Xu [aut], Dong Yin [aut], Zhiwu Zhang [aut], Xiaohui Yuan [aut], Mengjin Zhu [aut], Shuhong Zhao [aut], Xinyun Li [aut], Qishan Wang [ctb], Feng Tian [ctb], Hyunmin Kang [ctb], Xiang Zhou [ctb], Xiaolei Liu [cre, aut, cph]
Repository: CRAN
Date/Publication: 2020-03-19 18:00:02 UTC

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Package rcrossref updated to version 1.0.0 with previous version 0.9.2 dated 2019-05-04

Title: Client for Various 'CrossRef' 'APIs'
Description: Client for various 'CrossRef' 'APIs', including 'metadata' search with their old and newer search 'APIs', get 'citations' in various formats (including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs' to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to full text of articles when available.
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>), Hao Zhu [aut], Najko Jahn [aut], Carl Boettiger [aut], Karthik Ram [aut]
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package pROC updated to version 1.16.2 with previous version 1.16.1 dated 2020-01-14

Title: Display and Analyze ROC Curves
Description: Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Author: Xavier Robin [cre, aut] (<https://orcid.org/0000-0002-6813-3200>), Natacha Turck [aut], Alexandre Hainard [aut], Natalia Tiberti [aut], Frédérique Lisacek [aut], Jean-Charles Sanchez [aut], Markus Müller [aut], Stefan Siegert [ctb] (Fast DeLong code), Matthias Doering [ctb] (Hand & Till Multiclass)
Maintainer: Xavier Robin <pROC-cran@xavier.robin.name>

Diff between pROC versions 1.16.1 dated 2020-01-14 and 1.16.2 dated 2020-03-19

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Package OncoBayes2 updated to version 0.6-3 with previous version 0.6-0 dated 2020-02-11

Title: Bayesian Logistic Regression for Oncology Dose-Escalation Trials
Description: Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) <doi:10.1002/sim.3230> and Neuenschwander et al. (2016) <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Author: Novartis Pharma AG [cph], Sebastian Weber [aut, cre], Lukas A. Widmer [aut], Andrew Bean [aut], Trustees of Columbia University [cph] (R/stanmodels.R, configure, configure.win)
Maintainer: Sebastian Weber <sebastian.weber@novartis.com>

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Package heuristicsmineR updated to version 0.2.3 with previous version 0.2.2 dated 2020-02-26

Title: Discovery of Process Models with the Heuristics Miner
Description: Provides the heuristics miner algorithm for process discovery as proposed by Weijters et al. (2011) <doi:10.1109/CIDM.2011.5949453>. The algorithm builds a causal net from an event log created with the 'bupaR' package. Event logs are a set of ordered sequences of events for which 'bupaR' provides the S3 class eventlog(). The discovered causal nets can be visualised as 'htmlwidgets' and it is possible to annotate them with the occurrence frequency or processing and waiting time of process activities.
Author: Felix Mannhardt [aut, cre]
Maintainer: Felix Mannhardt <felix.mannhardt@sintef.no>

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New package glmglrt with initial version 0.1.0
Package: glmglrt
Type: Package
Title: GLRT P-Values in Generalized Linear Models
Version: 0.1.0
Maintainer: André GILLIBERT <andre.gillibert@chu-rouen.fr>
Author: André GILLIBERT [aut, cre]
Authors@R: person("André","GILLIBERT",role=c("aut","cre"),email="andre.gillibert@chu-rouen.fr")
Description: Provides functions to compute Generalized Likelihood Ratio Tests (GLRT) and Rao's score tests of simple and complex contrasts of Generalized Linear Models (GLMs). It provides the same interface as summary.glm(), adding GLRT P-values, less biased than Wald's P-values and consistent with profile-likelihood confidence interval generated by confint().
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: base, stats, parameters (>= 0.1.0), MASS
RoxygenNote: 7.1.0
Suggests: testthat (>= 2.1.0), lme4, nlme, datasets
Enhances: nnet, survival, lmerTest
NeedsCompilation: no
Packaged: 2020-03-14 22:01:34 UTC; andre.gillibert
Repository: CRAN
Date/Publication: 2020-03-19 17:20:02 UTC

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Package afpt updated to version 1.1.0.1 with previous version 1.1.0.0 dated 2020-03-17

Title: Tools for Modelling of Animal Flight Performance
Description: Allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model described in Klein Heerenbrink et al. (2015) <doi:10.1098/rspa.2014.0952>. Taking inspiration from the program 'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. 'afpt' can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions.
Author: Marco KleinHeerenbrink [aut, cre], Anders Hedenström [fnd]
Maintainer: Marco KleinHeerenbrink <Marco.KleinHeerenbrink@zoo.ox.ac.uk>

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Package actuaryr updated to version 1.1.0 with previous version 1.0.0 dated 2020-03-06

Title: Develop Actuarial Models
Description: Actuarial reports are prepared for the last day of a specific period, such as a month, a quarter or a year. Actuarial models assume that certain events happen at the beginning or end of periods. The package contains functions to easily refer to the first or last (working) day within a specific period relative to a base date to facilitate actuarial reporting and to compare results.
Author: Zuzanna Chmielewska [aut, cre] (<https://orcid.org/0000-0001-7622-3321>)
Maintainer: Zuzanna Chmielewska <zchmielewska@gmail.com>

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New package optinterv with initial version 0.1.0
Package: optinterv
Type: Package
Title: Optimal Intervention
Version: 0.1.0
Authors@R: c( person("Elad", "Guttman", role = c("aut", "cre"), email = "eladguttman@mail.tau.ac.il"), person("Oren", "Danieli", role = "aut"))
Description: Provides both parametric and non-parametric estimates of the correlates of some desired outcome (e.g. test scores, income) using a new method proposed by Danieli, Devi and Fryer (2019). This method relaxes the assumption that one can alter individual characteristics in any way the data suggest is optimal, and so can be used anytime one wants to use observational data to better optimize social experiments designed to increase some desired outcome.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: rootSolve, distances, weights, boot, lattice, pbapply, Hmisc, stats, graphics
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-03-15 09:54:59 UTC; eladg
Author: Elad Guttman [aut, cre], Oren Danieli [aut]
Maintainer: Elad Guttman <eladguttman@mail.tau.ac.il>
Repository: CRAN
Date/Publication: 2020-03-19 16:30:02 UTC

More information about optinterv at CRAN
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New package jquerylib with initial version 0.1
Package: jquerylib
Title: Obtain 'jQuery' as an HTML Dependency Object
Version: 0.1
Authors@R: c( person("Carson", "Sievert", role = c("aut", "cre"), email = "carson@rstudio.com", comment = c(ORCID = "0000-0002-4958-2844")), person("Joe", "Cheng", role = "aut", email = "joe@rstudio.com"), person(family = "RStudio", role = "cph"), person(family = "jQuery Foundation", role = "cph", comment = "jQuery library and jQuery UI library"), person(family = "jQuery contributors", role = c("ctb", "cph"), comment = "jQuery library; authors listed in inst/lib/jquery-AUTHORS.txt") )
Description: Obtain any major version of 'jQuery' (<https://code.jquery.com/>) and use it in any webpage generated by 'htmltools' (e.g. 'shiny', 'htmlwidgets', and 'rmarkdown'). Most R users don't need to use this package directly, but other R packages (e.g. 'shiny', 'rmarkdown', etc.) depend on this package to avoid bundling redundant copies of 'jQuery'.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Imports: htmltools
Suggests: testthat
NeedsCompilation: no
Packaged: 2020-03-11 17:11:21 UTC; cpsievert
Author: Carson Sievert [aut, cre] (<https://orcid.org/0000-0002-4958-2844>), Joe Cheng [aut], RStudio [cph], jQuery Foundation [cph] (jQuery library and jQuery UI library), jQuery contributors [ctb, cph] (jQuery library; authors listed in inst/lib/jquery-AUTHORS.txt)
Maintainer: Carson Sievert <carson@rstudio.com>
Repository: CRAN
Date/Publication: 2020-03-19 16:50:02 UTC

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New package wsbackfit with initial version 1.0
Package: wsbackfit
Type: Package
Title: Weighted Smooth Backfitting for Structured Models
Version: 1.0
Date: 2020-03-12
Imports: graphics, stats
Authors@R: c(person(given = "Javier", family = "Roca-Pardinas", email = "roca@uvigo.es", role = c("aut", "cre")), person(given = "Maria Xose", family = "Rodriguez-Alvarez", email = "mxrodriguez@bcamath.org", role = "aut"), person(given = "Stefan", family = "Sperlich", email = "stefan.sperlich@unige.dot.ch", role = "aut"), person(give = "Alan", family = "Miller (FORTRAN code lsq.f90: weighted least-squares module)", role = "ctb"))
Description: Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.
License: GPL
LazyLoad: yes
Depends: R (>= 3.5.0)
NeedsCompilation: yes
Packaged: 2020-03-13 20:18:30 UTC; mrodriguez
Author: Javier Roca-Pardinas [aut, cre], Maria Xose Rodriguez-Alvarez [aut], Stefan Sperlich [aut], Alan Miller (FORTRAN code lsq.f90: weighted least-squares module) [ctb]
Maintainer: Javier Roca-Pardinas <roca@uvigo.es>
Repository: CRAN
Date/Publication: 2020-03-19 15:40:02 UTC

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Package tidyLPA updated to version 1.0.6 with previous version 1.0.5 dated 2020-02-09

Title: Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software
Description: An interface to the 'mclust' package to easily carry out latent profile analysis ("LPA"). Provides functionality to estimate commonly-specified models. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Also has functions to interface to the commercial 'MPlus' software via the 'MplusAutomation' package.
Author: Joshua M Rosenberg [aut, cre], Caspar van Lissa [aut], Jennifer A Schmidt [ctb], Patrick N Beymer [ctb], Daniel Anderson [ctb], Matthew J. Schell [ctb]
Maintainer: Joshua M Rosenberg <jmichaelrosenberg@gmail.com>

Diff between tidyLPA versions 1.0.5 dated 2020-02-09 and 1.0.6 dated 2020-03-19

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Package tidybayes updated to version 2.0.2 with previous version 2.0.1 dated 2020-01-28

Title: Tidy Data and 'Geoms' for Bayesian Models
Description: Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands.
Author: Matthew Kay [aut, cre], Timothy Mastny [ctb]
Maintainer: Matthew Kay <mjskay@umich.edu>

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 NEWS.md                                                                                 |   10 
 R/binning_methods.R                                                                     |    2 
 R/compare_levels.R                                                                      |    6 
 R/gather_variables.R                                                                    |    4 
 R/parse_dist.R                                                                          |    2 
 R/predicted_draws.R                                                                     |   15 
 R/stat_sample_slabinterval.R                                                            |   29 
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 tests/testthat/test.gather_draws.R                                                      |    2 
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 tests/testthat/test.gather_variables.R                                                  |    2 
 tests/testthat/test.point_interval.R                                                    |    7 
 tests/testthat/test.predicted_draws.R                                                   |   60 
 tests/testthat/test.residual_draws.R                                                    |    5 
 tests/testthat/test.spread_draws.R                                                      |   51 
 vignettes/slabinterval.Rmd                                                              |   13 
 vignettes/tidy-brms.Rmd                                                                 |    2 
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Package surveillance updated to version 1.18.0 with previous version 1.17.3 dated 2019-12-16

Title: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena
Description: Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of H<f6>hle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by H<f6>hle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
Author: Michael H<f6>hle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Saban<e9>s Bov<e9> [ctb], Ma<eb>lle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)
Maintainer: Sebastian Meyer <seb.meyer@fau.de>

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Package rtmpt updated to version 0.1-19 with previous version 0.1-18 dated 2020-02-04

Title: Fitting RT-MPT Models
Description: Fit response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) <doi:10.1016/j.jmp.2017.12.003>. The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. 'rtmpt' is a Bayesian framework and posterior samples are sampled using a Metropolis-Gibbs sampler like the one described in the Klauer and Kellen (2018), but with some modifications. Other than in the original C++ program we use the free and open source GNU Scientific Library (GSL). There is also the possibility to suppress single process completion times.
Author: Raphael Hartmann [aut, cre], Karl C. Klauer [cph, aut, ctb, ths], Henrik Singmann [aut, ctb, cph], Jean Marie Linhart [cph]
Maintainer: Raphael Hartmann <raphael.hartmann@protonmail.com>

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New package ROCnReg with initial version 1.0
Package: ROCnReg
Type: Package
Title: ROC Curve Inference with and without Covariates
Version: 1.0
Date: 2020-03-14
Authors@R: c( person(given = "Maria Xose", family = "Rodriguez-Alvarez", email = "mxrodriguez@bcamath.org", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1329-9238")), person(given = "Vanda", family = "Inacio", email = "Vanda.Inacio@ed.ac.uk", role = c("aut"), comment = c(ORCID = "0000-0001-8084-1616")))
Imports: stats, grDevices, graphics, splines, np, Matrix, moments, nor1mix, spatstat, lattice, MASS, pbivnorm
Description: Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) <doi:10.1002/sim.2496>; Faraggi, D. (2003) <doi:10.1111/1467-9884.00350>; Gu, J. et al. (2008) <doi:10.1002/sim.3366>; Inacio de Carvalho, V. et al. (2013) <doi:10.1214/13-BA825>; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2018) <arXiv:1806.00473>; Janes, H., and Pepe, M.S. (2009) <doi:10.1093/biomet/asp002>; Pepe, M.S. (1998) <https://www.jstor.org/stable/2534001?seq=1>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1016/j.csda.2010.07.018>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1007/s11222-010-9184-1>.
License: GPL
NeedsCompilation: no
Packaged: 2020-03-14 20:55:35 UTC; mrodriguez
Author: Maria Xose Rodriguez-Alvarez [aut, cre] (<https://orcid.org/0000-0002-1329-9238>), Vanda Inacio [aut] (<https://orcid.org/0000-0001-8084-1616>)
Maintainer: Maria Xose Rodriguez-Alvarez <mxrodriguez@bcamath.org>
Repository: CRAN
Date/Publication: 2020-03-19 15:50:15 UTC

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Package R2DT updated to version 0.2 with previous version 0.1 dated 2018-01-26

Title: Translation of Base R-Like Functions for 'data.table' Objects
Description: Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.
Author: Robin Van Oirbeek
Maintainer: Robin Van Oirbeek <robin.vanoirbeek@gmail.com>

Diff between R2DT versions 0.1 dated 2018-01-26 and 0.2 dated 2020-03-19

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Package nmathresh updated to version 0.1.5 with previous version 0.1.4 dated 2019-01-07

Title: Thresholds and Invariant Intervals for Network Meta-Analysis
Description: Calculation and presentation of decision-invariant bias adjustment thresholds and intervals for Network Meta-Analysis, as described by Phillippo et al. (2018) <doi:10.1111/rssa.12341>. These describe the smallest changes to the data that would result in a change of decision.
Author: David Phillippo [aut, cre]
Maintainer: David Phillippo <david.phillippo@bristol.ac.uk>

Diff between nmathresh versions 0.1.4 dated 2019-01-07 and 0.1.5 dated 2020-03-19

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Permanent link

New package MQMF with initial version 0.1.0
Package: MQMF
Title: Modelling and Quantitative Methods in Fisheries
Version: 0.1.0
Date: 2020-03-14
Authors@R: person("Malcolm", "Haddon", role = c("aut", "cre"), email = "malcolm.haddon@gmail.com")
Maintainer: Malcolm Haddon <malcolm.haddon@gmail.com>
Description: Complements the book "Using R for Modelling and Quantitative Methods in Fisheries" ISBN: 9780367469894, being published in June 2020 by Chapman & Hall in their "Using R series". There are numerous functions and data-sets that are used in the book's many practical examples.
URL: https://github.com/haddonm/MQMF
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: MASS, mvtnorm
Suggests: knitr, rmarkdown
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-03-14 01:52:21 UTC; Malcolm
Author: Malcolm Haddon [aut, cre]
Repository: CRAN
Date/Publication: 2020-03-19 15:20:02 UTC

More information about MQMF at CRAN
Permanent link

New package ggstar with initial version 0.0.4
Package: ggstar
Title: Star Layer for 'ggplot2'
Version: 0.0.4
Authors@R: person(given = "Shuangbin", family = "Xu", role = c("aut", "cre"), email = "xshuangbin@163.com")
Description: To create the regular polygon layer for easily discernible shapes, we developed the package, it can be easily used if you know the 'ggplot2'.
Depends: R (>= 3.5)
Imports: grid, utils, ggplot2, scales, gridExtra
Suggests: knitr
License: Artistic-2.0
Encoding: UTF-8
URL: https://github.com/xiangpin/ggstar/
BugReports: https://github.com/xiangpin/ggstar/issues
VignetteBuilder: knitr
LazyData: false
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-03-13 11:03:32 UTC; xushuangbin
Author: Shuangbin Xu [aut, cre]
Maintainer: Shuangbin Xu <xshuangbin@163.com>
Repository: CRAN
Date/Publication: 2020-03-19 15:10:11 UTC

More information about ggstar at CRAN
Permanent link

Package distr6 updated to version 1.3.5 with previous version 1.3.4 dated 2020-02-19

Title: The Complete R6 Probability Distributions Interface
Description: An R6 object oriented distributions package. Unified interface for 42 probability distributions and 11 kernels including functionality for multiple scientific types. Additionally functionality for composite distributions and numerical imputation. Design patterns including wrappers and decorators are described in Gamma et al. (1994, ISBN:0-201-63361-2). For quick reference of probability distributions including d/p/q/r functions and results we refer to McLaughlin, M. P. (2001). Additionally Devroye (1986, ISBN:0-387-96305-7) for sampling the Dirichlet distribution, Gentle (2009) <doi:10.1007/978-0-387-98144-4> for sampling the Multivariate Normal distribution and Michael et al. (1976) <doi:10.2307/2683801> for sampling the Wald distribution.
Author: Raphael Sonabend [aut, cre] (<https://orcid.org/0000-0001-9225-4654>), Franz Kiraly [aut], Peter Ruckdeschel [ctb] (Author of distr), Matthias Kohl [ctb] (Author of distr), Shen Chen [ctb], Jordan Deenichin [ctb], Chengyang Gao [ctb], Chloe Zhaoyuan Gu [ctb], Yunjie He [ctb], Xiaowen Huang [ctb], Shuhan Liu [ctb], Runlong Yu [ctb], Chijing Zeng [ctb], Qian Zhou [ctb]
Maintainer: Raphael Sonabend <raphael.sonabend.15@ucl.ac.uk>

Diff between distr6 versions 1.3.4 dated 2020-02-19 and 1.3.5 dated 2020-03-19

 DESCRIPTION                                       |    9 
 MD5                                               |  353 ++++++++-------
 NAMESPACE                                         |    1 
 NEWS.md                                           |    7 
 R/Distribution.R                                  |  491 ++++++++++------------
 R/DistributionDecorator.R                         |    1 
 R/DistributionDecorator_CoreStatistics.R          |   16 
 R/DistributionDecorator_ExoticStatistics.R        |   22 
 R/DistributionDecorator_FunctionImputation.R      |   22 
 R/Distribution_Kernel.R                           |   30 -
 R/Distribution_SDistribution.R                    |   28 -
 R/SDistribution_Categorical.R                     |   14 
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 R/SDistribution_Empirical.R                       |   14 
 R/SDistribution_Erlang.R                          |only
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 R/Wrapper_HuberizedDistribution.R                 |   50 +-
 R/Wrapper_ProductDistribution.R                   |    4 
 R/Wrapper_TruncatedDistribution.R                 |   26 -
 R/Wrapper_VectorDistribution.R                    |   26 -
 R/assertions.R                                    |   26 -
 R/c.Distribution.R                                |    4 
 R/decorate.R                                      |    2 
 R/getParameterSet.R                               |   22 
 R/lines.R                                         |    4 
 R/plot.R                                          |   10 
 R/simulateEmpiricalDistribution.R                 |    6 
 R/zzz.R                                           |    2 
 inst/doc/distr6.R                                 |    8 
 inst/doc/distr6.html                              |   22 
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 man/decorators.Rd                                 |    2 
 man/dmax.Rd                                       |    2 
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 man/inf.Rd                                        |    2 
 man/kurtosisType.Rd                               |    2 
 man/properties.Rd                                 |    2 
 man/skewnessType.Rd                               |    2 
 man/sup.Rd                                        |    2 
 man/support.Rd                                    |    2 
 man/symmetry.Rd                                   |    2 
 man/traits.Rd                                     |    2 
 man/type.Rd                                       |    2 
 man/valueSupport.Rd                               |    2 
 man/variateForm.Rd                                |    2 
 tests/testthat/Rplots.pdf                         |only
 tests/testthat/test-DistArcsine.R                 |   14 
 tests/testthat/test-DistBernoulli.R               |   14 
 tests/testthat/test-DistBeta.R                    |   16 
 tests/testthat/test-DistBetaNoncentral.R          |   16 
 tests/testthat/test-DistBinomial.R                |   16 
 tests/testthat/test-DistCategorical.R             |    8 
 tests/testthat/test-DistCauchy.R                  |   14 
 tests/testthat/test-DistChiSquared.R              |   20 
 tests/testthat/test-DistChiSquaredNoncentral.R    |   18 
 tests/testthat/test-DistDegenerate.R              |   14 
 tests/testthat/test-DistDirichlet.R               |   14 
 tests/testthat/test-DistDiscreteUniform.R         |   16 
 tests/testthat/test-DistEmpirical.R               |    6 
 tests/testthat/test-DistErlang.R                  |only
 tests/testthat/test-DistExponential.R             |   14 
 tests/testthat/test-DistFDistribution.R           |   18 
 tests/testthat/test-DistFDistributionNoncentral.R |   18 
 tests/testthat/test-DistFrechet.R                 |   14 
 tests/testthat/test-DistGamma.R                   |   14 
 tests/testthat/test-DistGeometric.R               |   14 
 tests/testthat/test-DistGompertz.R                |   14 
 tests/testthat/test-DistGumbel.R                  |   14 
 tests/testthat/test-DistHypergeometric.R          |   14 
 tests/testthat/test-DistInverseGamma.R            |   14 
 tests/testthat/test-DistLaplace.R                 |   14 
 tests/testthat/test-DistLogLogistic.R             |   16 
 tests/testthat/test-DistLogarithmic.R             |   14 
 tests/testthat/test-DistLogistic.R                |   14 
 tests/testthat/test-DistLognormal.R               |   14 
 tests/testthat/test-DistMultinomial.R             |   14 
 tests/testthat/test-DistMultivariateNormal.R      |   18 
 tests/testthat/test-DistNegativeBinomial.R        |   20 
 tests/testthat/test-DistNormal.R                  |   14 
 tests/testthat/test-DistPareto.R                  |   14 
 tests/testthat/test-DistPoisson.R                 |   14 
 tests/testthat/test-DistRayleigh.R                |   14 
 tests/testthat/test-DistStudentT.R                |   14 
 tests/testthat/test-DistStudentTNoncentral.R      |   14 
 tests/testthat/test-DistTriangular.R              |   18 
 tests/testthat/test-DistUniform.R                 |   14 
 tests/testthat/test-DistWald.R                    |   14 
 tests/testthat/test-DistWeibull.R                 |   14 
 tests/testthat/test-DistWeightedDiscrete.R        |    6 
 tests/testthat/test-Distributions.R               |   20 
 tests/testthat/test-KernelCosine.R                |    2 
 tests/testthat/test-KernelEpanechnikov.R          |    2 
 tests/testthat/test-KernelLogistic.R              |    2 
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 tests/testthat/test-VectorDistribution.R          |   14 
 tests/testthat/test-cDistribution.R               |    6 
 tests/testthat/test-continuousDistribution.R      |    8 
 tests/testthat/test-decorator.R                   |    2 
 tests/testthat/test-discreteDistribution.R        |   12 
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 vignettes/distr6.rmd                              |    8 
 182 files changed, 1935 insertions(+), 1893 deletions(-)

More information about distr6 at CRAN
Permanent link

New package spatstat.gui with initial version 1.0-2
Package: spatstat.gui
Type: Package
Title: Interactive Graphics Functions for the 'spatstat' Package
Version: 1.0-2
Date: 2020-03-13
Authors@R: c(person("Adrian", "Baddeley", role = c("aut", "cre"), email = "Adrian.Baddeley@curtin.edu.au", comment = c(ORCID="0000-0001-9499-8382")), person("Rolf", "Turner", role = "aut", email="r.turner@auckland.ac.nz", comment=c(ORCID="0000-0001-5521-5218")), person("Ege", "Rubak", role = "aut", email = "rubak@math.aau.dk", comment=c(ORCID="0000-0002-6675-533X")))
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>
Depends: R (>= 3.3.0), spatstat (>= 1.63-0), rpanel, tcltk, stats, graphics, grDevices, utils, methods
Imports: spatstat.utils (>= 1.17-0)
Description: Extension to the 'spatstat' package, containing interactive graphics capabilities.
License: GPL (>= 2)
LazyLoad: yes
ByteCompile: true
NeedsCompilation: no
Packaged: 2020-03-13 02:17:14 UTC; adrian
Author: Adrian Baddeley [aut, cre] (<https://orcid.org/0000-0001-9499-8382>), Rolf Turner [aut] (<https://orcid.org/0000-0001-5521-5218>), Ege Rubak [aut] (<https://orcid.org/0000-0002-6675-533X>)
Repository: CRAN
Date/Publication: 2020-03-19 14:30:03 UTC

More information about spatstat.gui at CRAN
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Package smurf updated to version 1.0.5 with previous version 1.0.4 dated 2020-02-12

Title: Sparse Multi-Type Regularized Feature Modeling
Description: Implementation of the SMuRF algorithm of Devriendt et al. (2018) <arXiv:1810.03136> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.
Author: Tom Reynkens [aut, cre] (<https://orcid.org/0000-0002-5516-5107>), Sander Devriendt [aut], Katrien Antonio [aut]
Maintainer: Tom Reynkens <tomreynkens@hotmail.com>

Diff between smurf versions 1.0.4 dated 2020-02-12 and 1.0.5 dated 2020-03-19

 smurf-1.0.4/smurf/R/rent.R                      |only
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 smurf-1.0.5/smurf/R/Lambda_select.R             |    2 
 smurf-1.0.5/smurf/R/PO.R                        |    2 
 smurf-1.0.5/smurf/R/glmsmurf_fit.R              |    2 
 smurf-1.0.5/smurf/R/residuals.R                 |    2 
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 smurf-1.0.5/smurf/R/stand.R                     |    2 
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 smurf-1.0.5/smurf/tests/testthat/test_input.R   |    5 ++
 smurf-1.0.5/smurf/tests/testthat/test_output.R  |    8 +--
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 28 files changed, 101 insertions(+), 73 deletions(-)

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New package shinyr with initial version 0.2.6
Package: shinyr
Type: Package
Title: Data Insights Through Inbuilt R Shiny App
Version: 0.2.6
Authors@R: c(person("Jayachandra", "N", email = "itsjay510@gmail.com", role = c("aut", "cre")), person("Pushker", "Ravindra", email = "pushker@gmail.com", role = c("aut")))
Description: It builds dynamic R shiny based dashboards to analyze any CSV files. It provides simple dashboard design to subset the data, perform exploratory data analysis and preliminary machine learning (supervised and unsupervised). It also provides filters based on columns of interest.
Depends: R (>= 3.1.0),
Imports: dplyr, shiny, shinydashboard, tm, wordcloud, corrplot, randomForest, RColorBrewer, DMwR, caret, nnet, plotly, e1071, knitr
Maintainer: Jayachandra N <itsjay510@gmail.com>
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Suggests: testthat
NeedsCompilation: no
VignetteBuilder: knitr
Packaged: 2020-03-13 10:31:20 UTC; rpush1
Author: Jayachandra N [aut, cre], Pushker Ravindra [aut]
Repository: CRAN
Date/Publication: 2020-03-19 15:00:02 UTC

More information about shinyr at CRAN
Permanent link

Package phytools updated to version 0.7-20 with previous version 0.6-99 dated 2019-06-18

Title: Phylogenetic Tools for Comparative Biology (and Other Things)
Description: A wide range of functions for phylogenetic analysis. Functionality is concentrated in phylogenetic comparative biology, but also includes a diverse array of methods for visualizing, manipulating, reading or writing, and even inferring phylogenetic trees and data. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, simulation of phylogenies and data, and multivariate analysis. There are a broad range of plotting methods for phylogenies and comparative data which include, but are not restricted to, methods for mapping trait evolution on trees, for projecting trees into phenotypic space or a geographic map, and for visualizing correlated speciation between trees. Finally, there are a number of functions for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data not covered by other packages. For instance, there are functions for randomly or non-randomly attaching species or clades to a phylogeny, for estimating supertrees or consensus phylogenies from a set, for simulating trees and phylogenetic data under a range of models, and for a wide variety of other manipulations and analyses that phylogenetic biologists might find useful in their research.
Author: Liam J. Revell
Maintainer: Liam J. Revell <liam.revell@umb.edu>

Diff between phytools versions 0.6-99 dated 2019-06-18 and 0.7-20 dated 2020-03-19

 DESCRIPTION               |    8 -
 MD5                       |   96 ++++++++--------
 NAMESPACE                 |   22 ++-
 R/backbonePhylo.R         |   10 +
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 R/fitMk.R                 |   17 ++
 R/fitpolyMk.R             |    3 
 R/ltt.R                   |   35 +++--
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 R/mcmcMk.R                |   27 ++--
 R/phyl.pca.R              |   39 ++++++
 R/phylosig.R              |  234 +++++++++++++++++++++++++++++++--------
 R/plotBranchbyTrait.R     |   22 ++-
 R/plotSimmap.R            |   81 ++++++++++---
 R/read.simmap.R           |    3 
 R/simBMphylo.R            |only
 R/threshBayes.R           |    8 +
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 R/write.simmap.R          |  275 ++++++++++++++++++++++++++++++++++++----------
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 51 files changed, 886 insertions(+), 320 deletions(-)

More information about phytools at CRAN
Permanent link

New package parzer with initial version 0.1.0
Package: parzer
Type: Package
Title: Parse Messy Geographic Coordinates
Description: Parse geographic coordinates from various formats to decimal degree numeric values. Parse coordinates into their parts (degree, minutes, seconds); calculate hemisphere from coordinates; pull out individually degrees, minutes, or seconds; add and subtract degrees, minutes, and seconds. C++ code herein originally inspired from code written by Jeffrey D. Bogan (<http://bit.ly/CLongLatString>), but then completely re-written.
Version: 0.1.0
Authors@R: c( person("Scott", "Chamberlain", role = c("aut", "cre"), email = "sckott@protonmail.com", comment = c(ORCID = "0000-0003-1444-9135")), person("Jeffrey", "Bogan", role = "ctb", comment = "C++ code originally from Jeffrey Bogan, but completely re-written"), person("Julien", "Brun", role = "rev", comment = "Julien Brun reviewed the package, see https://github.com/ropensci/onboarding/issues/341"), person("Maria", "Munafó", role = "rev", comment = "Maria Munafó reviewed the package, see https://github.com/ropensci/onboarding/issues/341"), person("rOpenSci", role = "fnd", comment = "https://ropensci.org") )
License: MIT + file LICENSE
URL: https://github.com/ropensci/parzer (devel) https://docs.ropensci.org/parzer (docs)
BugReports: https://github.com/ropensci/parzer/issues
Imports: Rcpp (>= 1.0.2)
Suggests: testthat, randgeo, knitr, rmarkdown
LinkingTo: Rcpp
SystemRequirements: C++11
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.1.0
X-schema.org-applicationCategory: Geospatial
X-schema.org-keywords: geospatial, data, latitude, longitude, parser, coordinates
X-schema.org-isPartOf: https://ropensci.org
NeedsCompilation: yes
Packaged: 2020-03-13 18:57:49 UTC; sckott
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>), Jeffrey Bogan [ctb] (C++ code originally from Jeffrey Bogan, but completely re-written), Julien Brun [rev] (Julien Brun reviewed the package, see https://github.com/ropensci/onboarding/issues/341), Maria Munafó [rev] (Maria Munafó reviewed the package, see https://github.com/ropensci/onboarding/issues/341), rOpenSci [fnd] (https://ropensci.org)
Maintainer: Scott Chamberlain <sckott@protonmail.com>
Repository: CRAN
Date/Publication: 2020-03-19 14:50:02 UTC

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New package mrbin with initial version 1.3.0
Package: mrbin
Title: Magnetic Resonance Binning, Integration and Normalization
Version: 1.3.0
Authors@R: person(given = "Matthias", family = "Klein", role = c("aut", "cre"), email = "klein.663@osu.edu", comment = c(ORCID = "0000-0001-7455-5381"))
Description: Nuclear Magnetic Resonance is widely used in Life Science research. The package (<http://www.kleinomicslab.com/software>) converts 1D or 2D data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, are replaced by positive values. All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time.
Imports: grDevices, graphics, stats, utils
Depends: R (>= 2.10)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2.9000
NeedsCompilation: no
Packaged: 2020-03-13 17:18:59 UTC; klein.663
Author: Matthias Klein [aut, cre] (<https://orcid.org/0000-0001-7455-5381>)
Maintainer: Matthias Klein <klein.663@osu.edu>
Repository: CRAN
Date/Publication: 2020-03-19 14:50:05 UTC

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New package elsa with initial version 1.1-28
Package: elsa
Type: Package
Title: Entropy-Based Local Indicator of Spatial Association
Version: 1.1-28
Date: 2020-03-13
Depends: methods, sp (>= 1.2-0), raster, R (>= 3.0.0)
Suggests: knitr, rmarkdown
Description: A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) <doi:10.1016/j.spasta.2018.10.001>.
License: GPL (>= 3)
VignetteBuilder: knitr
URL: http://r-gis.net
BugReports: https://github.com/babaknaimi/elsa/issues/
Authors@R: c( person("Babak", "Naimi", role = c("cre", "aut"), email = "naimi.b@gmail.com", comment = c(ORCID = "0000-0001-5431-2729")), person("Roger", "Bivand", role = "ctb", comment="part of the dnn C code, from the spdep package"), person("William", "Venables", role = "ctb", comment="part of the dnn C code, taken from the spdep package"), person("Brian", "Ripley", role = "ctb", comment="part of the dnn C code, taken from the spdep package") )
NeedsCompilation: yes
Packaged: 2020-03-12 22:57:04 UTC; naimi
Author: Babak Naimi [cre, aut] (<https://orcid.org/0000-0001-5431-2729>), Roger Bivand [ctb] (part of the dnn C code, from the spdep package), William Venables [ctb] (part of the dnn C code, taken from the spdep package), Brian Ripley [ctb] (part of the dnn C code, taken from the spdep package)
Maintainer: Babak Naimi <naimi.b@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-19 14:30:09 UTC

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New package CoxR2 with initial version 1.0
Package: CoxR2
Type: Package
Title: R-Squared Measure Based on Partial LR Statistic, for the Cox PH Regression Model
Version: 1.0
Date: 2020-02-28
Author: Hyeri You, Ronghui Xu
Maintainer: Hyeri You <h2you@health.ucsd.edu>
Description: Calculate the R-squared, aka explained randomness, based on the partial likelihood ratio statistic under the Cox Proportional Hazard model [J O'Quigley, R Xu, J Stare (2005) <doi:10.1002/sim.1946>].
Depends: survival, stats
License: GPL-2
NeedsCompilation: no
Packaged: 2020-03-10 16:26:41 UTC; h2you
Repository: CRAN
Date/Publication: 2020-03-19 14:10:08 UTC

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Package contrast updated to version 0.22 with previous version 0.21 dated 2016-09-22

Title: A Collection of Contrast Methods
Description: One degree of freedom contrasts for 'lm', 'glm', 'gls', and 'geese' objects.
Author: Alan O'Callaghan [aut, cre], Max Kuhn [aut], Steve Weston [aut], Jed Wing [aut], James Forester [aut], Thorn Thaler [aut]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>

Diff between contrast versions 0.21 dated 2016-09-22 and 0.22 dated 2020-03-19

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Package brglm2 updated to version 0.6.2 with previous version 0.6.1 dated 2020-02-06

Title: Bias Reduction in Generalized Linear Models
Description: Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2016) <arXiv:1604.04768>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <http://www.jstor.org/stable/2345592>. See Kosmidis et al (2019) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation). 'brglm2' also provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in binomial response generalized linear models.
Author: Ioannis Kosmidis [aut, cre] (<https://orcid.org/0000-0003-1556-0302>), Kjell Konis [ctb], Euloge Clovis Kenne Pagui [ctb], Nicola Sartori [ctb]
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>

Diff between brglm2 versions 0.6.1 dated 2020-02-06 and 0.6.2 dated 2020-03-19

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New package bahc with initial version 0.1.0
Package: bahc
Type: Package
Title: Filter Covariance and Correlation Matrices with Bootstrapped-Averaged Hierarchical Ansatz
Version: 0.1.0
Date: 2020-02-14
Author: Christian Bongiorno and Damien Challet
Maintainer: Damien Challet <damien.challet@gmail.com>
Description: A method to filter correlation and covariance matrices by averaging bootstrapped filtered hierarchical clustering. See Ch. Bongiorno and D. Challet, Covariance matrix filtering with bootstrapped hierarchies (2020) <arXiv:2003.05807>.
License: GPL
Depends: R (>= 3.5.0), fastcluster, matrixStats
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-03-13 05:35:03 UTC; damien
Repository: CRAN
Date/Publication: 2020-03-19 14:10:05 UTC

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New package whatr with initial version 1.0.0
Package: whatr
Title: Scrape and Analyze the J! Archive
Version: 1.0.0
Authors@R: person(given = "Kiernan", family = "Nicholls", role = c("aut", "cre"), email = "kiernann@protonmail.com")
Description: Scrape the fan-made J! Archive <https://www.j-archive.com/> for Jeopardy episode contestants, categories, clues, answers, and scores.
License: GPL-3
URL: https://github.com/kiernann/whatr
BugReports: https://github.com/kiernann/whatr/issues
Depends: R (>= 3.2)
Imports: dplyr (>= 0.8.3), ggplot2 (>= 3.2.1), httr (>= 1.4.1), magrittr (>= 1.5), methods, purrr (>= 0.3.3), rlang (>= 0.4.4), rvest (>= 0.3.4), scales (>= 1.0.0), stringr (>= 1.4.0), tibble (>= 2.1.3), tidyr (>= 1.0.0), xml2 (>= 1.2.2)
Suggests: covr (>= 3.3.2), spelling (>= 2.1), testthat (>= 2.3.0)
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-03-11 23:23:26 UTC; kiernan
Author: Kiernan Nicholls [aut, cre]
Maintainer: Kiernan Nicholls <kiernann@protonmail.com>
Repository: CRAN
Date/Publication: 2020-03-19 13:30:02 UTC

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New package TVMM with initial version 1.0
Package: TVMM
Type: Package
Title: Multivariate Tests for the Vector of Means
Version: 1.0
Authors@R: c( person("Henrique", "Jose de Paula Alves", email = "jpahenrique@gmail.com", role= c("aut", "cre")), person("Daniel", "Furtado Ferreira", email = "danielff@ufla.br", role= "aut"))
Author: Henrique Jose de Paula Alves [aut, cre], Daniel Furtado Ferreira [aut]
Maintainer: Henrique Jose de Paula Alves <jpahenrique@gmail.com>
Description: This is a statistical tool that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).
Depends: R (>= 3.1.0)
Imports: MASS, stats, robustbase
Encoding: UTF-8
License: GPL-2
LazyData: TRUE
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-03-19 12:46:23 UTC; Henrique
Repository: CRAN
Date/Publication: 2020-03-19 13:50:07 UTC

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Package SHT updated to version 0.1.3 with previous version 0.1.2 dated 2019-12-04

Title: Statistical Hypothesis Testing Toolbox
Description: We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.
Author: Kyoungjae Lee [aut], Lizhen Lin [aut], Hyungmin Park [aut], Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>)
Maintainer: Kisung You <kyoustat@gmail.com>

Diff between SHT versions 0.1.2 dated 2019-12-04 and 0.1.3 dated 2020-03-19

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New package sdafilter with initial version 1.0.0
Package: sdafilter
Title: Symmetrized Data Aggregation
Version: 1.0.0
Authors@R: c(person(given = "Lilun", family = "Du", role = c("aut", "cre"), email = "dulilun@ust.hk"), person(given = "Xu", family = "Guo", role = c("ctb")), person(given = "Wenguang", family = "Sun", role = c("ctb")), person(given = "Changliang", family = "Zou", role = c("ctb")) )
Author: Lilun Du [aut, cre], Xu Guo [ctb], Wenguang Sun [ctb], Changliang Zou [ctb]
Maintainer: Lilun Du <dulilun@ust.hk>
Description: We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2020), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <arXiv:2002.11992>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Imports: glmnet, glasso, huge, POET, stats
Suggests: testthat (>= 2.1.0)
Repository: CRAN
NeedsCompilation: no
Packaged: 2020-03-12 03:55:33 UTC; dulilun
Date/Publication: 2020-03-19 13:30:05 UTC

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Package RProtoBuf updated to version 0.4.16 with previous version 0.4.15 dated 2020-02-08

Title: R Interface to the 'Protocol Buffers' 'API' (Version 2 or 3)
Description: Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal 'RPC' protocols and file formats. Additional documentation is available in two included vignettes one of which corresponds to our 'JSS' paper (2016, <doi:10.18637/jss.v071.i02>. Either version 2 or 3 of the 'Protocol Buffers' 'API' is supported.
Author: Romain Francois, Dirk Eddelbuettel, Murray Stokely and Jeroen Ooms
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RProtoBuf versions 0.4.15 dated 2020-02-08 and 0.4.16 dated 2020-03-19

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Package move updated to version 3.3.0 with previous version 3.2.2 dated 2019-09-07

Title: Visualizing and Analyzing Animal Track Data
Description: Contains functions to access movement data stored in 'movebank.org' as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions.
Author: Bart Kranstauber [aut, cre], Marco Smolla [aut], Anne K Scharf [aut]
Maintainer: Bart Kranstauber <b.kranstauber@uva.nl>

Diff between move versions 3.2.2 dated 2019-09-07 and 3.3.0 dated 2020-03-19

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New package MCPModPack with initial version 0.1
Package: MCPModPack
Type: Package
Title: Simulation-Based Design and Analysis of Dose-Finding Trials
Version: 0.1
Date: 2020-02-24
Authors@R: c( person("Alex", "Dmitrienko", email = "admitrienko@medianainc.com", role = c("aut", "cre")), person("Alun", "Bedding", role = "ctb"), person("John", "Cook", role = "ctb"))
Depends: mvtnorm, R (>= 3.4.1)
Imports: Rcpp, RcppNumerical, officer, flextable, devEMF, shiny, shinydashboard
Suggests: testthat, devtools, DoseFinding
LinkingTo: Rcpp, RcppEigen, RcppNumerical
Maintainer: Alex Dmitrienko <admitrienko@medianainc.com>
Description: An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>).
License: GPL-3
LazyLoad: yes
LazyData: true
NeedsCompilation: yes
Packaged: 2020-03-11 00:30:53 UTC; victordmitrienko
Repository: CRAN
Author: Alex Dmitrienko [aut, cre], Alun Bedding [ctb], John Cook [ctb]
Date/Publication: 2020-03-19 13:20:12 UTC
URL: https://github.com/medianainc/MCPModPack
BugReports: https://github.com/medianainc/MCPModPack/issues

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Package ggeasy updated to version 0.1.2 with previous version 0.1.1 dated 2020-01-31

Title: Easy Access to 'ggplot2' Commands
Description: Provides a series of aliases to commonly used but difficult to remember 'ggplot2' sequences.
Author: Jonathan Carroll [aut, cre] (<https://orcid.org/0000-0002-1404-5264>), Alicia Schep [aut] (<https://orcid.org/0000-0002-3915-0618>), Jonathan Sidi [aut] (<https://orcid.org/0000-0002-4222-1819>), Bob Rudis [ctb] (<https://orcid.org/0000-0001-5670-2640>), Mohamed El Fodil Ihaddaden [ctb]
Maintainer: Jonathan Carroll <rpkg@jcarroll.com.au>

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Package dplR updated to version 1.7.1 with previous version 1.7.0 dated 2019-07-18

Title: Dendrochronology Program Library in R
Description: Perform tree-ring analyses such as detrending, chronology building, and cross dating. Read and write standard file formats used in dendrochronology.
Author: Andy Bunn [aut, cph, cre, trl], Mikko Korpela [aut, cph, trl], Franco Biondi [aut, cph], Filipe Campelo [aut, cph], Pierre Mérian [aut, cph], Fares Qeadan [aut, cph], Christian Zang [aut, cph], Allan Buras [ctb], Jacob Cecile [ctb], Manfred Mudelsee [ctb], Michael Schulz [ctb]
Maintainer: Andy Bunn <andy.bunn@wwu.edu>

Diff between dplR versions 1.7.0 dated 2019-07-18 and 1.7.1 dated 2020-03-19

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New package FDX with initial version 1.0.0
Package: FDX
Type: Package
Title: False Discovery Exceedance Controlling Multiple Testing Procedures
Version: 1.0.0
Date: 2020-03-12
Authors@R: c(person("Sebastian", "Döhler", role = "aut"), person("Florian","Junge", role = c("aut", "cre"), email = "florian.junge@h-da.de"), person("Etienne", "Roquain", role = "ctb"))
Maintainer: Florian Junge <florian.junge@h-da.de>
Description: Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2019) <arXiv:1912.04607v1>. The main algorithms of the paper are available as continuous, discrete and weighted versions.
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 3.00), DiscreteFDR
Imports: Rcpp (>= 1.0.3), methods, PoissonBinomial, pracma
LinkingTo: Rcpp, PoissonBinomial
RoxygenNote: 7.0.2
NeedsCompilation: yes
Packaged: 2020-03-12 07:36:41 UTC; Florian Junge
Author: Sebastian Döhler [aut], Florian Junge [aut, cre], Etienne Roquain [ctb]
Repository: CRAN
Date/Publication: 2020-03-19 13:30:11 UTC

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New package collapse with initial version 1.0.0
Package: collapse
Title: Advanced and Fast Data Transformation
Version: 1.0.0
Date: 2020-03-11
Authors@R: c( person("Sebastian", "Krantz", role = c("aut", "cre"), email = "sebastian.krantz@graduateinstitute.ch"), person("Matt", "Dowle", role = "ctb"), person("Arun", "Srinivasan", role = "ctb"), person("Simen", "Gaure", role = "ctb"), person("Dirk", "Eddelbuettel", role = "ctb"), person("R Core Team and contributors worldwide", role = "ctb"), person("Martyn", "Plummer", role = "cph"), person("1999-2016 The R Core Team", role = "cph") )
BugReports: https://github.com/SebKrantz/collapse/issues
Description: A C/C++ based package for advanced data transformation in R that is extremely fast, flexible and parsimonious to code with and programmer friendly. It is well integrated with 'dplyr', 'plm' and 'data.table'. --- Key Features: --- (1) Advanced data programming: A full set of fast statistical functions supporting grouped and/or weighted computations on vectors, matrices and data.frames. Fast (ordered) and reusable grouping, quick data conversions, and quick select, replace or add data.frame columns. (2) Advanced aggregation: Fast and easy multi-data-type, multi-function, weighted, parallelized and fully customized data aggregation. (3) Advanced transformations: Fast (grouped, weighted) replacing and sweeping out of statistics, scaling, centering, higher-dimensional centering, complex linear prediction and partialling-out. (4) Advanced time-computations: Fast (sequences of) lags / leads, and (lagged / leaded, iterated) differences and growth rates on (unordered) time-series and panel data. Multivariate auto, partial and cross- correlation functions for panel data. Panel data to (ts-)array conversions. (5) List Processing: (Recursive) list search / identification, extraction / subsetting, data-apply, and row-binding / unlisting in 2D. (6) Advanced data exploration: Fast (grouped, weighted, panel-decomposed) summary statistics for complex multilevel / panel data.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.1), lfe (>= 2.7)
LinkingTo: Rcpp
Suggests: dplyr, plm, data.table, ggplot2, scales, vars, knitr, rmarkdown, testthat, microbenchmark
SystemRequirements: C++11
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-03-12 17:41:33 UTC; Sebastian Krantz
Author: Sebastian Krantz [aut, cre], Matt Dowle [ctb], Arun Srinivasan [ctb], Simen Gaure [ctb], Dirk Eddelbuettel [ctb], R Core Team and contributors worldwide [ctb], Martyn Plummer [cph], 1999-2016 The R Core Team [cph]
Maintainer: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Repository: CRAN
Date/Publication: 2020-03-19 13:50:02 UTC

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New package aiRly with initial version 0.1.0
Package: aiRly
Type: Package
Title: R Wrapper for 'Airly' API
Version: 0.1.0
Authors@R: c( person(given = "Piotr", family = "Janus", role = c("cre","aut"), email = "piotr_janus@icloud.com") )
Maintainer: Piotr Janus <piotr_janus@icloud.com>
Description: Get information about air quality using 'Airly' <https://airly.eu/> API through R.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: utils, httr, jsonlite, reshape2, tibble
URL: https://github.com/piotrekjanus/aiRly
BugReports: https://github.com/piotrekjanus/aiRly/issues
Language: en-US
Suggests: testthat, httptest, covr
NeedsCompilation: no
Packaged: 2020-03-12 19:31:14 UTC; Emedium
Author: Piotr Janus [cre, aut]
Repository: CRAN
Date/Publication: 2020-03-19 14:00:02 UTC

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Package vdar updated to version 0.1.2-1 with previous version 0.1.1 dated 2020-03-10

Title: Discriminant Analysis Incorporating Individual Uncertainties
Description: The qda() function from package 'MASS' is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties. The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties. The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.
Author: Solveig Pospiech [aut, cre], K. Gerald van den Boogaart [ctb]
Maintainer: Solveig Pospiech <s.pospiech@hzdr.de>

Diff between vdar versions 0.1.1 dated 2020-03-10 and 0.1.2-1 dated 2020-03-19

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Package ursa updated to version 3.8.17 with previous version 3.8.16 dated 2020-02-28

Title: Non-Interactive Spatial Tools for Raster Processing and Visualization
Description: S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.
Author: Nikita Platonov [aut, cre] (<https://orcid.org/0000-0001-7196-7882>)
Maintainer: Nikita Platonov <platonov@sevin.ru>

Diff between ursa versions 3.8.16 dated 2020-02-28 and 3.8.17 dated 2020-03-19

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Package sociome updated to version 1.4.0 with previous version 1.3.2 dated 2020-01-27

Title: Operationalizing Social Determinants of Health Data for Researchers
Description: Accesses raw data via API and calculates social determinants of health measures for user-specified locations in the US, returning them in tidyverse- and sf-compatible data frames.
Author: Nik Krieger [aut, cre], Jarrod Dalton [aut], Cindy Wang [aut], Adam Perzynski [aut], National Institutes of Health/National Institute on Aging [fnd] (The development of this software package was supported by a research grant from the National Institutes of Health/National Institute on Aging, (Principal Investigators: Jarrod E. Dalton, PhD and Adam T. Perzynski, PhD; Grant Number: 5R01AG055480-02). All of its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.)
Maintainer: Nik Krieger <nk@case.edu>

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New package simplevis with initial version 1.0.0
Package: simplevis
Type: Package
Title: Simple Visualisation with 'ggplot2' and 'leaflet' Wrappers
Version: 1.0.0
Authors@R: c(person(given = "David", family = "Hodge", email = "davidhodge931@gmail.com", role = c("aut", "cre")), person("Statistics New Zealand", role = c("cph")))
Description: 'ggplot2' and 'leaflet' wrapper functions designed to simplify the creation of high quality graph and map visualisations. These functions only require inputs of data, variables and titles to provide beautiful interactive or image visualisations. However they allow for more flexibility if required. The intent is that high quality well-designed graphs and maps can be made more consistently with less effort, code and expertise than would otherwise be required.
License: MIT + file LICENSE
URL: https://StatisticsNZ.github.io/simplevis, https://github.com/StatisticsNZ/simplevis
BugReports: https://github.com/StatisticsNZ/simplevis/issues
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: dplyr (>= 0.8.0), DT, forcats, ggplot2 (>= 3.3.0), leaflet (>= 2.0.0), leafpop, leaflet.extras, janitor, lubridate, magrittr, methods, plotly, readr, rgdal, rmarkdown, rlang (>= 0.4.0), scales, sf (>= 0.8.0), shiny, shinycssloaders, stars (>= 0.4.0), stats, stringr (>= 1.4.0), tibble, tidyr (>= 1.0.0), viridis
Suggests: knitr
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-03-10 21:01:29 UTC; dhodge
Author: David Hodge [aut, cre], Statistics New Zealand [cph]
Maintainer: David Hodge <davidhodge931@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-19 13:00:02 UTC

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Package Radviz updated to version 0.9.0 with previous version 0.8.2 dated 2020-02-04

Title: Project Multidimensional Data in 2D Space
Description: An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Ankerst *et al.* (1996) (<http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.1811>) for original implementation, see Di Caro *et al* (2012) (<http://link.springer.com/chapter/10.1007/978-3-642-13672-6_13>) for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.
Author: Yann Abraham [aut, cre], Nicolas Sauwen [aut]
Maintainer: Yann Abraham <yann.abraham@gmail.com>

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Package pkgmaker updated to version 0.31.1 with previous version 0.31 dated 2020-01-22

Title: Development Utilities for R Packages
Description: Provides some low-level utilities to use for package development. It currently provides managers for multiple package specific options and registries, vignette, unit test and bibtex related utilities. It serves as a base package for packages like NMF, RcppOctave, doRNG, and as an incubator package for other general purposes utilities, that will eventually be packaged separately. It is still under heavy development and changes in the interface(s) are more than likely to happen.
Author: Renaud Gaujoux [aut, cre]
Maintainer: Renaud Gaujoux <renozao@protonmail.com>

Diff between pkgmaker versions 0.31 dated 2020-01-22 and 0.31.1 dated 2020-03-19

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Package pCalibrate updated to version 0.2-1 with previous version 0.1-1 dated 2017-11-06

Title: Bayesian Calibrations of p-Values
Description: Implements transformations of p-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2018, <doi:10.1146/annurev-statistics-031017-100307>). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test.
Author: Manuela Ott [aut, cre] (<https://orcid.org/0000-0002-8050-7279>), Leonhard Held [aut] (<https://orcid.org/0000-0002-8686-5325>)
Maintainer: Manuela Ott <manuela.c.ott@gmail.com>

Diff between pCalibrate versions 0.1-1 dated 2017-11-06 and 0.2-1 dated 2020-03-19

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Package opencastR updated to version 0.1.3 with previous version 0.1.2 dated 2019-11-04

Title: 'Opencast' External API Package
Description: Query the external API of a 'Opencast' video management server for information related to the recorded videos. For more information about 'Opencast' see: <https://opencast.org>.
Author: Daniel Ebbert [cre, aut] (<https://orcid.org/0000-0003-3666-7205>)
Maintainer: Daniel Ebbert <daniel@ebbert.nrw>

Diff between opencastR versions 0.1.2 dated 2019-11-04 and 0.1.3 dated 2020-03-19

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New package oenb with initial version 0.0.1
Package: oenb
Title: Tools for the OeNB Data Web Service
Version: 0.0.1
Authors@R: person("Franz X.", "Mohr", email = "franz.x.mohr@outlook.com", role = c("aut","cre"))
Description: Tools to access data from the data web service of the Oesterreichische Nationalbank (OeNB), <https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html>.
License: GPL (>= 2)
Depends: R (>= 3.2.0)
Imports: dplyr, XML
LazyData: true
RoxygenNote: 7.0.2
Encoding: UTF-8
URL: https://github.com/franzmohr/oenb
BugReports: https://github.com/franzmohr/oenb/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-03-10 21:37:47 UTC; franz
Author: Franz X. Mohr [aut, cre]
Maintainer: Franz X. Mohr <franz.x.mohr@outlook.com>
Repository: CRAN
Date/Publication: 2020-03-19 13:00:11 UTC

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New package netcontrol with initial version 0.1
Package: netcontrol
Type: Package
Title: Control Theory Methods for Networks
Version: 0.1
Date: 2020-02-11
Author: Teague R. Henry
Maintainer: Teague R. Henry <trhenry@email.unc.edu>
Description: Implementations of various control theory methods for use in brain and psychological networks. Contains controllability statistics from Pasqualetti, Zampieri & Bullo (2014) <doi:10.1109/TCNS.2014.2310254>, optimal control algorithms from Lewis, Vrabie & Syrmos (2012, ISBN:978-0-470-63349-6), and various utilities.
License: MIT + file LICENSE
Imports: Rcpp (>= 1.0.1), Rdpack, Matrix, MASS, pracma, expm
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.0.2
Encoding: UTF-8
RdMacros: Rdpack
NeedsCompilation: yes
Packaged: 2020-03-10 16:51:33 UTC; Teague
Repository: CRAN
Date/Publication: 2020-03-19 12:40:02 UTC

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Package neighbr updated to version 1.0.3 with previous version 1.0.2 dated 2019-03-15

Title: Classification, Regression, Clustering with K Nearest Neighbors
Description: Classification, regression, and clustering with k nearest neighbors algorithm. Implements several distance and similarity measures, covering continuous and logical features. Outputs ranked neighbors. Most features of this package are directly based on the PMML specification for KNN.
Author: Dmitriy Bolotov [aut, cre], Software AG [cph]
Maintainer: Dmitriy Bolotov <dmitriy.bolotov@softwareag.com>

Diff between neighbr versions 1.0.2 dated 2019-03-15 and 1.0.3 dated 2020-03-19

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New package list with initial version 9.2
Package: list
Version: 9.2
Title: Statistical Methods for the Item Count Technique and List Experiment
Authors@R: c(person("Graeme", "Blair", email = "graeme.blair@ucla.edu", role = c("aut", "cre")), person("Winston", "Chou", email = "wchou@princeton.edu", role = c("aut")), person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut")), person("Bethany", "Park", email = "bapark@princeton.edu", role = c("ctb")), person("Alexander", "Coppock", email = "alex.coppock@yale.edu", role = c("ctb")))
Depends: R (>= 3.2.0), utils, sandwich (>= 2.3-3)
Imports: VGAM (>= 0.9-8), magic (>= 1.5-6), gamlss.dist (>= 4.3-4), MASS (>= 7.3-40), quadprog (>= 1.5-5), corpcor (>= 1.6.7), mvtnorm (>= 1.0-2), coda (>= 0.17-1), stats, arm
Suggests: testthat (>= 0.9.1), knitr (>= 1.10.5)
VignetteBuilder: knitr
Description: Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, Blair and Imai (2012) <doi:10.1093/pan/mpr048>, Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>, Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 7.0.2
NeedsCompilation: yes
Packaged: 2020-03-18 05:24:23 UTC; gdblair
Author: Graeme Blair [aut, cre], Winston Chou [aut], Kosuke Imai [aut], Bethany Park [ctb], Alexander Coppock [ctb]
Maintainer: Graeme Blair <graeme.blair@ucla.edu>
Repository: CRAN
Date/Publication: 2020-03-19 13:00:14 UTC

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Package gimms updated to version 1.1.3 with previous version 1.1.1 dated 2018-12-07

Title: Download and Process GIMMS NDVI3g Data
Description: This is a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets from NASA Ames Ecological Forecasting Lab (ECOCAST); import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread 'raster' package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon 'Raster*' images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation.
Author: Florian Detsch [cre, aut]
Maintainer: Florian Detsch <fdetsch@web.de>

Diff between gimms versions 1.1.1 dated 2018-12-07 and 1.1.3 dated 2020-03-19

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New package ForestDisc with initial version 0.1.0
Package: ForestDisc
Type: Package
Title: Forest Discretization
Version: 0.1.0
Author: Haddouchi Maïssae
Maintainer: Haddouchi Maïssae <maissaem7@gmail.com>
Description: Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) <doi:10.1109/AICCSA.2009.5069327>, and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) <doi: 10.1287/mnsc.47.2.295.9834>.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: randomForest, nloptr, moments, stats
NeedsCompilation: no
Packaged: 2020-03-10 19:53:40 UTC; Maissae
Repository: CRAN
Date/Publication: 2020-03-19 13:00:21 UTC

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Package estimatr updated to version 0.22.0 with previous version 0.20.0 dated 2019-09-09

Title: Fast Estimators for Design-Based Inference
Description: Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.
Author: Graeme Blair [aut, cre], Jasper Cooper [aut], Alexander Coppock [aut], Macartan Humphreys [aut], Luke Sonnet [aut], Neal Fultz [ctb], Lily Medina [ctb], Russell Lenth [ctb]
Maintainer: Graeme Blair <graeme.blair@ucla.edu>

Diff between estimatr versions 0.20.0 dated 2019-09-09 and 0.22.0 dated 2020-03-19

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 26 files changed, 269 insertions(+), 86 deletions(-)

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New package errum with initial version 0.0.2
Package: errum
Title: Exploratory Reduced Reparameterized Unified Model Estimation
Version: 0.0.2
Authors@R: c( person("James Joseph", "Balamuta", email = "balamut2@illinois.edu", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0003-2826-8458")), person("Steven Andrew", "Culpepper", email = "sculpepp@illinois.edu", role = c("aut", "cph"), comment = c(ORCID = "0000-0003-4226-6176") ), person("Jeffrey A.", "Douglas", email = "jeffdoug@illinois.edu", role = c("aut")) )
Description: Perform a Bayesian estimation of the exploratory reduced reparameterized unified model (ErRUM) described by Culpepper and Chen (2018) <doi:10.3102/1076998618791306>.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp, RcppArmadillo (>= 0.9.200)
Suggests: simcdm
LazyData: true
RoxygenNote: 7.0.2
Encoding: UTF-8
Language: en-US
NeedsCompilation: yes
Packaged: 2020-03-10 16:57:46 UTC; ronin
Author: James Joseph Balamuta [aut, cre, cph] (<https://orcid.org/0000-0003-2826-8458>), Steven Andrew Culpepper [aut, cph] (<https://orcid.org/0000-0003-4226-6176>), Jeffrey A. Douglas [aut]
Maintainer: James Joseph Balamuta <balamut2@illinois.edu>
Repository: CRAN
Date/Publication: 2020-03-19 12:40:05 UTC

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New package edbuildr with initial version 0.1.1
Package: edbuildr
Type: Package
Title: Automated School District Data Download and Processing
Version: 0.1.1
Authors@R: c( person("Megan", "Brodzik", email = "megan@edbuild.org", role = c("cre", "aut")), person("Cecilia", "Depman", email = "cecilia@edbuild.org", role = "aut"), person("Sara", "Hodges", email = "sara@edbuild.org", role = "aut"), person("Kailey", "Spencer", email = "kailey.spencer.depoe@gmail.com", role = "ctb"))
Description: Import the 'EdBuild' master dataset of school district finance, student demographics, and community economic indicators for every school district in the United States. The master dataset is built from the US Census, Annual Survey of School System Finances (F33) and joins data from the National Center for Education Statistics, Common Core of Data; the US Census, Small Area Income and Poverty Estimates; and the US Census, Education Demographic and Geographic Estimates. We apply 'EdBuild' standard processing to the dataset and provide the option to select from four different exclusion criteria - see the masterpull() help file for more details. The master dataset is available for any school year 2013-2017 or longitudinally for 2013-2017. Additional functions in the package use 'EdBuild' master data to analyze the difference between neighboring school districts and create formatted excel tables of school district data. For full details about 'EdBuild' data processing please see 'EdBuild' (2019) <https://edbuild.org/content/dividing-lines/main/methodology>.
License: CC0
Imports: dplyr, magrittr, openxlsx, scales, stringr, tidyselect (>= 1.0.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2020-03-10 18:51:48 UTC; MeganBrodzik
Author: Megan Brodzik [cre, aut], Cecilia Depman [aut], Sara Hodges [aut], Kailey Spencer [ctb]
Maintainer: Megan Brodzik <megan@edbuild.org>
Repository: CRAN
Date/Publication: 2020-03-19 12:50:05 UTC

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New package DSSAT with initial version 0.0.1
Package: DSSAT
Type: Package
Title: A Comprehensive R Interface for the DSSAT Cropping Systems Model
Version: 0.0.1
Author: Phillip D. Alderman [aut, cre]
Maintainer: Phillip D. Alderman <phillip.alderman@okstate.edu>
Description: The purpose of this package is to provide a comprehensive R interface to the Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) documented by Jones et al (2003) <doi:10.1016/S1161-0301(02)00107-7>. The package provides cross-platform functions to read and write input files, run DSSAT-CSM, and read output files.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: utils, readr, stringr, dplyr, purrr, glue, tidyr, lubridate, rlang
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
BugReports: https://github.com/palderman/DSSAT/issues
NeedsCompilation: no
Packaged: 2020-03-10 17:13:05 UTC; palderman
Repository: CRAN
Date/Publication: 2020-03-19 12:40:08 UTC

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Package contact updated to version 1.2.1 with previous version 1.2.0 dated 2020-03-16

Title: Creating Contact and Social Networks
Description: Process spatially- and temporally-discrete data into contact and social networks, and facilitate network analysis by randomizing individuals' movement paths and/or related categorical variables. To use this package, users need only have a dataset containing spatial data (i.e., latitude/longitude, or planar x & y coordinates), individual IDs relating spatial data to specific individuals, and date/time information relating spatial locations to temporal locations. The functionality of this package ranges from data "cleaning" via multiple filtration functions, to spatial and temporal data interpolation, and network creation and analysis. Functions within this package are not limited to describing interpersonal contacts. Package functions can also identify and quantify "contacts" between individuals and fixed areas (e.g., home ranges, water bodies, buildings, etc.). As such, this package is an incredibly useful resource for facilitating epidemiological, ecological, ethological and sociological research.
Author: Trevor Farthing [aut, cre], Daniel Dawson [aut], Cristina Lanzas [ctb]
Maintainer: Trevor Farthing <tsfarthi@ncsu.edu>

Diff between contact versions 1.2.0 dated 2020-03-16 and 1.2.1 dated 2020-03-19

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 contact-1.2.1/contact/R/findDistThresh.R                             |    2 
 contact-1.2.1/contact/R/mps.R                                        |    2 
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 26 files changed, 78 insertions(+), 159 deletions(-)

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Package BeSS updated to version 1.0.8 with previous version 1.0.7 dated 2020-03-18

Title: Best Subset Selection in Linear, Logistic and CoxPH Models
Description: An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2017) <arXiv:1709.06254>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
Author: Canhong Wen [aut, cre], Aijun Zhang [aut], Shijie Quan [aut], Xueqin Wang [aut]
Maintainer: Canhong Wen <wencanhong@gmail.com>

Diff between BeSS versions 1.0.7 dated 2020-03-18 and 1.0.8 dated 2020-03-19

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Package tabula updated to version 1.5.1 with previous version 1.5.0 dated 2020-01-16

Title: Analysis, Seriation and Visualization of Archaeological Count Data
Description: An easy way to examine archaeological count data. This package provides a convenient and reproducible toolkit for relative and absolute dating and analysis of (chronological) patterns. It includes functions for matrix seriation (reciprocal ranking, CA-based seriation), chronological modeling and dating of archaeological assemblages and/or objects. Beyond these, the package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). The package make it easy to visualize count data and statistical thresholds: rank vs. abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams.
Author: Nicolas Frerebeau [aut, cre] (<https://orcid.org/0000-0001-5759-4944>), Brice Lebrun [ctb] (<https://orcid.org/0000-0001-7503-8685>), Matthew Peeples [ctb] (<https://orcid.org/0000-0003-4496-623X>), Ben Marwick [ctb] (<https://orcid.org/0000-0001-7879-4531>), Anne Philippe [ctb], Jean-Baptiste Fourvel [ctb] (<https://orcid.org/0000-0002-1061-4642>)
Maintainer: Nicolas Frerebeau <nicolas.frerebeau@u-bordeaux-montaigne.fr>

Diff between tabula versions 1.5.0 dated 2020-01-16 and 1.5.1 dated 2020-03-19

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Package LDATS updated to version 0.2.7 with previous version 0.2.6 dated 2020-03-03

Title: Latent Dirichlet Allocation Coupled with Time Series Analyses
Description: Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.
Author: Juniper L. Simonis [aut, cre] (<https://orcid.org/0000-0001-9798-0460>), Erica M. Christensen [aut] (<https://orcid.org/0000-0002-5635-2502>), David J. Harris [aut] (<https://orcid.org/0000-0003-3332-9307>), Renata M. Diaz [aut] (<https://orcid.org/0000-0003-0803-4734>), Hao Ye [aut] (<https://orcid.org/0000-0002-8630-1458>), Ethan P. White [aut] (<https://orcid.org/0000-0001-6728-7745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000-0002-6026-8530>), Weecology [cph]
Maintainer: Juniper L. Simonis <juniper.simonis@weecology.org>

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Package Hmsc updated to version 3.0-6 with previous version 3.0-4 dated 2019-12-16

Title: Hierarchical Model of Species Communities
Description: Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data. This package implements it in the Bayesian framework with Gibbs Markov chain Monte Carlo (MCMC) sampling.
Author: Gleb Tikhonov [aut], Otso Ovaskainen [aut, cre] (<https://orcid.org/0000-0001-9750-4421>), Jari Oksanen [aut], Melinda de Jonge [aut], Oystein Opedal [aut], Tad Dallas [aut]
Maintainer: Otso Ovaskainen <otso.ovaskainen@helsinki.fi>

Diff between Hmsc versions 3.0-4 dated 2019-12-16 and 3.0-6 dated 2020-03-19

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Package BayesSenMC (with last version 0.1.1) was removed from CRAN

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

2019-04-15 0.1.1
2019-04-08 0.1.0

Permanent link
Package UniprotR updated to version 1.2.4 with previous version 1.2.3 dated 2020-01-21

Title: Retrieving Information of Proteins from Uniprot
Description: Connect to Uniprot <https://www.uniprot.org/> to retrieve information about proteins using their accession number such information could be name or taxonomy information, For detailed information kindly read the publication <https://www.sciencedirect.com/science/article/pii/S1874391919303859>.
Author: Mohamed Soudy [aut, cre], Ali Mostafa [aut]
Maintainer: Mohamed Soudy <MohmedSoudy2009@gmail.com>

Diff between UniprotR versions 1.2.3 dated 2020-01-21 and 1.2.4 dated 2020-03-19

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Package RKEEL updated to version 1.3.2 with previous version 1.3.1 dated 2019-07-18

Title: Using KEEL in R Code
Description: 'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. This package takes the advantages of 'KEEL' and R, allowing to use 'KEEL' algorithms in simple R code. The implemented R code layer between R and 'KEEL' makes easy both using 'KEEL' algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way. It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process. For more information about KEEL, see <http://www.keel.es/>.
Author: Jose M. Moyano [aut, cre], Luciano Sanchez [aut], Oliver Sanchez [ctb], Jesus Alcala-Fernandez [ctb]
Maintainer: Jose M. Moyano <jmoyano@uco.es>

Diff between RKEEL versions 1.3.1 dated 2019-07-18 and 1.3.2 dated 2020-03-19

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Package igraph updated to version 1.2.5 with previous version 1.2.4.2 dated 2019-11-27

Title: Network Analysis and Visualization
Description: Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.
Author: See AUTHORS file.
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

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 igraph-1.2.5/igraph/man/igraph-vs-attributes.Rd                   |   47 
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 igraph-1.2.5/igraph/man/layout_with_gem.Rd                        |   52 
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 igraph-1.2.5/igraph/man/make_clusters.Rd                          |    9 
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 igraph-1.2.5/igraph/man/path.Rd                                   |   14 
 igraph-1.2.5/igraph/man/plot.igraph.Rd                            |   14 
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 igraph-1.2.5/igraph/man/plot_dendrogram.igraphHRG.Rd              |    3 
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 igraph-1.2.5/igraph/src/dgetv0.f                                  |    6 
 igraph-1.2.5/igraph/src/dnaitr.f                                  |    8 
 igraph-1.2.5/igraph/src/dnapps.f                                  |   16 
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 igraph-1.2.5/igraph/src/foreign-dl-lexer.c                        |  943 ++-
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 igraph-1.2.5/igraph/src/foreign-lgl-parser.h                      |   87 
 igraph-1.2.5/igraph/src/foreign-ncol-lexer.c                      |  906 ++-
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 igraph-1.2.5/igraph/src/foreign-ncol-parser.h                     |   84 
 igraph-1.2.5/igraph/src/foreign-pajek-lexer.c                     |  919 ++-
 igraph-1.2.5/igraph/src/foreign-pajek-lexer.l                     |   10 
 igraph-1.2.5/igraph/src/foreign-pajek-parser.c                    | 2523 +++++-----
 igraph-1.2.5/igraph/src/foreign-pajek-parser.h                    |  222 
 igraph-1.2.5/igraph/src/igraph_arpack_internal.h                  |   36 
 igraph-1.2.5/igraph/src/igraph_blas_internal.h                    |   28 
 igraph-1.2.5/igraph/src/igraph_version.h                          |    2 
 igraph-1.2.5/igraph/src/lsap.c                                    |    4 
 igraph-1.2.5/igraph/src/plfit/plfit.c                             |    2 
 igraph-1.2.5/igraph/src/sparsemat.c                               |    2 
 igraph-1.2.5/igraph/src/uuid/gen_uuid.c                           |    6 
 igraph-1.2.5/igraph/src/wrap.f                                    |only
 igraph-1.2.5/igraph/tests/testthat.R                              |    1 
 igraph-1.2.5/igraph/tests/testthat/helper.R                       |   20 
 igraph-1.2.5/igraph/tests/testthat/test-vs-es-printing.R          |    4 
 igraph-1.2.5/igraph/tests/testthat/test_bipartite.projection.R    |    1 
 igraph-1.2.5/igraph/tests/testthat/test_contract.vertices.R       |    1 
 igraph-1.2.5/igraph/tests/testthat/test_graph.adjacency.R         |    5 
 igraph-1.2.5/igraph/tests/testthat/test_graph.subisomorphic.lad.R |    1 
 igraph-1.2.5/igraph/tests/testthat/test_igraph.options.R          |    2 
 igraph-1.2.5/igraph/tests/testthat/test_layout.fr.R               |   18 
 igraph-1.2.5/igraph/tests/testthat/test_optimal.community.R       |    1 
 igraph-1.2.5/igraph/tests/testthat/test_scan.R                    |    1 
 igraph-1.2.5/igraph/tests/testthat/test_sgm.R                     |    3 
 igraph-1.2.5/igraph/tests/testthat/test_sir.R                     |   10 
 312 files changed, 11754 insertions(+), 8946 deletions(-)

More information about igraph at CRAN
Permanent link

Package IsoplotR updated to version 3.3 with previous version 3.2 dated 2020-01-23

Title: Statistical Toolbox for Radiometric Geochronology
Description: Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington' and 'Ludwig' approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots.
Author: Pieter Vermeesch [aut, cre]
Maintainer: Pieter Vermeesch <p.vermeesch@ucl.ac.uk>

Diff between IsoplotR versions 3.2 dated 2020-01-23 and 3.3 dated 2020-03-19

 IsoplotR-3.2/IsoplotR/inst/UPb8.csv       |only
 IsoplotR-3.3/IsoplotR/DESCRIPTION         |   10 -
 IsoplotR-3.3/IsoplotR/MD5                 |   87 +++++----
 IsoplotR-3.3/IsoplotR/NAMESPACE           |   12 +
 IsoplotR-3.3/IsoplotR/R/ArAr.R            |   77 --------
 IsoplotR-3.3/IsoplotR/R/IsoplotR.R        |    2 
 IsoplotR-3.3/IsoplotR/R/PbPb.R            |  108 ------------
 IsoplotR-3.3/IsoplotR/R/ThPb.R            |only
 IsoplotR-3.3/IsoplotR/R/UPb.R             |  102 +++--------
 IsoplotR-3.3/IsoplotR/R/UThPb.R           |only
 IsoplotR-3.3/IsoplotR/R/age.R             |   59 +++---
 IsoplotR-3.3/IsoplotR/R/cad.R             |   34 ++-
 IsoplotR-3.3/IsoplotR/R/commonPb.R        |  268 +++++++++++-------------------
 IsoplotR-3.3/IsoplotR/R/concordia.R       |   26 +-
 IsoplotR-3.3/IsoplotR/R/constants.R       |    6 
 IsoplotR-3.3/IsoplotR/R/discordia.R       |   76 ++++----
 IsoplotR-3.3/IsoplotR/R/documentation.R   |    7 
 IsoplotR-3.3/IsoplotR/R/io.R              |  157 ++++++++++-------
 IsoplotR-3.3/IsoplotR/R/isochron.R        |  197 ++++++++++++++--------
 IsoplotR-3.3/IsoplotR/R/kde.R             |   33 ++-
 IsoplotR-3.3/IsoplotR/R/ludwig.R          |  120 +++++++------
 IsoplotR-3.3/IsoplotR/R/peakfit.R         |   35 ++-
 IsoplotR-3.3/IsoplotR/R/radialplot.R      |   32 ++-
 IsoplotR-3.3/IsoplotR/R/tests.R           |only
 IsoplotR-3.3/IsoplotR/R/toolbox.R         |    4 
 IsoplotR-3.3/IsoplotR/R/weightedmean.R    |   29 ++-
 IsoplotR-3.3/IsoplotR/R/york.R            |  167 ++++++++++--------
 IsoplotR-3.3/IsoplotR/data/examples.rda   |binary
 IsoplotR-3.3/IsoplotR/inst/PbPb2.csv      |   28 +--
 IsoplotR-3.3/IsoplotR/inst/PbPb3.csv      |   38 ++--
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 IsoplotR-3.3/IsoplotR/inst/UPb7.csv       |   32 ++-
 IsoplotR-3.3/IsoplotR/man/IsoplotR.Rd     |    2 
 IsoplotR-3.3/IsoplotR/man/age.Rd          |   41 ++--
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 IsoplotR-3.3/IsoplotR/man/data2york.Rd    |  134 +++++++--------
 IsoplotR-3.3/IsoplotR/man/examples.Rd     |    7 
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 IsoplotR-3.3/IsoplotR/man/peakfit.Rd      |   29 +--
 IsoplotR-3.3/IsoplotR/man/radialplot.Rd   |   24 +-
 IsoplotR-3.3/IsoplotR/man/read.data.Rd    |   44 ++--
 IsoplotR-3.3/IsoplotR/man/settings.Rd     |    6 
 IsoplotR-3.3/IsoplotR/man/weightedmean.Rd |   19 +-
 48 files changed, 1142 insertions(+), 1109 deletions(-)

More information about IsoplotR at CRAN
Permanent link

Package graph4lg updated to version 0.3.0 with previous version 0.2.0 dated 2019-12-12

Title: Build Graphs for Landscape Genetics Analysis
Description: Build graphs for landscape genetics analysis. This set of functions can be used to import and convert spatial and genetic data initially in different formats, import landscape graphs created with 'GRAPHAB' software (Foltete et al., 2012) <doi:10.1016/j.envsoft.2012.07.002>, make diagnosis plots of isolation by distance relationships in order to choose how to build genetic graphs, create graphs with a large range of pruning methods, weight their links with several genetic distances, plot and analyse graphs, compare them with other graphs. It uses functions from other packages such as 'adegenet' (Jombart, 2008) <doi:10.1093/bioinformatics/btn129> and 'igraph' (Csardi et Nepusz, 2006) <https://bit.ly/35a3V3H>. It also implements methods commonly used in landscape genetics to create graphs, described by Dyer et Nason (2004) <doi:10.1111/j.1365-294X.2004.02177.x> and Greenbaum et Fefferman (2017) <doi:10.1111/mec.14059>, and to analyse distance data (van Strien et al., 2015) <doi:10.1038/hdy.2014.62>.
Author: Paul Savary
Maintainer: Paul Savary <savarypaul660@gmail.com>

Diff between graph4lg versions 0.2.0 dated 2019-12-12 and 0.3.0 dated 2020-03-19

 DESCRIPTION                     |    6 
 MD5                             |   34 
 NEWS.md                         |    5 
 R/aux_25_09.R                   |    3 
 R/genind_to_genepop.R           |   10 
 R/graph_plan.R                  |   21 
 R/mat_gen_dist.R                |   16 
 R/mat_pw_d_jost.R               |    2 
 R/mat_pw_fst.R                  |    2 
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 R/plot_graph_lg.R               |    2 
 build/partial.rdb               |binary
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Package augmentedRCBD (with last version 0.1.1) was removed from CRAN

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

2019-07-21 0.1.1

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Package bioRad (with last version 0.5.0) was removed from CRAN

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

2020-02-19 0.5.0
2018-12-14 0.4.0

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Package gwsem (with last version 0.1.14) was removed from CRAN

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

2020-02-17 0.1.14
2020-02-17 0.1.15

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Package DEVis (with last version 1.0.1) was removed from CRAN

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2019-01-25 1.0.1

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Package ggroups updated to version 2.0.3 with previous version 2.0.2 dated 2020-02-28

Title: Pedigree and Genetic Groups
Description: Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
Author: Mohammad Ali Nilforooshan [aut, cre] (<https://orcid.org/0000-0003-0339-5442>)
Maintainer: Mohammad Ali Nilforooshan <m.a.nilforooshan@gmail.com>

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Package PythonInR updated to version 0.1-11 with previous version 0.1-9 dated 2020-03-10

Title: Use 'Python' from Within 'R'
Description: Interact with 'Python' <https://www.python.org/> from within 'R'.
Author: Florian Schwendinger [aut, cre], Kimyen Truong [ctb]
Maintainer: Florian Schwendinger <FlorianSchwendinger@gmx.at>

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Package n1qn1 updated to version 6.0.1-7 with previous version 6.0.1-6 dated 2020-03-02

Title: Port of the 'Scilab' 'n1qn1' and 'qnbd' Modules for (Un)constrained BFGS Optimization
Description: Provides 'Scilab' 'n1qn1', or Quasi-Newton BFGS "qn" without constraints and 'qnbd' or Quasi-Newton BFGS with constraints. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. Both algorithms are described in the 'Scilab' optimization documentation located at <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>.
Author: Matthew Fidler [aut, cre], Wenping Wang [aut], Claude Lemarechal [aut, ctb], Joseph Bonnans [ctb], Jean-Charles Gilbert [ctb], Claudia Sagastizabal [ctb], Stephen L. Campbell, [ctb], Jean-Philippe Chancelier [ctb], Ramine Nikoukhah [ctb], Dirk Eddelbuettel [ctb], Bruno Jofret [ctb], INRIA [cph]
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>

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Package BisqueRNA updated to version 1.0.2 with previous version 1.0.1 dated 2019-12-15

Title: Decomposition of Bulk Expression with Single-Cell Sequencing
Description: Provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples. For more details, see Jew and Alvarez et al (2019) <doi:10.1101/669911>.
Author: Brandon Jew [aut, cre], Marcus Alvarez [aut]
Maintainer: Brandon Jew <brandon.jew@ucla.edu>

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Package rsubgroup (with last version 0.9) was removed from CRAN

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

2020-03-04 0.9
2014-09-11 0.6
2014-08-06 0.5

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