Sun, 23 Feb 2020

Package SSN updated to version 1.1.14 with previous version 1.1.13 dated 2018-05-12

Title: Spatial Modeling on Stream Networks
Description: Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., 2010. <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.
Author: Jay Ver Hoef [aut, cre], Erin Peterson [aut]
Maintainer: Jay Ver Hoef <ver.hoef@SpatialStreamNetworks.com>

Diff between SSN versions 1.1.13 dated 2018-05-12 and 1.1.14 dated 2020-02-23

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Package PMCMRplus updated to version 1.4.3 with previous version 1.4.2 dated 2019-10-21

Title: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended
Description: For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons tests (Tukey-Kramer test, Scheffe test, LSD-test) and many-to-one tests (Dunnett test) for normally distributed residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane's T2 test, Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals and heterogeneous variances are provided. Van der Waerden's normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks. Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included. In addition, a Friedman-test for one-way ANOVA with repeated measures on ranks (CRBD) and Skillings-Mack test for unbalanced CRBD is provided with consequent all-pairs tests (Nemenyi test, Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test). A trend can be tested with Pages's test. Durbin's test for a two-way balanced incomplete block design (BIBD) is given in this package as well as Gore's test for CRBD with multiple observations per cell is given. Outlier tests, Mandel's k- and h statistic as well as functions for Type I error and Power analysis as well as generic summary, print and plot methods are provided.
Author: Thorsten Pohlert [aut, cre] (<https://orcid.org/0000-0003-3855-3025>)
Maintainer: Thorsten Pohlert <thorsten.pohlert@gmx.de>

Diff between PMCMRplus versions 1.4.2 dated 2019-10-21 and 1.4.3 dated 2020-02-23

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Package mlr3 updated to version 0.1.7 with previous version 0.1.6 dated 2019-12-19

Title: Machine Learning in R - Next Generation
Description: Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>), Giuseppe Casalicchio [ctb] (<https://orcid.org/0000-0001-5324-5966>), Stefan Coors [ctb] (<https://orcid.org/0000-0002-7465-2146>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [aut]
Maintainer: Michel Lang <michellang@gmail.com>

Diff between mlr3 versions 0.1.6 dated 2019-12-19 and 0.1.7 dated 2020-02-23

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Package genepop updated to version 1.1.7 with previous version 1.1.4 dated 2019-12-06

Title: Population Genetic Data Analysis Using Genepop
Description: Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
Author: François Rousset [aut, cre, cph] (<https://orcid.org/0000-0003-4670-0371>), Jimmy Lopez [ctb], Khalid Belkhir [ctb]
Maintainer: François Rousset <francois.rousset@umontpellier.fr>

Diff between genepop versions 1.1.4 dated 2019-12-06 and 1.1.7 dated 2020-02-23

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 inst/doc/GenepopS.tex                                                            |    8 
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 inst/doc/bibliography.html                                                       |   10 
 inst/doc/code-maintenance-credits-contact-etc-.html                              |   10 
 inst/doc/copyright.html                                                          |   10 
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 inst/doc/methods.html                                                            |   12 
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 src/F_est.cpp                                                                    |    2 
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 src/genepop.cpp                                                                  |   10 
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 tests/testthat/tests_HW.R                                                        |    5 
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Package dscore updated to version 1.1.0 with previous version 1.0.0 dated 2020-01-18

Title: D-Score for Child Development
Description: The D-score is a quantitative measure of child development. The D-score follows the Rasch model. See Jacobusse, van Buuren and Verkerk (2006) <doi:10.1002/sim.2351>. The user can convert milestone scores from 19 assessment instruments into the D-score and the DAZ (D-score adjusted for age). Several tools assist in mapping milestone names into the 9-position Global Scale of Early Development (GSED) convention. Supports calculation of the D-score using 'dutch' <doi:10.1177/0962280212473300>, 'gcdg' <doi:10.1136/bmjgh-2019-001724> and 'gsed' conversion keys. The user can calculate DAZ using 'dutch' and 'gcdg' age-conditional references.
Author: Stef van Buuren [cre, aut], Iris Eekhout [aut], Arjan Huizing [aut]
Maintainer: Stef van Buuren <stef.vanbuuren@tno.nl>

Diff between dscore versions 1.0.0 dated 2020-01-18 and 1.1.0 dated 2020-02-23

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Package paws.common updated to version 0.2.6 with previous version 0.2.5 dated 2020-01-11

Title: Paws Low-Level Amazon Web Services API
Description: Functions for making low-level API requests to Amazon Web Services <https://aws.amazon.com>. The functions handle building, signing, and sending requests, and receiving responses. They are designed to help build higher-level interfaces to individual services, such as Simple Storage Service (S3).
Author: David Kretch [aut, cre], Adam Banker [aut], Amazon.com, Inc. [cph]
Maintainer: David Kretch <david.kretch@gmail.com>

Diff between paws.common versions 0.2.5 dated 2020-01-11 and 0.2.6 dated 2020-02-23

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Package gameofthrones updated to version 1.0.2 with previous version 1.0.0 dated 2018-12-30

Title: Palettes Inspired in the TV Show "Game of Thrones"
Description: Implementation of the characteristic palettes from the TV show 'Game of Thrones'.
Author: Alejandro Jimenez Rico [aut, cre]
Maintainer: Alejandro Jimenez Rico <aljrico@gmail.com>

Diff between gameofthrones versions 1.0.0 dated 2018-12-30 and 1.0.2 dated 2020-02-23

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New package coronavirus with initial version 0.1.0
Package: coronavirus
Title: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset
Version: 0.1.0
Authors@R: person("Rami", "Krispin", email = "rami.krispin@gmail.com", role = c("aut", "cre"))
Maintainer: Rami Krispin <rami.krispin@gmail.com>
Description: Provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus <https://systems.jhu.edu/research/public-health/ncov/>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0.2)
Suggests: dplyr, knitr, rmarkdown, devtools, remotes, testthat (>= 2.1.0)
URL: https://github.com/RamiKrispin/coronavirus
BugReports: https://github.com/RamiKrispin/coronavirus/issues
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-02-17 05:13:54 UTC; ramikrispin
Author: Rami Krispin [aut, cre]
Repository: CRAN
Date/Publication: 2020-02-23 19:10:02 UTC

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New package blockCV with initial version 2.1.1
Package: blockCV
Type: Package
Title: Spatial and Environmental Blocking for K-Fold Cross-Validation
Version: 2.1.1
Date: 2020-02-16
Authors@R: c(person("Roozbeh", "Valavi", role = c("aut", "cre"), email = "valavi.r@gmail.com"), person("Jane", "Elith", role = "aut", email = "j.elith@unimelb.edu.au"), person("José", "Lahoz-Monfort", role = "aut", email = "jose.lahoz@unimelb.edu.au"), person("Gurutzeta", "Guillera-Arroita", role = "aut", email = "gurutzeta.guillera@unimelb.edu.au"))
URL: https://github.com/rvalavi/blockCV
Maintainer: Roozbeh Valavi <valavi.r@gmail.com>
Description: Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: raster (>= 2.5-8), sf (>= 0.8-0), progress
RoxygenNote: 7.0.2
Suggests: knitr, ggplot2 (>= 3.2.1), cowplot, automap (>= 1.0-14), rgeos, future, future.apply, shiny (>= 1.0.3), shinydashboard, geosphere, methods, rmarkdown, testthat, covr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-17 11:54:41 UTC; rvalavi
Author: Roozbeh Valavi [aut, cre], Jane Elith [aut], José Lahoz-Monfort [aut], Gurutzeta Guillera-Arroita [aut]
Repository: CRAN
Date/Publication: 2020-02-23 20:00:02 UTC

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New package tidyHeatmap with initial version 0.99.9
Package: tidyHeatmap
Type: Package
Title: A Tidy Implementation of Heatmap
Version: 0.99.9
Authors@R: person("Stefano", "Mangiola", email = "mangiolastefano@gmail.com", role = c("aut", "cre"))
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
Description: This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes.
License: GPL-3
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 7.0.2
biocViews: AssayDomain, Infrastructure
Biarch: true
Depends: R (>= 3.6), ComplexHeatmap
Imports: dplyr, magrittr, tidyr, rlang, purrr, stats, viridis, circlize, RColorBrewer, grid, grDevices, utils
Suggests: testthat, BiocManager, knitr, qpdf, covr
URL: https://www.r-project.org, https://github.com/stemangiola/tidyHeatmap
BugReports: https://github.com/stemangiola/tidyHeatmap
NeedsCompilation: no
Packaged: 2020-02-17 00:23:35 UTC; mangiola.s
Author: Stefano Mangiola [aut, cre]
Repository: CRAN
Date/Publication: 2020-02-23 19:00:02 UTC

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New package RTL with initial version 0.1.1
Package: RTL
Type: Package
Title: Risk Tool Library
Version: 0.1.1
Date: 2020-02-16
Authors@R: c(person("Philippe", "Cote", email = "pcote@ualberta.ca", role = c("aut", "cre")),person("Nima", "Safaian", email = "nima.safaian@gmail.com", role = c("aut")))
Description: Collection of functions and metadata to complement core packages in Finance and Commodities, including futures expiry tables and <http://www.morningstarcommodity.com/> API functions. See <https://github.com/risktoollib/RTL>.
Depends: R (>= 3.6)
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
URL: https://github.com/risktoollib/RTL
Suggests: testthat (>= 2.1.0)
RoxygenNote: 7.0.2
Imports: zoo, xts, stats, magrittr, tibble, dplyr, tidyr, ggplot2, httr, stringr, purrr, lubridate, EIAdata, timetk, PerformanceAnalytics, tibbletime, quantmod, forecast, tidyquant, readr, Quandl, fGarch, fitdistrplus, tsibble, feasts, plotly, fabletools
NeedsCompilation: no
Packaged: 2020-02-17 00:02:50 UTC; coteph
Author: Philippe Cote [aut, cre], Nima Safaian [aut]
Maintainer: Philippe Cote <pcote@ualberta.ca>
Repository: CRAN
Date/Publication: 2020-02-23 18:50:02 UTC

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Package rpf updated to version 1.0.3 with previous version 1.0.2 dated 2020-02-19

Title: Response Probability Functions
Description: The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions are made available with R_RegisterCCallable().
Author: Joshua Pritikin [cre, aut], Jonathan Weeks [ctb], Li Cai [ctb], Carrie Houts [ctb], Phil Chalmers [ctb], Michael D. Hunter [ctb], Carl F. Falk [ctb]
Maintainer: Joshua Pritikin <jpritikin@pobox.com>

Diff between rpf versions 1.0.2 dated 2020-02-19 and 1.0.3 dated 2020-02-23

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Package rfPermute updated to version 2.1.81 with previous version 2.1.7 dated 2019-04-22

Title: Estimate Permutation p-Values for Random Forest Importance Metrics
Description: Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for 'randomForest' results.
Author: Eric Archer [aut, cre]
Maintainer: Eric Archer <eric.archer@noaa.gov>

Diff between rfPermute versions 2.1.7 dated 2019-04-22 and 2.1.81 dated 2020-02-23

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New package PCMBaseCpp with initial version 0.1.8
Package: PCMBaseCpp
Type: Package
Title: Fast Likelihood Calculation for Phylogenetic Comparative Models
Version: 0.1.8
Authors@R: c(person("Venelin", "Mitov", email = "vmitov@gmail.com", comment = '<a href="https://venelin.github.io">venelin.github.io</a>', role = c("aut", "cre", "cph")))
Maintainer: Venelin Mitov <vmitov@gmail.com>
Description: Provides a C++ backend for multivariate phylogenetic comparative models implemented in the R-package 'PCMBase'. Can be used in combination with 'PCMBase' to enable fast and parallel likelihood calculation. Implements the pruning likelihood calculation algorithm described in Mitov et al. (2018) <arXiv:1809.09014>. Uses the 'SPLITT' C++ library for parallel tree traversal described in Mitov and Stadler (2018) <doi:10.1111/2041-210X.13136>.
Encoding: UTF-8
License: GPL (>= 3.0)
LazyData: true
Depends: R (>= 3.1.0), Rcpp, methods
Imports: PCMBase, data.table, abind
Suggests: testthat, knitr, rmarkdown, covr
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.1.1
ByteCompile: yes
NeedsCompilation: yes
URL: https://github.com/venelin/PCMBaseCpp, https://venelin.github.io
BugReports: https://github.com/venelin/PCMBaseCpp/issues
Repository: CRAN
VignetteBuilder: knitr, rmarkdown
Packaged: 2020-02-23 17:27:31 UTC; vmitov
Author: Venelin Mitov [aut, cre, cph] (<a href="https://venelin.github.io">venelin.github.io</a>)
Date/Publication: 2020-02-23 18:30:06 UTC

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Package summariser updated to version 2.0.0 with previous version 0.1.0 dated 2017-03-23

Title: Easy Calculation and Visualisation of Confidence Intervals
Description: Functions to speed up the exploratory analysis of simple datasets using 'dplyr'. Functions are provided to do the common tasks of calculating confidence intervals.
Author: Conor Neilson [aut, cre]
Maintainer: Conor Neilson <condwanaland@gmail.com>

Diff between summariser versions 0.1.0 dated 2017-03-23 and 2.0.0 dated 2020-02-23

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Package subplex updated to version 1.6 with previous version 1.5-4 dated 2018-04-05

Title: Unconstrained Optimization using the Subplex Algorithm
Description: The subplex algorithm for unconstrained optimization, developed by Tom Rowan <http://www.netlib.org/opt/subplex.tgz>.
Author: Aaron A. King [aut, trl, cre], Tom Rowan [aut]
Maintainer: Aaron A. King <kingaa@umich.edu>

Diff between subplex versions 1.5-4 dated 2018-04-05 and 1.6 dated 2020-02-23

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Package spatstat updated to version 1.63-2 with previous version 1.63-0 dated 2020-01-23

Title: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Description: Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Author: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>, Rolf Turner <r.turner@auckland.ac.nz> and Ege Rubak <rubak@math.aau.dk>, with substantial contributions of code by Kasper Klitgaard Berthelsen; Ottmar Cronie; Tilman Davies; Yongtao Guan; Ute Hahn; Abdollah Jalilian; Marie-Colette van Lieshout; Greg McSwiggan; Tuomas Rajala; Suman Rakshit; Dominic Schuhmacher; Rasmus Waagepetersen; and Hangsheng Wang. Additional contributions by M. Adepeju; C. Anderson; Q.W. Ang; R. Arellano; J. Astrom; M. Austenfeld; S. Azaele; M. Baddeley; C. Beale; M. Bell; R. Bernhardt; T. Bendtsen; A. Bevan; B. Biggerstaff; A. Bilgrau; L. Bischof; C. Biscio; R. Bivand; J.M. Blanco Moreno; F. Bonneu; J. Brown; J. Burgos; S. Byers; Y.M. Chang; J.B. Chen; I. Chernayavsky; Y.C. Chin; B. Christensen; L. Cobo Sanchez; J.-F. Coeurjolly; K. Colyvas; H. Commenges; R. Constantine; R. Corria Ainslie; R. Cotton; M. de la Cruz; P. Dalgaard; M. D'Antuono; S. Das; P.J. Diggle; P. Donnelly; I. Dryden; S. Eglen; A. El-Gabbas; B. Fandohan; O. Flores; E.D. Ford; P. Forbes; S. Frank; J. Franklin; N. Funwi-Gabga; O. Garcia; A. Gault; J. Geldmann; M. Genton; S. Ghalandarayeshi; J. Gilbey; J. Goldstick; P. Grabarnik; C. Graf; U. Hahn; A. Hardegen; M.B. Hansen; M. Hazelton; J. Heikkinen; M. Hering; M. Herrmann; M. Hesselbarth; P. Hewson; H. Heydarian; K. Hingee; K. Hornik; P. Hunziker; J. Hywood; R. Ihaka; C. Icos; A. Jammalamadaka; R. John-Chandran; D. Johnson; M. Khanmohammadi; R. Klaver; P. Kovesi; L. Kozmian-Ledward; M. Kuhn; J. Laake; R.A. Lamb; F. Lavancier; T. Lawrence; T. Lazauskas; J. Lee; G.P. Leser; A. Li; H.T. Li; G. Limitsios; A. Lister; N. Luambua; B. Madin; M. Maechler; J. Marcus; K. Marchikanti; R. Mark; J. Mateu; P. McCullagh; U. Mehlig; F. Mestre; S. Meyer; X.C. Mi; L. De Middeleer; R.K. Milne; E. Miranda; J. Moller; A. Mollie; I. Moncada; M. Moradi; V. Morera Pujol; E. Mudrak; G.M. Nair; N. Najari; N. Nava; L.S. Nielsen; F. Nunes; J.R. Nyengaard; J. Oehlschlaegel; T. Onkelinx; S. O'Riordan; E. Parilov; J. Picka; N. Picard; T. Pollington; M. Porter; S. Protsiv; A. Raftery; S. Rakshit; B. Ramage; P. Ramon; X. Raynaud; N. Read; M. Reiter; I. Renner; T.O. Richardson; B.D. Ripley; E. Rosenbaum; B. Rowlingson; J. Rudokas; J. Rudge; C. Ryan; F. Safavimanesh; A. Sarkka; C. Schank; K. Schladitz; S. Schutte; B.T. Scott; O. Semboli; F. Semecurbe; V. Shcherbakov; G.C. Shen; P. Shi; H.-J. Ship; T.L. Silva; I.-M. Sintorn; Y. Song; M. Spiess; M. Stevenson; K. Stucki; J. Sulavik; M. Sumner; P. Surovy; B. Taylor; T. Thorarinsdottir; L. Torres; B. Turlach; T. Tvedebrink; K. Ummer; M. Uppala; A. van Burgel; T. Verbeke; M. Vihtakari; A. Villers; F. Vinatier; S. Voss; S. Wagner; H. Wang; H. Wendrock; J. Wild; C. Witthoft; S. Wong; M. Woringer; L. Yates; M.E. Zamboni and A. Zeileis.
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat versions 1.63-0 dated 2020-01-23 and 1.63-2 dated 2020-02-23

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Package slider updated to version 0.1.1 with previous version 0.1.0 dated 2020-02-06

Title: Sliding Window Functions
Description: Provides type-stable rolling window functions over any R data type. Cumulative and expanding windows are also supported. For more advanced usage, an index can be used as a secondary vector that defines how sliding windows are to be created.
Author: Davis Vaughan [aut, cre], RStudio [cph]
Maintainer: Davis Vaughan <davis@rstudio.com>

Diff between slider versions 0.1.0 dated 2020-02-06 and 0.1.1 dated 2020-02-23

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Package nlpred updated to version 1.0.1 with previous version 1.0.0 dated 2019-09-27

Title: Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples
Description: Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), <doi:10.1080/01621459.2019.1668794>). Cross-validated area under the receiver operating characteristics curve (LeDell, Petersen, van der Laan (2015), <doi:10.1214/15-EJS1035>) and other metrics are included.
Author: David Benkeser [aut, cre]
Maintainer: David Benkeser <benkeser@emory.edu>

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Permanent link

Package multigroup updated to version 0.4.5 with previous version 0.4.4 dated 2015-03-11

Title: Multigroup Data Analysis
Description: Multivariate analysis methods including principal component analysis, partial least square regression, and multiblock analysis to describe, summarize, and visualize data with a group structure.
Author: Aida Eslami, El Mostafa Qannari, Stephanie Bougeard, Gaston Sanchez Questions and comments go to Aida Eslami <aida.eslami@yahoo.fr> and Stephanie Bougeard <stephanie.bougeard@anses.fr>
Maintainer: Aida Eslami <aida.eslami@yahoo.fr>

Diff between multigroup versions 0.4.4 dated 2015-03-11 and 0.4.5 dated 2020-02-23

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Package flsa updated to version 1.5.2 with previous version 1.5.1 dated 2019-03-13

Title: Path Algorithm for the General Fused Lasso Signal Approximator
Description: Implements a path algorithm for the Fused Lasso Signal Approximator. For more details see the help files or the article by Hoefling (2009) <arXiv:0910.0526>.
Author: Holger Hoefling
Maintainer: Holger Hoefling <hhoeflin@gmail.com>

Diff between flsa versions 1.5.1 dated 2019-03-13 and 1.5.2 dated 2020-02-23

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Package DEoptim updated to version 2.2-5 with previous version 2.2-4 dated 2016-12-19

Title: Global Optimization by Differential Evolution
Description: Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector.
Author: David Ardia [aut], Katharine Mullen [aut, cre], Brian Peterson [aut], Joshua Ulrich [aut], Kris Boudt [ctb]
Maintainer: Katharine Mullen <mullenkate@gmail.com>

Diff between DEoptim versions 2.2-4 dated 2016-12-19 and 2.2-5 dated 2020-02-23

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Package brms updated to version 2.12.0 with previous version 2.11.1 dated 2020-01-19

Title: Bayesian Regression Models using 'Stan'
Description: Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Author: Paul-Christian Bürkner [aut, cre]
Maintainer: Paul-Christian Bürkner <paul.buerkner@gmail.com>

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Package sylcount updated to version 0.2-2 with previous version 0.2-1 dated 2019-03-12

Title: Syllable Counting and Readability Measurements
Description: An English language syllable counter, plus readability score measure-er. For readability, we support 'Flesch' Reading Ease and 'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975) <https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>, Automated Readability Index ('Senter' and Smith 1967) <http://www.dtic.mil/cgi-bin/GetTRDoc?AD=AD0667273>, Simple Measure of Gobbledygook (McLaughlin 1969) <https://www.semanticscholar.org/paper/SMOG-Grading-A-New-Readability-Formula.-Laughlin/5fccb74c14769762b3de010c5e8a1a7ce700d17a>, and 'Coleman-Liau' (Coleman and 'Liau' 1975) <doi:10.1037/h0076540>. The package has been carefully optimized and should be very efficient, both in terms of run time performance and memory consumption. The main methods are 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'.
Author: Drew Schmidt [aut, cre]
Maintainer: Drew Schmidt <wrathematics@gmail.com>

Diff between sylcount versions 0.2-1 dated 2019-03-12 and 0.2-2 dated 2020-02-23

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Package fakemake updated to version 1.7.0 with previous version 1.6.0 dated 2019-11-28

Title: Mock the Unix Make Utility
Description: Use R as a minimal build system. This might come in handy if you are developing R packages and can not use a proper build system. Stay away if you can (use a proper build system).
Author: Andreas Dominik Cullmann [aut, cre]
Maintainer: Andreas Dominik Cullmann <fvafrcu@mailbox.org>

Diff between fakemake versions 1.6.0 dated 2019-11-28 and 1.7.0 dated 2020-02-23

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Package fastNaiveBayes updated to version 2.2.0 with previous version 2.1.1 dated 2020-02-09

Title: Extremely Fast Implementation of a Naive Bayes Classifier
Description: This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
Author: Martin Skogholt
Maintainer: Martin Skogholt <m.skogholt@gmail.com>

Diff between fastNaiveBayes versions 2.1.1 dated 2020-02-09 and 2.2.0 dated 2020-02-23

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Package AMR updated to version 1.0.1 with previous version 1.0.0 dated 2020-02-17

Title: Antimicrobial Resistance Analysis
Description: Functions to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods, like those defined by Leclercq et al. (2013) <doi:10.1111/j.1469-0691.2011.03703.x> and the Clinical and Laboratory Standards Institute (2014) <isbn: 1-56238-899-1>.
Author: Matthijs S. Berends [aut, cre] (<https://orcid.org/0000-0001-7620-1800>), Christian F. Luz [aut, ctb] (<https://orcid.org/0000-0001-5809-5995>), Alexander W. Friedrich [aut, ths] (<https://orcid.org/0000-0003-4881-038X>), Bhanu N. M. Sinha [aut, ths] (<https://orcid.org/0000-0003-1634-0010>), Casper J. Albers [aut, ths] (<https://orcid.org/0000-0002-9213-6743>), Corinna Glasner [aut, ths] (<https://orcid.org/0000-0003-1241-1328>), Judith M. Fonville [ctb], Erwin E. A. Hassing [ctb], Eric H. L. C. M. Hazenberg [ctb], Annick Lenglet [ctb], Bart C. Meijer [ctb], Sofia Ny [ctb], Dennis Souverein [ctb]
Maintainer: Matthijs S. Berends <m.s.berends@umcg.nl>

Diff between AMR versions 1.0.0 dated 2020-02-17 and 1.0.1 dated 2020-02-23

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 34 files changed, 725 insertions(+), 552 deletions(-)

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Package spBayesSurv updated to version 1.1.4 with previous version 1.1.3 dated 2018-04-23

Title: Bayesian Modeling and Analysis of Spatially Correlated Survival Data
Description: Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.
Author: Haiming Zhou <zhouh@niu.edu> and Timothy Hanson <hansont@stat.sc.edu>
Maintainer: Haiming Zhou <zhouh@niu.edu>

Diff between spBayesSurv versions 1.1.3 dated 2018-04-23 and 1.1.4 dated 2020-02-23

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 spBayesSurv-1.1.4/spBayesSurv/src/spSurv_PO_BP.cpp             |   58 
 spBayesSurv-1.1.4/spBayesSurv/src/spSurv_frailty_GRF_LDTFP.cpp |   49 
 28 files changed, 1080 insertions(+), 1049 deletions(-)

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Package SNSequate updated to version 1.3.3 with previous version 1.3.2 dated 2020-01-23

Title: Standard and Nonstandard Statistical Models and Methods for Test Equating
Description: Contains functions to perform various models and methods for test equating. It currently implements the traditional mean, linear and equipercentile equating methods. Both IRT observed-score and true-score equating are also supported, as well as the mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods. It also supports newest methods such that local equating, kernel equating (using Gaussian, logistic, Epanechnikov, uniform and adaptive kernels) with presmoothing, and IRT parameter linking methods based on asymmetric item characteristic functions. Functions to obtain both standard error of equating (SEE) and standard error of equating differences between two equating functions (SEED) are also implemented for the kernel method of equating.
Author: Jorge Gonzalez [cre, aut], Daniel Leon Acuna [ctb]
Maintainer: Jorge Gonzalez <jorge.gonzalez@mat.uc.cl>

Diff between SNSequate versions 1.3.2 dated 2020-01-23 and 1.3.3 dated 2020-02-23

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Package OjaNP updated to version 1.0-0 with previous version 0.9-13 dated 2020-02-09

Title: Multivariate Methods Based on the Oja Median and Related Concepts
Description: Functions to calculate the Oja median, Oja signs and ranks and methods based upon them. For details, see the the corresponding publication in the Journal of Statistical Software <doi:10.18637/jss.v092.i08>.
Author: Daniel Fischer, Karl Mosler, Jyrki Möttönen, Klaus Nordhausen, Oleksii Pokotylo, Daniel Vogel
Maintainer: Daniel Fischer <daniel.fischer@luke.fi>

Diff between OjaNP versions 0.9-13 dated 2020-02-09 and 1.0-0 dated 2020-02-23

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 13 files changed, 46 insertions(+), 18 deletions(-)

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Package gdpc updated to version 1.1.1 with previous version 1.1.0 dated 2018-05-08

Title: Generalized Dynamic Principal Components
Description: Functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02> for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy.
Author: Daniel Peña <daniel.pena@uc3m.es>, Ezequiel Smucler <ezequiels.90@gmail.com>, Victor Yohai <vyohai@dm.uba.ar>
Maintainer: Ezequiel Smucler <ezequiels.90@gmail.com>

Diff between gdpc versions 1.1.0 dated 2018-05-08 and 1.1.1 dated 2020-02-23

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Package SelectBoost updated to version 2.0.0 with previous version 1.4.0 dated 2019-05-27

Title: A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets
Description: An implementation of the selectboost algorithm (Bertrand et al. 2020, <arXiv:1810.01670>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.
Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Ismail Aouadi [ctb], Nicolas Jung [ctb]
Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr>

Diff between SelectBoost versions 1.4.0 dated 2019-05-27 and 2.0.0 dated 2020-02-23

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 SelectBoost-2.0.0/SelectBoost/DESCRIPTION                                                               |   16 
 SelectBoost-2.0.0/SelectBoost/MD5                                                                       |  115 
 SelectBoost-2.0.0/SelectBoost/NAMESPACE                                                                 |   16 
 SelectBoost-2.0.0/SelectBoost/NEWS.md                                                                   |   12 
 SelectBoost-2.0.0/SelectBoost/R/SelectBoost-package.R                                                   |    5 
 SelectBoost-2.0.0/SelectBoost/R/autoboost.R                                                             | 1505 ++++++----
 SelectBoost-2.0.0/SelectBoost/R/boost.R                                                                 |   12 
 SelectBoost-2.0.0/SelectBoost/R/datasets.R                                                              |   14 
 SelectBoost-2.0.0/SelectBoost/R/group_func_1.R                                                          |    2 
 SelectBoost-2.0.0/SelectBoost/R/group_func_2.R                                                          |    2 
 SelectBoost-2.0.0/SelectBoost/R/lasso_cv.R                                                              |  456 ++-
 SelectBoost-2.0.0/SelectBoost/R/miscplots.R                                                             |    4 
 SelectBoost-2.0.0/SelectBoost/R/plot_selectboost_microarray.R                                           |    2 
 SelectBoost-2.0.0/SelectBoost/R/selectboost_microarray.R                                                |    2 
 SelectBoost-2.0.0/SelectBoost/R/simulation.R                                                            |   22 
 SelectBoost-2.0.0/SelectBoost/build/vignette.rds                                                        |binary
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 SelectBoost-2.0.0/SelectBoost/data/datalist                                                             |only
 SelectBoost-2.0.0/SelectBoost/demo/00Index                                                              |    8 
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 SelectBoost-2.0.0/SelectBoost/inst/doc/benchmarking-selectboost-networks.R                              |  763 -----
 SelectBoost-2.0.0/SelectBoost/inst/doc/benchmarking-selectboost-networks.Rmd                            |    2 
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 SelectBoost-2.0.0/SelectBoost/inst/doc/confidence-indices-Cascade-networks.Rmd                          |    4 
 SelectBoost-2.0.0/SelectBoost/inst/doc/confidence-indices-Cascade-networks.html                         |   61 
 SelectBoost-2.0.0/SelectBoost/inst/doc/sim-with-sb.R                                                    |  421 --
 SelectBoost-2.0.0/SelectBoost/inst/doc/sim-with-sb.Rmd                                                  |    2 
 SelectBoost-2.0.0/SelectBoost/inst/doc/sim-with-sb.html                                                 |  543 ---
 SelectBoost-2.0.0/SelectBoost/man/Cascade_confidence.Rd                                                 |    3 
 SelectBoost-2.0.0/SelectBoost/man/Cascade_example.Rd                                                    |    3 
 SelectBoost-2.0.0/SelectBoost/man/SelectBoost.Rd                                                        |    3 
 SelectBoost-2.0.0/SelectBoost/man/auto.analyze.Rd                                                       |    7 
 SelectBoost-2.0.0/SelectBoost/man/autoboost.Rd                                                          |   34 
 SelectBoost-2.0.0/SelectBoost/man/boost.Rd                                                              |   60 
 SelectBoost-2.0.0/SelectBoost/man/fastboost.Rd                                                          |   44 
 SelectBoost-2.0.0/SelectBoost/man/force.non.inc.Rd                                                      |only
 SelectBoost-2.0.0/SelectBoost/man/group_func_1.Rd                                                       |    2 
 SelectBoost-2.0.0/SelectBoost/man/group_func_2.Rd                                                       |    2 
 SelectBoost-2.0.0/SelectBoost/man/miscplot.Rd                                                           |    2 
 SelectBoost-2.0.0/SelectBoost/man/network.confidence-class.Rd                                           |    8 
 SelectBoost-2.0.0/SelectBoost/man/plot.selectboost.Rd                                                   |   16 
 SelectBoost-2.0.0/SelectBoost/man/plot.summary.selectboost.Rd                                           |    7 
 SelectBoost-2.0.0/SelectBoost/man/plot_selectboost_cascade.Rd                                           |   13 
 SelectBoost-2.0.0/SelectBoost/man/selectboost_cascade.Rd                                                |   25 
 SelectBoost-2.0.0/SelectBoost/man/simulation.Rd                                                         |    4 
 SelectBoost-2.0.0/SelectBoost/man/summary.selectboost.Rd                                                |   19 
 SelectBoost-2.0.0/SelectBoost/man/trajC0.Rd                                                             |   19 
 SelectBoost-2.0.0/SelectBoost/man/var_select.Rd                                                         |    5 
 SelectBoost-2.0.0/SelectBoost/man/var_select_all.Rd                                                     |only
 SelectBoost-2.0.0/SelectBoost/vignettes/benchmarking-selectboost-networks.Rmd                           |    2 
 SelectBoost-2.0.0/SelectBoost/vignettes/confidence-indices-Cascade-networks.Rmd                         |    4 
 SelectBoost-2.0.0/SelectBoost/vignettes/sim-with-sb.Rmd                                                 |    2 
 65 files changed, 2043 insertions(+), 2809 deletions(-)

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Package BNSP updated to version 2.1.3 with previous version 2.1.2 dated 2019-12-05

Title: Bayesian Non- And Semi-Parametric Model Fitting
Description: MCMC algorithms & processing functions for: 1. multivariate (and univariate) regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, and 2. Dirichlet process mixtures.
Author: Georgios Papageorgiou
Maintainer: Georgios Papageorgiou <gpapageo@gmail.com>

Diff between BNSP versions 2.1.2 dated 2019-12-05 and 2.1.3 dated 2020-02-23

 DESCRIPTION         |   12 +--
 MD5                 |   30 ++++----
 NAMESPACE           |    3 
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 man/BNSP-package.Rd |    8 +-
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 16 files changed, 203 insertions(+), 110 deletions(-)

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New package matricks with initial version 0.8.2
Package: matricks
Type: Package
Title: Useful Tricks for Matrix Manipulation
Version: 0.8.2
Authors@R: person(given = "Krzysztof", family = "Joachimiak", role = c("aut", "cre"), email = "joachimiak.krzysztof@gmail.com", comment = c(ORCID = "0000-0003-4780-7947"))
Description: Provides functions, which make matrix creation conciser (such as the core package's function m() for rowwise matrix definition or runifm() for random value matrices). Allows to set multiple matrix values at once, by using list of formulae. Provides additional matrix operators and dedicated plotting function.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
BugReports: https://github.com/krzjoa/matricks/issues
URL: https://github.com/krzjoa/matricks, https://krzjoa.github.io/matricks/
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, covr
RoxygenNote: 6.1.1
LinkingTo: Rcpp
Imports: Rcpp, rlang, ggplot2, reshape2
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-02-12 22:40:10 UTC; krzysztof
Author: Krzysztof Joachimiak [aut, cre] (<https://orcid.org/0000-0003-4780-7947>)
Maintainer: Krzysztof Joachimiak <joachimiak.krzysztof@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-23 11:40:02 UTC

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New package iNZightTools with initial version 1.8.3
Package: iNZightTools
Type: Package
Title: Tools for 'iNZight'
Version: 1.8.3
Imports: utils, stats, methods, survey, grDevices, readr (>= 1.2.0), readxl, haven, magrittr, tibble, dplyr, forcats, tidyr, stringr, tools, chron, lubridate, zoo, validate
Suggests: testthat, covr, RCurl
Authors@R: c( person("Tom", "Elliott", role = c("aut", "cre"), email = "tom.elliott@auckland.ac.nz", comment = c(ORCID = "0000-0002-7815-6318")), person("Christoph", "Knopf", role = "ctb"), person("Akshay", "Gupta", role = "ctb"), person("Owen", "Jin", role = "ctb", comment = "Tidyverse variable/data manipulation functions"), person("Lushi", "Cai", role = "ctb"), person("Yiwen", "He", role = "ctb", comment = "Dates/times and dataset manipulation"), person("Daniel", "Barnett", role = "ctb", comment = "Data validation") )
BugReports: https://github.com/iNZightVIT/iNZightTools/issues
Contact: inzight_support@stat.auckland.ac.nz
URL: https://www.stat.auckland.ac.nz/~wild/iNZight/
Description: Provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the 'tidyverse' code used to obtain the result in an effort to bridge the gap between GUI and coding.
License: GPL-3
Encoding: UTF-8
Language: en-GB
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-02-12 22:28:51 UTC; emperor
Author: Tom Elliott [aut, cre] (<https://orcid.org/0000-0002-7815-6318>), Christoph Knopf [ctb], Akshay Gupta [ctb], Owen Jin [ctb] (Tidyverse variable/data manipulation functions), Lushi Cai [ctb], Yiwen He [ctb] (Dates/times and dataset manipulation), Daniel Barnett [ctb] (Data validation)
Maintainer: Tom Elliott <tom.elliott@auckland.ac.nz>
Repository: CRAN
Date/Publication: 2020-02-23 11:30:03 UTC

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New package usa with initial version 0.1.0
Package: usa
Title: Updated US State Facts and Figures
Version: 0.1.0
Authors@R: person(given = "Kiernan", family = "Nicholls", role = c("aut", "cre"), email = "kiernann@protonmail.com")
Description: Updated versions of the 1970's "US State Facts and Figures" objects from the 'datasets' package included with R. The new data is compiled from a number of sources, primarily from United States Census Bureau or the relevant federal agency.
License: CC BY 4.0
URL: https://kiernann.com/usa https://github.com/kiernann/usa
BugReports: https://github.com/kiernann/usa/issues
Depends: R (>= 3.2)
Imports: tibble (>= 2.1.3)
Suggests: covr (>= 3.3.2), testthat (>= 2.1.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-02-12 18:35:29 UTC; kiernan
Author: Kiernan Nicholls [aut, cre]
Maintainer: Kiernan Nicholls <kiernann@protonmail.com>
Repository: CRAN
Date/Publication: 2020-02-23 09:30:03 UTC

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Package styler updated to version 1.3.2 with previous version 1.3.1 dated 2020-02-13

Title: Non-Invasive Pretty Printing of R Code
Description: Pretty-prints R code without changing the user's formatting intent.
Author: Kirill Müller [aut], Lorenz Walthert [cre, aut]
Maintainer: Lorenz Walthert <lorenz.walthert@icloud.com>

Diff between styler versions 1.3.1 dated 2020-02-13 and 1.3.2 dated 2020-02-23

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 styler-1.3.2/styler/DESCRIPTION                                                   |   12 
 styler-1.3.2/styler/MD5                                                           |  162 ++++----
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More information about styler at CRAN
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Package checkpoint updated to version 0.4.9 with previous version 0.4.8 dated 2020-01-13

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

Diff between checkpoint versions 0.4.8 dated 2020-01-13 and 0.4.9 dated 2020-02-23

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More information about checkpoint at CRAN
Permanent link


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