Fri, 15 Jan 2021

Package withr updated to version 2.4.0 with previous version 2.3.0 dated 2020-09-22

Title: Run Code 'With' Temporarily Modified Global State
Description: A set of functions to run code 'with' safely and temporarily modified global state. Many of these functions were originally a part of the 'devtools' package, this provides a simple package with limited dependencies to provide access to these functions.
Author: Jim Hester [aut, cre], Kirill Müller [aut], Kevin Ushey [aut], Hadley Wickham [aut], Winston Chang [aut], Jennifer Bryan [ctb], Richard Cotton [ctb], RStudio [cph]
Maintainer: Jim Hester <james.f.hester@gmail.com>

Diff between withr versions 2.3.0 dated 2020-09-22 and 2.4.0 dated 2021-01-15

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Package sharpData updated to version 1.3 with previous version 1.2 dated 2019-08-19

Title: Data Sharpening
Description: Functions and data sets inspired by data sharpening - data perturbation to achieve improved performance in nonparametric estimation, as described in Choi, E., Hall, P. and Rousson, V. (2000) <doi:10.1214/aos/1015957396>. Capabilities for enhanced local linear regression function and derivative estimation are included, as well as an asymptotically correct iterated data sharpening estimator for any degree of local polynomial regression estimation. A cross-validation-based bandwidth selector is included which, in concert with the iterated sharpener, will often provide superior performance, according to a median integrated squared error criterion. Sample data sets are provided to illustrate function usage.
Author: W.J. Braun
Maintainer: W.J. Braun <john.braun@ubc.ca>

Diff between sharpData versions 1.2 dated 2019-08-19 and 1.3 dated 2021-01-15

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Package mixture updated to version 2.0.1 with previous version 2.0.0 dated 2021-01-11

Title: Mixture Models for Clustering and Classification
Description: An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.
Author: Nik Pocuca [aut], Ryan P. Browne [aut], Paul D. McNicholas [aut, cre]
Maintainer: Paul D. McNicholas <mcnicholas@math.mcmaster.ca>

Diff between mixture versions 2.0.0 dated 2021-01-11 and 2.0.1 dated 2021-01-15

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Package directlabels updated to version 2021.1.13 with previous version 2020.12.29 dated 2021-01-05

Title: Direct Labels for Multicolor Plots
Description: An extensible framework for automatically placing direct labels onto multicolor 'lattice' or 'ggplot2' plots. Label positions are described using Positioning Methods which can be re-used across several different plots. There are heuristics for examining "trellis" and "ggplot" objects and inferring an appropriate Positioning Method.
Author: Toby Dylan Hocking
Maintainer: Toby Dylan Hocking <toby.hocking@r-project.org>

Diff between directlabels versions 2020.12.29 dated 2021-01-05 and 2021.1.13 dated 2021-01-15

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New package SpatMCA with initial version 1.0.2.0
Package: SpatMCA
Type: Package
Title: Regularized Spatial Maximum Covariance Analysis
Version: 1.0.2.0
Date: 2021-01-06
URL: https://github.com/egpivo/SpatMCA
BugReports: https://github.com/egpivo/SpatMCA/issues
Description: Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2017 <doi:10.1002/env.2481>).
License: GPL-2
Imports: Rcpp, RcppParallel (>= 0.11.2), fields, MASS
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: testthat (>= 2.1.0), RColorBrewer, plot3D, pracma, spTimer, maps
SystemRequirements: GNU make
NeedsCompilation: yes
Author: Wen-Ting Wang [aut, cre], Hsin-Cheng Huang [aut]
Maintainer: Wen-Ting Wang <egpivo@gmail.com>
Encoding: UTF-8
RoxygenNote: 7.1.1
Packaged: 2021-01-12 14:31:12 UTC; wen-tingwang
Repository: CRAN
Date/Publication: 2021-01-16 00:50:08 UTC

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Package metasens updated to version 0.6-0 with previous version 0.5-0 dated 2020-09-29

Title: Statistical Methods for Sensitivity Analysis in Meta-Analysis
Description: The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 'Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>; - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>; - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>; - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.
Author: Guido Schwarzer [cre, aut] (<https://orcid.org/0000-0001-6214-9087>), James R. Carpenter [aut] (<https://orcid.org/0000-0003-3890-6206>), Gerta Rücker [aut] (<https://orcid.org/0000-0002-2192-2560>)
Maintainer: Guido Schwarzer <sc@imbi.uni-freiburg.de>

Diff between metasens versions 0.5-0 dated 2020-09-29 and 0.6-0 dated 2021-01-15

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New package GLMcat with initial version 0.1.1
Package: GLMcat
Title: Generalized Linear Models for Categorical Responses
Version: 0.1.1
Authors@R: c(person(given = "Lorena", family = "León",role = c("aut", "cre"), email = "ylorenaleonv@gmail.com"), person(given = "Jean", family = "Peyhardi", email = "jean.peyhardi@umontpellier.fr", role = "aut"), person(given = "Catherine", family = "Trottier", email = "catherine.trottier@umontpellier.fr", role = "aut"))
Description: In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) <doi:10.1093/biomet/asv042>, we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative distribution function for the linkage, and Z, the design matrix.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 2.10)
LazyData: true
RoxygenNote: 7.0.2
LinkingTo: Rcpp, BH, RcppEigen
Imports: Rcpp, stats
Suggests: knitr, rmarkdown, testthat, dplyr, ggplot2, gridExtra, gtools, tidyr
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2021-01-12 22:07:45 UTC; leonvelasco
Author: Lorena León [aut, cre], Jean Peyhardi [aut], Catherine Trottier [aut]
Maintainer: Lorena León <ylorenaleonv@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-16 00:50:11 UTC

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Package egor updated to version 0.21.01 with previous version 0.20.06 dated 2020-06-12

Title: Import and Analyse Ego-Centered Network Data
Description: Tools for importing, analyzing and visualizing ego-centered network data. Supports several data formats, including the export formats of 'EgoNet', 'EgoWeb 2.0' and 'openeddi'. An interactive (shiny) app for the intuitive visualization of ego-centered networks is provided. Also included are procedures for creating and visualizing Clustered Graphs (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).
Author: Till Krenz [aut, cre], Pavel N. Krivitsky [aut], Raffaele Vacca [aut], Michal Bojanowski [aut] (<https://orcid.org/0000-0001-7503-852X>), Markus Gamper [ctb], Andreas Herz [aut], Christopher McCarty [ctb]
Maintainer: Till Krenz <egor@tillt.net>

Diff between egor versions 0.20.06 dated 2020-06-12 and 0.21.01 dated 2021-01-15

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More information about egor at CRAN
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New package extRatum with initial version 1.0.3
Package: extRatum
Title: Summary Statistics for Geospatial Features
Version: 1.0.3
Authors@R: c( person("Nikos", "Patias", email = "n.patias@liverpool.ac.uk", role = c("aut","cre"), comment = c(ORCID = "0000-0002-6542-2330")), person("Francisco", "Rowe", email = "fcorowe@liverpool.ac.uk", role = ("aut")) )
Description: Provides summary statistics of local geospatial features within a given geographic area. It does so by calculating the area covered by a target geospatial feature (i.e. buildings, parks, lakes, etc.). The geospatial features can be of any type of geospatial data, including point, polygon or line data.
License: MIT + file LICENSE
Depends: R (>= 4.0.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
SystemRequirements: C++11, GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0)
Date/Publication: 2021-01-16 00:50:02 UTC
Imports: sf (>= 0.9.5), dplyr (>= 1.0.0), tidyr (>= 1.1.0)
NeedsCompilation: no
Packaged: 2021-01-13 15:04:09 UTC; User
Author: Nikos Patias [aut, cre] (<https://orcid.org/0000-0002-6542-2330>), Francisco Rowe [aut]
Maintainer: Nikos Patias <n.patias@liverpool.ac.uk>
Repository: CRAN

More information about extRatum at CRAN
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Package convoSPAT updated to version 1.2.7 with previous version 1.2.6 dated 2020-01-23

Title: Convolution-Based Nonstationary Spatial Modeling
Description: Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the Kriging predictor and standard errors, and create various plots to visualize nonstationarity.
Author: Mark D. Risser [aut, cre]
Maintainer: Mark D. Risser <markdrisser@gmail.com>

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Package bRacatus updated to version 1.0.2 with previous version 1.0.1 dated 2020-12-11

Title: A Method to Estimate the Accuracy and Biogeographical Status of Georeferenced Biological Data
Description: Automated assessment of accuracy and geographical status of georeferenced biological data. The methods rely on reference regions, namely checklists and range maps. Includes functions to obtain data from the Global Biodiversity Information Facility <https://www.gbif.org/> and from the Global Inventory of Floras and Traits <https://gift.uni-goettingen.de/home>. Alternatively, the user can input their own data. Furthermore, provides easy visualisation of the data and the results through the plotting functions. Especially suited for large datasets. The reference for the methodology is: Arlé et al. (under review).
Author: Eduardo Arlé [aut, cre], Alexander Zizka [aut], Patrick Weigelt [ctb], Sam Levin [ctb], Carsten Meyer [ths]
Maintainer: Eduardo Arlé <eduardo.arle@idiv.de>

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Package BET updated to version 0.3.1 with previous version 0.3.0 dated 2021-01-12

Title: Binary Expansion Testing
Description: Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921> and Zhigen Zhao, Michael Baiocchi, Kai Zhang. SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables.
Author: Wan Zhang [aut, cre], Zhigen Zhao [aut], Michael Baiocchi [aut], Kai Zhang [aut]
Maintainer: Wan Zhang <wanz63@live.unc.edu>

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Package arcpullr updated to version 0.1.1 with previous version 0.1 dated 2021-01-15

Title: Pull Data from an 'ArcGIS REST' API
Description: Functions to efficiently query 'ArcGIS REST' APIs <https://developers.arcgis.com/rest/>. Both spatial and SQL queries can be used to retrieve data. Simple Feature (sf) objects are utilized to perform spatial queries. This package was neither produced nor is maintained by Esri.
Author: Paul Frater [aut, cre] (<https://orcid.org/0000-0002-7237-6563>), Zac Driscoll [aut] (<https://orcid.org/0000-0002-8233-0980>)
Maintainer: Paul Frater <paul.frater@wisconsin.gov>

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Package OneStep updated to version 0.9.2 with previous version 0.9.1 dated 2020-09-16

Title: One-Step Estimation
Description: Provide principally an eponymic function that numerically computes the Le Cam one-step estimator which is asymptotically efficient (see e.g. L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) and can be computed faster than the maximum likelihood estimator for large observation samples.
Author: Alexandre Brouste, Darel Noutsa Mieniedou, Christophe Dutang
Maintainer: Christophe Dutang <christophe.dutang@ensimag.fr>

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Package ggrepel updated to version 0.9.1 with previous version 0.9.0 dated 2020-12-16

Title: Automatically Position Non-Overlapping Text Labels with 'ggplot2'
Description: Provides text and label geoms for 'ggplot2' that help to avoid overlapping text labels. Labels repel away from each other and away from the data points.
Author: Kamil Slowikowski [aut, cre] (<https://orcid.org/0000-0002-2843-6370>), Alicia Schep [ctb] (<https://orcid.org/0000-0002-3915-0618>), Sean Hughes [ctb] (<https://orcid.org/0000-0002-9409-9405>), Trung Kien Dang [ctb] (<https://orcid.org/0000-0001-7562-6495>), Saulius Lukauskas [ctb], Jean-Olivier Irisson [ctb] (<https://orcid.org/0000-0003-4920-3880>), Zhian N Kamvar [ctb] (<https://orcid.org/0000-0003-1458-7108>), Thompson Ryan [ctb] (<https://orcid.org/0000-0002-0450-8181>), Dervieux Christophe [ctb] (<https://orcid.org/0000-0003-4474-2498>), Yutani Hiroaki [ctb], Pierre Gramme [ctb], Amir Masoud Abdol [ctb], Malcolm Barrett [ctb] (<https://orcid.org/0000-0003-0299-5825>), Robrecht Cannoodt [ctb] (<https://orcid.org/0000-0003-3641-729X>), Michał Krassowski [ctb] (<https://orcid.org/0000-0002-9638-7785>), Michael Chirico [ctb] (<https://orcid.org/0000-0003-0787-087X>), Pedro Aphalo [ctb] (<https://orcid.org/0000-0003-3385-972X>)
Maintainer: Kamil Slowikowski <kslowikowski@gmail.com>

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Package pairwiseComparisons updated to version 3.1.2 with previous version 3.1.1 dated 2020-12-03

Title: Multiple Pairwise Comparison Tests
Description: Multiple pairwise comparison tests on tidy data for one-way analysis of variance for both between-subjects and within-subjects designs. Currently, it supports only the most common types of statistical analyses and tests: parametric (Welch's and Student's t-test), nonparametric (Durbin-Conover and Dunn test), robust (Yuen’s trimmed means test), and Bayes Factor (Student's t-test).
Author: Indrajeet Patil [cre, aut, cph] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package rubias updated to version 0.3.2 with previous version 0.3.1 dated 2020-04-02

Title: Bayesian Inference from the Conditional Genetic Stock Identification Model
Description: Implements Bayesian inference for the conditional genetic stock identification model. It allows inference of mixed fisheries and also simulation of mixtures to predict accuracy. A full description of the underlying methods is available in a recently published article in the Canadian Journal of Fisheries and Aquatic Sciences: <doi:10.1139/cjfas-2018-0016>.
Author: Eric C. Anderson [aut, cre], Ben Moran [aut]
Maintainer: Eric C. Anderson <eric.anderson@noaa.gov>

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Package PMCMRplus updated to version 1.9.0 with previous version 1.7.1 dated 2020-11-30

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>

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Package FSA updated to version 0.8.32 with previous version 0.8.31 dated 2020-11-08

Title: Simple Fisheries Stock Assessment Methods
Description: A variety of simple fish stock assessment methods. Detailed vignettes are available on the fishR website <http://derekogle.com/fishR/>.
Author: Derek Ogle [aut, cre] (<https://orcid.org/0000-0002-0370-9299>), Powell Wheeler [aut], Alexis Dinno [aut] (Provided base functionality of dunnTest())
Maintainer: Derek Ogle <derek@derekogle.com>

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Package contentid updated to version 0.0.9 with previous version 0.0.8 dated 2020-09-25

Title: An Interface for Content-Based Identifiers
Description: An interface for creating, registering, and resolving content-based identifiers for data management. Content-based identifiers rely on the 'cryptographic' hashes to refer to the files they identify, thus, anyone possessing the file can compute the identifier using a well-known standard algorithm, such as 'SHA256'. By registering a URL at which the content is accessible to a public archive, such as <https://hash-archive.org>, the content identifier can serve many functions typically associated with A Digital Object Identifier ('DOI'). Unlike location-based identifiers like 'DOIs', content-based identifiers permit the same content to be registered in many locations.
Author: Carl Boettiger [aut, cre] (<https://orcid.org/0000-0002-1642-628X>), Jorrit Poelen [aut] (<https://orcid.org/0000-0003-3138-4118>), NSF OAC 1839201 [fnd] (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839201)
Maintainer: Carl Boettiger <cboettig@gmail.com>

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Package powdR updated to version 1.2.5 with previous version 1.2.4 dated 2020-11-16

Title: Full Pattern Summation of X-Ray Powder Diffraction Data
Description: Full pattern summation of X-ray powder diffraction data as described in Chipera and Bish (2002) <doi:10.1107/S0021889802017405>. Derives quantitative estimates of crystalline and amorphous phase concentrations in complex mixtures.
Author: Benjamin Butler [aut, cre], Stephen Hillier [aut], Dylan Beaudette [ctb], Dennis Eberl [ctb]
Maintainer: Benjamin Butler <benjamin.butler@hutton.ac.uk>

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Package AzureRMR updated to version 2.4.0 with previous version 2.3.6 dated 2020-10-14

Title: Interface to 'Azure Resource Manager'
Description: A lightweight but powerful R interface to the 'Azure Resource Manager' REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting 'Azure' resource groups, resources and templates. While 'AzureRMR' can be used to manage any 'Azure' service, it can also be extended by other packages to provide extra functionality for specific services. Part of the 'AzureR' family of packages.
Author: Hong Ooi [aut, cre], Microsoft [cph]
Maintainer: Hong Ooi <hongooi73@gmail.com>

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Permanent link

New package shiny.router with initial version 0.2.1
Package: shiny.router
Type: Package
Title: Basic Routing for Shiny Web Applications
Version: 0.2.1
Authors@R: c(person("Filip", "Stachura", email = "filip@appsilon.com", role = c("aut")), person("Dominik", "Krzemiński", email = "dominik@appsilon.com", role = c("cre", "aut")), person("Krystian", "Igras", email = "krystian@appsilon.com", role = c("aut")), person(family = "Appsilon", role = c("cph")) )
Description: It is a simple router for your Shiny apps. The router allows you to create dynamic web applications with real-time User Interface and easily share url to pages within your Shiny apps.
Encoding: UTF-8
LazyData: true
License: MIT + file LICENSE
Imports: magrittr, shiny, htmltools
RoxygenNote: 7.1.1
Suggests: testthat, covr
NeedsCompilation: no
Packaged: 2021-01-15 13:41:23 UTC; dominik
Author: Filip Stachura [aut], Dominik Krzemiński [cre, aut], Krystian Igras [aut], Appsilon [cph]
Maintainer: Dominik Krzemiński <dominik@appsilon.com>
Repository: CRAN
Date/Publication: 2021-01-15 16:00:02 UTC

More information about shiny.router at CRAN
Permanent link

Package semantic.dashboard updated to version 0.2.0 with previous version 0.1.8 dated 2020-10-19

Title: Dashboard with Fomantic UI Support for Shiny
Description: It offers functions for creating dashboard with Semantic elements.
Author: Filip Stachura [aut], Dominik Krzeminski [cre], Krystian Igras [aut], Michał Maj [ctb], Michał Drzazga [ctb], Appsilon [cph]
Maintainer: Dominik Krzeminski <dominik@appsilon.com>

Diff between semantic.dashboard versions 0.1.8 dated 2020-10-19 and 0.2.0 dated 2021-01-15

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Package libcoin updated to version 1.0-7 with previous version 1.0-6 dated 2020-08-14

Title: Linear Test Statistics for Permutation Inference
Description: Basic infrastructure for linear test statistics and permutation inference in the framework of Strasser and Weber (1999) <https://epub.wu.ac.at/102/>. This package must not be used by end-users. CRAN package 'coin' implements all user interfaces and is ready to be used by anyone.
Author: Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between libcoin versions 1.0-6 dated 2020-08-14 and 1.0-7 dated 2021-01-15

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 vignettes/libcoin.Rnw           |  346 ++++++++++++++++++++--------------------
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More information about libcoin at CRAN
Permanent link

Package tibble updated to version 3.0.5 with previous version 3.0.4 dated 2020-10-12

Title: Simple Data Frames
Description: Provides a 'tbl_df' class (the 'tibble') that provides stricter checking and better formatting than the traditional data frame.
Author: Kirill Müller [aut, cre], Hadley Wickham [aut], Romain Francois [ctb], Jennifer Bryan [ctb], RStudio [cph]
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

Diff between tibble versions 3.0.4 dated 2020-10-12 and 3.0.5 dated 2021-01-15

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More information about tibble at CRAN
Permanent link

Package statsguRu updated to version 0.1.1 with previous version 0.1.0 dated 2019-05-17

Title: Extract, Visualize and Analyze Cricket Statistics from ESPNCricinfo's Statsguru
Description: Use this package to scrap Test Matches, One Day Internationals, Twenty-20 Internationals data of a player from ESPNCricinfo's Statsguru (<http://stats.espncricinfo.com/ci/engine/stats/index.html>) and then visualize their batting, bowling and fielding performances in the form of charts and graphs.
Author: Pranav Nagarajan
Maintainer: Pranav Nagarajan <npranav10@gmail.com>

Diff between statsguRu versions 0.1.0 dated 2019-05-17 and 0.1.1 dated 2021-01-15

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More information about statsguRu at CRAN
Permanent link

New package spatstat.geom with initial version 1.65-5
Package: spatstat.geom
Version: 1.65-5
Date: 2021-01-12
Title: Geometrical Functionality of the 'spatstat' Package
Authors@R: c(person("Adrian", "Baddeley", role = c("aut", "cre"), email = "Adrian.Baddeley@curtin.edu.au"), person("Rolf", "Turner", role = "aut", email="r.turner@auckland.ac.nz"), person("Ege", "Rubak", role = "aut", email = "rubak@math.aau.dk"), person("Tilman", "Davies", role = "ctb"), person("Ute", "Hahn", role = "ctb"), person("Abdollah", "Jalilian", role = "ctb"), person("Suman", "Rakshit", role = "ctb"), person("Dominic", "Schuhmacher", role = "ctb"), person("Rasmus", "Plenge Waagepetersen", role = "ctb"))
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>
Depends: R (>= 3.5.0), spatstat.data (>= 1.6-1), stats, graphics, grDevices, utils, methods
Imports: spatstat.utils (>= 1.18-0), spatstat.sparse, deldir (>= 0.0-21), polyclip (>= 1.10-0)
Suggests: spatstat.core, spatstat.linnet, maptools (>= 0.9-9), spatial, fftwtools (>= 0.9-8)
Description: This is a subset of the original 'spatstat' package, containing the user-level code from 'spatstat' which performs geometrical operations, except for the geometry of linear networks.
License: GPL (>= 2)
URL: http://spatstat.org/
LazyData: true
NeedsCompilation: yes
ByteCompile: true
BugReports: https://github.com/spatstat/spatstat.geom/issues
Packaged: 2021-01-12 10:41:35 UTC; adrian
Author: Adrian Baddeley [aut, cre], Rolf Turner [aut], Ege Rubak [aut], Tilman Davies [ctb], Ute Hahn [ctb], Abdollah Jalilian [ctb], Suman Rakshit [ctb], Dominic Schuhmacher [ctb], Rasmus Plenge Waagepetersen [ctb]
Repository: CRAN
Date/Publication: 2021-01-15 13:40:06 UTC

More information about spatstat.geom at CRAN
Permanent link

Package Rcpp updated to version 1.0.6 with previous version 1.0.5 dated 2020-07-06

Title: Seamless R and C++ Integration
Description: The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <https://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
Author: Dirk Eddelbuettel, Romain Francois, JJ Allaire, Kevin Ushey, Qiang Kou, Nathan Russell, Douglas Bates and John Chambers
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package parameters updated to version 0.11.0 with previous version 0.10.1 dated 2020-12-08

Title: Processing of Model Parameters
Description: Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see support list of insight; Lüdecke, Waggoner & Makowski (2019) <doi:10.21105/joss.01412>), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).
Author: Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>, @strengejacke), Dominique Makowski [aut] (<https://orcid.org/0000-0001-5375-9967>), Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>), Indrajeet Patil [aut] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Søren Højsgaard [aut], Zen J. Lau [ctb], Vincent Arel-Bundock [ctb] (<https://orcid.org/0000-0003-1995-6531>, @vincentab)
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

Diff between parameters versions 0.10.1 dated 2020-12-08 and 0.11.0 dated 2021-01-15

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More information about parameters at CRAN
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Package feisr updated to version 1.1.4 with previous version 1.1.3 dated 2021-01-11

Title: Estimating Fixed Effects Individual Slope Models
Description: Provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package 'plm' by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test.
Author: Tobias Ruettenauer [aut, cre] (<https://orcid.org/0000-0001-5747-9735>), Volker Ludwig [aut] (<https://orcid.org/0000-0003-3118-3172>)
Maintainer: Tobias Ruettenauer <ruettenauer@sowi.uni-kl.de>

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Permanent link

Package FCPS updated to version 1.2.7 with previous version 1.2.6 dated 2020-12-11

Title: Fundamental Clustering Problems Suite
Description: Over fifty conventional clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <DOI:10.1016/j.dib.2020.105501>.
Author: Michael Thrun [aut, cre, cph] (<https://orcid.org/0000-0001-9542-5543>), Peter Nahrgang [ctr, ctb], Felix Pape [ctr, ctb], Vasyl Pihur [ctb], Guy Brock [ctb], Susmita Datta [ctb], Somnath Datta [ctb], Luis Winckelmann [com], Alfred Ultsch [dtc, ctb], Quirin Stier [ctb, rev]
Maintainer: Michael Thrun <m.thrun@gmx.net>

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More information about FCPS at CRAN
Permanent link

Package dplyr updated to version 1.0.3 with previous version 1.0.2 dated 2020-08-18

Title: A Grammar of Data Manipulation
Description: A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
Author: Hadley Wickham [aut, cre] (<https://orcid.org/0000-0003-4757-117X>), Romain François [aut] (<https://orcid.org/0000-0002-2444-4226>), Lionel Henry [aut], Kirill Müller [aut] (<https://orcid.org/0000-0002-1416-3412>), RStudio [cph, fnd]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between dplyr versions 1.0.2 dated 2020-08-18 and 1.0.3 dated 2021-01-15

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More information about dplyr at CRAN
Permanent link

Package CARBayes updated to version 5.2.2 with previous version 5.2 dated 2020-03-13

Title: Spatial Generalised Linear Mixed Models for Areal Unit Data
Description: Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al. (1991) <doi:10.1007/BF00116466>), the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>) and the localised model (Lee et al. (2015) <doi:10.1002/env.2348>). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.
Author: Duncan Lee
Maintainer: Duncan Lee <Duncan.Lee@glasgow.ac.uk>

Diff between CARBayes versions 5.2 dated 2020-03-13 and 5.2.2 dated 2021-01-15

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More information about CARBayes at CRAN
Permanent link

Package baseflow updated to version 0.12.3 with previous version 0.12.2 dated 2021-01-05

Title: Computes Hydrograph Separation
Description: Computes hydrograph separation using the conceptual automated process from Pelletier and Andreassian (2020) <doi:10.5194/hess-24-1171-2020>. Contains scalar and vectorized functions to compute correlation criterion to calibrate the baseflow separation model.
Author: Antoine Pelletier [cre, aut] (<https://orcid.org/0000-0002-1286-9147>), Vazken Andréassian [ths, rev] (<https://orcid.org/0000-0001-7124-9303>), Olivier Delaigue [rev, ctb, dtc] (<https://orcid.org/0000-0002-7668-8468>)
Maintainer: Antoine Pelletier <antoine.pelletier@inrae.fr>

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Permanent link

Package pixiedust updated to version 0.9.1 with previous version 0.9.0 dated 2020-05-15

Title: Tables so Beautifully Fine-Tuned You Will Believe It's Magic
Description: The introduction of the 'broom' package has made converting model objects into data frames as simple as a single function. While the 'broom' package focuses on providing tidy data frames that can be used in advanced analysis, it deliberately stops short of providing functionality for reporting models in publication-ready tables. 'pixiedust' provides this functionality with a programming interface intended to be similar to 'ggplot2's system of layers with fine tuned control over each cell of the table. Options for output include printing to the console and to the common markdown formats (markdown, HTML, and LaTeX). With a little 'pixiedust' (and happy thoughts) tables can really fly.
Author: Benjamin Nutter [aut, cre], David Kretch [ctb]
Maintainer: Benjamin Nutter <benjamin.nutter@gmail.com>

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More information about pixiedust at CRAN
Permanent link

Package fansi updated to version 0.4.2 with previous version 0.4.1 dated 2020-01-08

Title: ANSI Control Sequence Aware String Functions
Description: Counterparts to R string manipulation functions that account for the effects of ANSI text formatting control sequences.
Author: Brodie Gaslam [aut, cre], Elliott Sales De Andrade [ctb], R Core Team [cph] (UTF8 byte length calcs from src/util.c)
Maintainer: Brodie Gaslam <brodie.gaslam@yahoo.com>

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Package rwalkr updated to version 0.5.4 with previous version 0.5.3 dated 2020-06-19

Title: API to Melbourne Open Data Portal
Description: Provides API to Melbourne pedestrian and weather data <https://data.melbourne.vic.gov.au> in tidy data form.
Author: Earo Wang [aut, cre] (<https://orcid.org/0000-0001-6448-5260>), Stuart Lee [aut] (<https://orcid.org/0000-0003-1179-8436>)
Maintainer: Earo Wang <earo.wang@gmail.com>

Diff between rwalkr versions 0.5.3 dated 2020-06-19 and 0.5.4 dated 2021-01-15

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New package Rthingsboard with initial version 0.2.2
Package: Rthingsboard
Type: Package
Title: 'ThingsBoard' API
Version: 0.2.2
Date: 2021-01-13
Authors@R: person(given = "David", family = "Dorchies", role = c("aut", "cre"), email = "david.dorchies@inrae.fr", comment = c(ORCID = "0000-0002-6595-7984"))
Description: The goal of 'Rthingsboard' is to provide interaction with the API of 'ThingsBoard' (<https://thingsboard.io/>), an open-source IoT platform for device management, data collection, processing and visualization.
License: AGPL-3
Encoding: UTF-8
LazyData: true
Imports: httr, logger, methods, dplyr
Suggests: ggplot2, testthat
RoxygenNote: 7.1.1
URL: https://github.com/DDorch/Rthingsboard
BugReports: https://github.com/DDorch/Rthingsboard/issues
NeedsCompilation: no
Packaged: 2021-01-13 16:24:25 UTC; david.dorchies
Author: David Dorchies [aut, cre] (<https://orcid.org/0000-0002-6595-7984>)
Maintainer: David Dorchies <david.dorchies@inrae.fr>
Repository: CRAN
Date/Publication: 2021-01-15 10:30:03 UTC

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Package pencal updated to version 0.2.2 with previous version 0.2.1 dated 2021-01-06

Title: Penalized Regression Calibration (PRC)
Description: Computes the penalized regression calibration (PRC) method, that allows to predict survival using high-dimensional longitudinal predictors. PRC is described in Signorelli et al. (in review, arXiv preprint: <arXiv:2101.04426>)).
Author: Mirko Signorelli [aut, cre, cph] (<https://orcid.org/0000-0002-8102-3356>), Pietro Spitali [ctb], Roula Tsonaka [ctb]
Maintainer: Mirko Signorelli <msignorelli.rpackages@gmail.com>

Diff between pencal versions 0.2.1 dated 2021-01-06 and 0.2.2 dated 2021-01-15

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New package IDF with initial version 2.0.0
Package: IDF
Type: Package
Title: Estimation and Plotting of IDF Curves
Version: 2.0.0
Date: 2021-01-15
Authors@R: c(person("Jana", "Ulrich", email = "jana.ulrich@met.fu-berlin.de", role = c("aut", "cre")), person("Laura","Mack", email= "laura.mack@fu-berlin.de",role="ctb"), person("Oscar E.","Jurado", email= "jurado@zedat.fu-berlin.de",role="ctb"), person("Christoph", "Ritschel", role = "aut"), person("Carola", "Detring", role = "ctb"), person("Sarah", "Joedicke", role = "ctb"))
Description: Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package 'IDF' provides functions to estimate IDF parameters for given precipitation time series on the basis of a duration-dependent generalized extreme value distribution [Koutsoyiannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>].
Author: Jana Ulrich [aut, cre], Laura Mack [ctb], Oscar E. Jurado [ctb], Christoph Ritschel [aut], Carola Detring [ctb], Sarah Joedicke [ctb]
Maintainer: Jana Ulrich <jana.ulrich@met.fu-berlin.de>
Imports: stats, evd, ismev, RcppRoll, pbapply, fastmatch
License: GPL (>= 2)
Encoding: UTF-8
URL: https://gitlab.met.fu-berlin.de/Rpackages/idf_package
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-15 09:59:13 UTC; janaulrich
Repository: CRAN
Date/Publication: 2021-01-15 10:20:05 UTC

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Package highfrequency updated to version 0.8.0.1 with previous version 0.8.0 dated 2021-01-09

Title: Tools for Highfrequency Data Analysis
Description: Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity.
Author: Kris Boudt [aut, cre] (<https://orcid.org/0000-0002-1000-5142>), Jonathan Cornelissen [aut], Scott Payseur [aut], Giang Nguyen [ctb], Onno Kleen [aut] (<https://orcid.org/0000-0003-4731-4640>), Emil Sjoerup [aut]
Maintainer: Kris Boudt <kris.boudt@ugent.be>

Diff between highfrequency versions 0.8.0 dated 2021-01-09 and 0.8.0.1 dated 2021-01-15

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New package ExhaustiveSearch with initial version 1.0.0
Package: ExhaustiveSearch
Type: Package
Title: A Fast and Scalable Exhaustive Feature Selection Framework
Version: 1.0.0
Authors@R: c(person(given = "Rudolf", family = "Jagdhuber", role = c("aut", "cre"), email = "r.jagdhuber@gmail.com"), person(given = "Jorge", family = "Nocedal", role = "cph", comment = "lbfgs c library"), person(given = "Naoaki", family = "Okazaki", role = "cph", comment = "lbfgs c library"))
Description: The goal of this package is to provide an easy to use, fast and scalable exhaustive search framework. Exhaustive feature selections typically require a very large number of models to be fitted and evaluated. Execution speed and memory management are crucial factors here. This package provides solutions for both. Execution speed is optimized by using a multi-threaded C++ backend, and memory issues are solved by by only storing the best results during execution and thus keeping memory usage constant.
License: GPL (>= 3)
Encoding: UTF-8
Imports: Rcpp
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.1.1
URL: https://github.com/RudolfJagdhuber/ExhaustiveSearch
BugReports: https://github.com/RudolfJagdhuber/ExhaustiveSearch/issues
Suggests: mlbench
NeedsCompilation: yes
Packaged: 2021-01-13 14:41:58 UTC; Rudi
Author: Rudolf Jagdhuber [aut, cre], Jorge Nocedal [cph] (lbfgs c library), Naoaki Okazaki [cph] (lbfgs c library)
Maintainer: Rudolf Jagdhuber <r.jagdhuber@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-15 10:20:09 UTC

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New package ExamPAData with initial version 0.2.0
Package: ExamPAData
Type: Package
Title: Data Sets for Predictive Analytics Exam
Version: 0.2.0
Authors@R: c( person("Guanglai", "Li", email = "liguanglai@gmail.com", role = c("aut", "cre")), person("Sam", "Castillo", role = "aut") )
Date: 2021-01-12
Description: Contains all data sets for Exam PA: Predictive Analytics at <https://exampa.net/>.
URL: https://github.com/sdcastillo/ExamPAData
BugReports: https://github.com/sdcastillo/ExamPAData/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-12 21:46:36 UTC; gl
Author: Guanglai Li [aut, cre], Sam Castillo [aut]
Maintainer: Guanglai Li <liguanglai@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-15 10:20:12 UTC

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Package DBI updated to version 1.1.1 with previous version 1.1.0 dated 2019-12-15

Title: R Database Interface
Description: A database interface definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations.
Author: R Special Interest Group on Databases (R-SIG-DB) [aut], Hadley Wickham [aut], Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), R Consortium [fnd]
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

Diff between DBI versions 1.1.0 dated 2019-12-15 and 1.1.1 dated 2021-01-15

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New package bmabasket with initial version 0.1.0
Package: bmabasket
Type: Package
Title: Bayesian Model Averaging for Basket Trials
Version: 0.1.0
Date: 2021-01-09
Authors@R: c( person("Matt", "Psioda", email = "matt_psioda@unc.edu", role = "cre"), person("Ethan", "Alt", email = "ethanalt@live.unc.edu", role = "aut"))
Maintainer: Matt Psioda <matt_psioda@unc.edu>
Description: An implementation of the Bayesian model averaging method of Psioda and others (2019) <doi:10.1093/biostatistics/kxz014> for basket trials. Contains a user-friendly wrapper for simulating basket trials under conditions and analyzing them with a Bayesian model averaging approach.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.3), partitions
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
URL: https://github.com/ethan-alt/bmabasket
BugReports: https://github.com/ethan-alt/bmabasket/issues
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-01-12 20:17:40 UTC; ethanalt
Author: Matt Psioda [cre], Ethan Alt [aut]
Repository: CRAN
Date/Publication: 2021-01-15 10:20:02 UTC

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New package sparsevb with initial version 0.1.0
Package: sparsevb
Type: Package
Title: Spike-and-Slab Variational Bayes for Linear and Logistic Regression
Version: 0.1.0
Date: 2021-1-04
Authors@R: c(person("Gabriel", "Clara", email = "gabriel.j.clara@gmail.com", role = c("aut", "cre")), person("Botond", "Szabo", role = "aut"), person("Kolyan", "Ray", role = "aut"))
Author: Gabriel Clara [aut, cre], Botond Szabo [aut], Kolyan Ray [aut]
Maintainer: Gabriel Clara <gabriel.j.clara@gmail.com>
Description: Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <arXiv:2010.11665>.
BugReports: https://gitlab.com/gclara/varpack/-/issues
License: GPL (>= 3)
Imports: Rcpp (>= 1.0.5), selectiveInference (>= 1.2.5), glmnet (>= 4.0-2), stats
LinkingTo: Rcpp, RcppArmadillo, RcppEnsmallen
SystemRequirements: C++11
Encoding: UTF-8
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-01-12 04:11:21 UTC; gclara
Repository: CRAN
Date/Publication: 2021-01-15 09:20:02 UTC

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Package scrobbler updated to version 1.0.1 with previous version 0.2.15 dated 2020-02-24

Title: Download 'Scrobbles' from 'Last.fm'
Description: 'Last.fm'<https://www.last.fm> is a music platform focussed on building a detailed profile of a users listening habits. It does this by 'scrobbling' (recording) every track you listen to on other platforms ('spotify', 'youtube', 'soundcloud' etc) and transferring them to your 'Last.fm' database. This allows 'Last.fm' to act as a complete record of your entire listening history. 'scrobbler' provides helper functions to download and analyse your listening history in R.
Author: Conor Neilson
Maintainer: Conor Neilson <condwanaland@gmail.com>

Diff between scrobbler versions 0.2.15 dated 2020-02-24 and 1.0.1 dated 2021-01-15

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Package rvkstat updated to version 3.0.0 with previous version 2.6.3 dated 2019-12-11

Title: R Interface to API 'vk.com'
Description: Load data from vk.com api about your communiti users and views, ads performance, post on user wall and etc. For more information see API Documentation <https://vk.com/dev/first_guide>.
Author: Alexey Seleznev
Maintainer: Alexey Seleznev <selesnow@gmail.com>

Diff between rvkstat versions 2.6.3 dated 2019-12-11 and 3.0.0 dated 2021-01-15

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 rvkstat-3.0.0/rvkstat/README.md                      |  152 ++++---------
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Package ridge updated to version 2.8 with previous version 2.7 dated 2020-09-04

Title: Ridge Regression with Automatic Selection of the Penalty Parameter
Description: Linear and logistic ridge regression functions. Additionally includes special functions for genome-wide single-nucleotide polymorphism (SNP) data.
Author: Steffen Moritz [aut, cre] (<https://orcid.org/0000-0002-0085-1804>), Erika Cule [aut], Dan Frankowski [aut]
Maintainer: Steffen Moritz <steffen.moritz10@gmail.com>

Diff between ridge versions 2.7 dated 2020-09-04 and 2.8 dated 2021-01-15

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 5 files changed, 18 insertions(+), 15 deletions(-)

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Package lilikoi updated to version 2.0.4 with previous version 2.0.3 dated 2020-11-10

Title: Metabolomics Personalized Pathway Analysis Tool
Description: A comprehensive analysis tool for metabolomics data. It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton <http://www.metabolomics-forum.com/index.php?topic=281.0> Jeff Xia <https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R> Travers Ching, Xun Zhu, Lana X. Garmire (2018) <doi:10.1371/journal.pcbi.1006076>.
Author: Xinying Fang [aut], Yu Liu [aut], Zhijie Ren [aut], Fadhl Alakwaa [aut], Sijia Huang [aut], Lana Garmire [aut, cre]
Maintainer: Lana Garmire <lana.garmire.group@gmail.com>

Diff between lilikoi versions 2.0.3 dated 2020-11-10 and 2.0.4 dated 2021-01-15

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 17 files changed, 65 insertions(+), 67 deletions(-)

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Package lactcurves updated to version 1.1.0 with previous version 1.0.0 dated 2019-08-20

Title: Lactation Curve Parameter Estimation
Description: AllCurves() runs multiple lactation curve models and extracts selection criteria for each model. This package summarises the most common lactation curve models from the last century and provides a tool for researchers to quickly decide on which model fits their data best to proceed with their analysis. Start parameters were optimized based on a dataset with 1.7 million Holstein-Friesian cows. If convergence fails, the start parameters need to be manually adjusted. The models included in the package are taken from: (1) Michaelis-Menten: Michaelis, L. and M.L. Menten (1913). <www.plantphys.info/plant_physiology/copyright/MichaelisMentenTranslation2.pdf> (1a) Michaelis-Menten (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). <doi:10.1017/S002185960007684X> (1b) Michaelis-Menten + exponential (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). <doi:10.1017/S002185960007684X> (2) Brody (1923): Brody, S., A.C. Ragsdale, and C.W. Turner (1923). <doi:10.1085/jgp.5.6.777> (3) Brody (1924): Brody, S., C.W. Tuner, and A.C. Ragsdale (1924). <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2140670/> (4) Schumacher: Schumacher, F.X. (1939) in Thornley, J.H.M. and J. France (2007). <https://books.google.com.au/books/about/Mathematical_Models_in_Agriculture.html?id=rlwBCRSHobcC&redir_esc=y> (4a) Schumacher (Lopez et al. 2015): Lopez, S. J. France, N.E. Odongo, R.A. McBride, E. Kebreab, O. AlZahal, B.W. McBride, and J. Dijkstra (2015). <doi:10.3168/jds.2014-8132> (5) Parabolic exponential (Adediran): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (6) Wood: Wood, P.D.P. (1967). <doi:10.1038/216164a0> (6a) Wood reparameterized (Dhanoa): Dhanoa, M.S. (1981). <doi:10.1017/S0003356100027276> (6b) Wood non-linear (Cappio-Borlino): Cappio-Borlino, A., G. Pulina, and G. Rossi (1995). <doi:10.1016/0921-4488(95)00713-U> (7) Quadratic Polynomial (Dave): Dave, B.K. (1971) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (8) Cobby and Le Du (Vargas): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). <doi:10.3168/jds.S0022-0302(00)75005-3> (9) Papajcsik and Bodero 1: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (10) Papajcsik and Bodero 2: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (11) Papajcsik and Bodero 3: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (12) Papajcsik and Bodero 4: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (13) Papajcsik and Bodero 6: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (14) Mixed log model 1 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (15) Mixed log model 3 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (16) Log-quadratic (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (17) Wilmink: J.B.M. Wilmink (1987). <doi:10.1016/0301-6226(87)90003-0> (17a) modified Wilmink (Jakobsen): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (17b) modified Wilmink (Laurenson & Strucken): Strucken E.M., Brockmann G.A., and Y.C.S.M. Laurenson (2019). <http://www.aaabg.org/aaabghome/AAABG23papers/35Strucken23139.pdf> (18) Bicompartemental (Ferguson and Boston 1993): Ferguson, J.D., and R. Boston (1993) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (19) Dijkstra: Dijkstra, J., J. France, M.S. Dhanoa, J.A. Maas, M.D. Hanigan, A.J. Rook, and D.E. Beever (1997). <doi:10.3168/jds.S0022-0302(97)76185-X> (20) Morant and Gnanasakthy (Pollott et al 2000): Pollott, G.E. and E. Gootwine (2000). <doi:10.1017/S1357729800055028> (21) Morant and Gnanasakthy (Vargas et al 2000): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). <doi:10.3168/jds.S0022-0302(00)75005-3> (22) Morant and Gnanasakthy (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (23) Khandekar (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (24) Ali and Schaeffer: Ali, T.E. and L.R. Schaeffer (1987). <https://cdnsciencepub.com/doi/pdf/10.4141/cjas87-067> (25) Fractional Polynomial (Elvira et al. 2013): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). <doi:10.1017/S175173111200239X> (26) Pollott multiplicative (Elvira): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). <doi:10.1017/S175173111200239X> (27) Pollott modified: Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (28) Monophasic Grossman: Grossman, M. and W.J. Koops (1988). <doi:10.3168/jds.S0022-0302(88)79723-4> (29) Monophasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (30) Diphasic (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (31) Diphasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (32) Legendre Polynomial (3th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (33) Legendre Polynomial (4th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (34) Legendre + Wilmink (Lidauer): Lidauer, M. and E.A. Mantysaari (1999). <https://journal.interbull.org/index.php/ib/article/view/417> (35) Natural Cubic Spline (3 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). <doi:10.3168/jds.S0022-0302(99)75277-X> (36) Natural Cubic Spline (4 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). <doi:10.3168/jds.S0022-0302(99)75277-X> (37) Natural Cubic Spline (5 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999) <doi:10.3168/jds.S0022-0302(99)75277-X> (38) Natural Cubic Spline (defined knots according to Harrell 2001): Jr. Harrell, F.E. (2001). <https://link.springer.com/book/10.1007/978-3-319-19425-7> The selection criteria measure the goodness of fit of the model and include: Residual standard error (RSE), R-square (R2), log likelihood, Akaike information criterion (AIC), Akaike information criterion corrected (AICC), Bayesian Information Criterion (BIC), Durbin Watson coefficient (DW). The following model parameters are included: Residual sum of squares (RSS), Residual standard deviation (RSD), F-value (F) based on F-ratio test.
Author: Eva M. Strucken
Maintainer: Eva M. Strucken <eva.strucken@gmail.com>

Diff between lactcurves versions 1.0.0 dated 2019-08-20 and 1.1.0 dated 2021-01-15

 DESCRIPTION                     |  145 ++++++---
 MD5                             |   12 
 NAMESPACE                       |    1 
 NEWS.md                         |    6 
 R/lactcurves_R-package.R        |  615 +++++++++++++++++++++++++++++++---------
 README.md                       |   20 +
 man/lactcurves_Documentation.Rd |   30 +
 7 files changed, 642 insertions(+), 187 deletions(-)

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New package eFRED with initial version 0.1.0
Package: eFRED
Title: Fetch Data from the Federal Reserve Economic Database
Version: 0.1.0
Author: Chris Mann <cmann3@unl.edu>
Maintainer: Chris Mann <cmann3@unl.edu>
Description: Interact with the FRED API, <https://fred.stlouisfed.org/docs/api/fred/>, to fetch observations across economic series; find information about different economic sources, releases, series, etc.; conduct searches by series name, attributes, or tags; and determine the latest updates. Includes functions for creating panels of related variables with minimal effort and datasets containing data sources, releases, and popular FRED tags.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: jsonlite, httr, R (>= 2.10)
Suggests: knitr, rmarkdown, datasets
VignetteBuilder: knitr
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2021-01-12 13:01:24 UTC; chris
Repository: CRAN
Date/Publication: 2021-01-15 09:40:13 UTC

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New package autostsm with initial version 1.0
Package: autostsm
Type: Package
Title: Automatic Structural Time Series Models
Version: 1.0
Date: 2021-01-11
Author: Alex Hubbard
Maintainer: Alex Hubbard <hubbard.alex@gmail.com>
Description: Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components using the Kalman filter. See Koopman, Siem Jan and Marios Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models <doi:10.1093/oxfordhb/9780195398649.013.0006>.
License: GPL (>= 2)
Imports: Matrix (>= 1.2), maxLik (>= 1.4), seastests (>= 0.14), forecast (>= 8.13), lubridate (>= 1.7), tsutils (>= 0.9), ggplot2 (>= 3.3), gridExtra (>= 2.3), strucchange (>= 1.5), imputeTS (>= 3.1), foreach (>= 1.5), doSNOW (>= 1.0), parallel (>= 4.0), zoo (>= 1.8)
Depends: R (>= 3.5.0), data.table (>= 1.13)
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2021-01-12 16:27:39 UTC; alex.hubbard
Repository: CRAN
Date/Publication: 2021-01-15 10:00:05 UTC

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New package arcpullr with initial version 0.1
Package: arcpullr
Type: Package
Title: Pull Data from an ArcGIS REST API
Version: 0.1
Authors@R: c(person(given = "Paul", family = "Frater", role = c("aut", "cre"), email = "paul.frater@wisconsin.gov", comment = c(ORCID = "0000-0002-7237-6563")), person("Zac", "Driscoll", email = "zdriscoll@mmsd.com", role = c("aut", "ctb"), comment = c(ORCID = "0000-0002-8233-0980")))
License: GPL-3
Encoding: UTF-8
LazyData: true
Description: Functions to efficiently query ArcGIS REST APIs <https://developers.arcgis.com/rest/>. Both spatial and SQL queries can be used to retrieve data. Simple Feature (sf) objects are utilized to perform spatial queries. This package was neither produced nor is maintained by Esri.
Depends: R (>= 3.6.0)
Imports: httr (>= 1.4.1), jsonlite (>= 1.6.1), sf (>= 0.9.5), dplyr (>= 1.0.2), ggplot2 (>= 3.3.0), tidyr (>= 1.0.2), rlang (>= 0.4.7)
RoxygenNote: 7.1.1
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-01-12 15:26:55 UTC; fratepnd
Author: Paul Frater [aut, cre] (<https://orcid.org/0000-0002-7237-6563>), Zac Driscoll [aut, ctb] (<https://orcid.org/0000-0002-8233-0980>)
Maintainer: Paul Frater <paul.frater@wisconsin.gov>
Repository: CRAN
Date/Publication: 2021-01-15 10:00:08 UTC

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New package activatr with initial version 0.1.0
Package: activatr
Type: Package
Title: Utilities for Parsing and Plotting Activities
Version: 0.1.0
Authors@R: person("Daniel", "Schafer", email = "dan.schafer@gmail.com", role = c("aut", "cre"))
Description: This contains helpful functions for parsing, managing, plotting, and visualizing activities, most often from GPX (GPS Exchange Format) files recorded by GPS devices. It allows easy parsing of the source files into standard R data formats, along with functions to compute derived data for the activity, and to plot the activity in a variety of ways.
License: MIT + file LICENSE
URL: https://github.com/dschafer/activatr
BugReports: https://github.com/dschafer/activatr/issues
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.0.0)
Imports: dplyr (>= 1.0.0), geosphere (>= 1.5), ggmap (>= 3.0.0), glue (>= 1.4.0), httr (>= 1.4.0), lubridate (>= 1.7.0), magrittr (>= 2.0.0), rlang (>= 0.4.0), tibble (>= 3.0.0), timetk (>= 2.6.0), xml2 (>= 1.3.2)
RoxygenNote: 7.1.1
Suggests: covr (>= 3.5.0), ggplot2 (>= 3.3.0), knitr (>= 1.30), mockery (>= 0.4.2), rmarkdown (>= 2.6), roxygen2 (>= 7.1.0), testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-01-12 04:41:36 UTC; dschafer
Author: Daniel Schafer [aut, cre]
Maintainer: Daniel Schafer <dan.schafer@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-15 09:20:05 UTC

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New package SeuratObject with initial version 4.0.0
Package: SeuratObject
Type: Package
Title: Data Structures for Single Cell Data
Version: 4.0.0
Date: 2021-01-07
Authors@R: c( person(given = 'Rahul', family = 'Satija', email = 'rsatija@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0001-9448-8833')), person(given = 'Andrew', family = 'Butler', email = 'abutler@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0003-3608-0463')), person(given = 'Paul', family = 'Hoffman', email = 'nygcSatijalab@nygenome.org', role = c('aut', 'cre'), comment = c(ORCID = '0000-0002-7693-8957')), person(given = 'Tim', family = 'Stuart', email = 'tstuart@nygenome.org', role = 'aut', comment = c(ORCID = '0000-0002-3044-0897')), person(given = 'Jeff', family = 'Farrell', email = 'jfarrell@g.harvard.edu', role = 'ctb'), person(given = 'Shiwei', family = 'Zheng', email = 'szheng@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-6682-6743')), person(given = 'Christoph', family = 'Hafemeister', email = 'chafemeister@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0001-6365-8254')), person(given = 'Patrick', family = 'Roelli', email = 'proelli@nygenome.org', role = 'ctb'), person(given = "Yuhan", family = "Hao", email = 'yhao@nygenome.org', role = 'ctb', comment = c(ORCID = '0000-0002-1810-0822')) )
Description: Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for more details.
URL: https://satijalab.org/seurat, https://github.com/mojaveazure/seurat-object
BugReports: https://github.com/mojaveazure/seurat-object/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 3.5.0)
Imports: grDevices, grid, Matrix (>= 1.2.18), methods, Rcpp (>= 1.0.5), rlang (>= 0.4.7), sctransform, stats, tools, utils
Suggests: tinytest
Collate: 'RcppExports.R' 'utils.R' 'zzz.R' 'generics.R' 'assay.R' 'command.R' 'data.R' 'default.R' 'jackstraw.R' 'dimreduc.R' 'graph.R' 'neighbor.R' 'spatial.R' 'seurat.R'
LinkingTo: Rcpp, RcppEigen
NeedsCompilation: yes
Packaged: 2021-01-11 19:28:57 UTC; paul
Author: Rahul Satija [aut] (<https://orcid.org/0000-0001-9448-8833>), Andrew Butler [aut] (<https://orcid.org/0000-0003-3608-0463>), Paul Hoffman [aut, cre] (<https://orcid.org/0000-0002-7693-8957>), Tim Stuart [aut] (<https://orcid.org/0000-0002-3044-0897>), Jeff Farrell [ctb], Shiwei Zheng [ctb] (<https://orcid.org/0000-0001-6682-6743>), Christoph Hafemeister [ctb] (<https://orcid.org/0000-0001-6365-8254>), Patrick Roelli [ctb], Yuhan Hao [ctb] (<https://orcid.org/0000-0002-1810-0822>)
Maintainer: Paul Hoffman <nygcSatijalab@nygenome.org>
Repository: CRAN
Date/Publication: 2021-01-15 08:50:02 UTC

More information about SeuratObject at CRAN
Permanent link

Package rGEDI (with last version 0.1.9) was removed from CRAN

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

2020-10-23 0.1.9
2020-07-15 0.1.8
2020-05-07 0.1.7

Permanent link
Package pmml updated to version 2.4.0 with previous version 2.3.1 dated 2020-04-22

Title: Generate PMML for Various Models
Description: The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://dmg.org/>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms. The package isofor (used for anomaly detection) can be installed with devtools::install_github("Zelazny7/isofor").
Author: Dmitriy Bolotov [aut, cre], Tridivesh Jena [aut], Graham Williams [aut], Wen-Ching Lin [aut], Michael Hahsler [aut], Hemant Ishwaran [aut], Udaya B. Kogalur [aut], Rajarshi Guha [aut], Software AG [cph]
Maintainer: Dmitriy Bolotov <dmitriy.bolotov@softwareag.com>

Diff between pmml versions 2.3.1 dated 2020-04-22 and 2.4.0 dated 2021-01-15

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Package spaMM updated to version 3.6.0 with previous version 3.5.0 dated 2020-10-03

Title: Mixed-Effect Models, Particularly Spatial Models
Description: Inference based on mixed-effect models, including generalized linear mixed models with spatial correlations and models with non-Gaussian random effects (e.g., Beta). Various approximations of likelihood or restricted likelihood are implemented, in particular Laplace approximation and h-likelihood (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>). Both classical geostatistical models, and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model.
Author: François Rousset [aut, cre, cph] (<https://orcid.org/0000-0003-4670-0371>), Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [aut] (<https://orcid.org/0000-0003-0637-2959>), GSL authors [ctb] (src/gsl_bessel.*)
Maintainer: François Rousset <francois.rousset@umontpellier.fr>

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More information about spaMM at CRAN
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Package RaSEn updated to version 2.0.0 with previous version 1.1.0 dated 2020-10-21

Title: Random Subspace Ensemble Classification and Variable Screening
Description: We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function available for both regression and classification.
Author: Ye Tian [aut, cre] and Yang Feng [aut]
Maintainer: Ye Tian <ye.t@columbia.edu>

Diff between RaSEn versions 1.1.0 dated 2020-10-21 and 2.0.0 dated 2021-01-15

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Package modelbased updated to version 0.5.0 with previous version 0.4.0 dated 2020-12-06

Title: Estimation of Model-Based Predictions, Contrasts and Means
Description: Implements a general interface for model-based estimations for a wide variety of models (see support list of insight; Lüdecke, Waggoner & Makowski (2019) <doi:10.21105/joss.01412>), used in the computation of marginal means, contrast analysis and predictions.
Author: Dominique Makowski [aut, cre] (<https://orcid.org/0000-0001-5375-9967>), Daniel Lüdecke [aut] (<https://orcid.org/0000-0002-8895-3206>), Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>)
Maintainer: Dominique Makowski <dom.makowski@gmail.com>

Diff between modelbased versions 0.4.0 dated 2020-12-06 and 0.5.0 dated 2021-01-15

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Package weathercan updated to version 0.5.0 with previous version 0.4.0 dated 2020-09-02

Title: Download Weather Data from Environment and Climate Change Canada
Description: Provides means for downloading historical weather data from the Environment and Climate Change Canada website (<https://climate.weather.gc.ca/historical_data/search_historic_data_e.html>). Data can be downloaded from multiple stations and over large date ranges and automatically processed into a single dataset. Tools are also provided to identify stations either by name or proximity to a location.
Author: Steffi LaZerte [aut, cre] (<https://orcid.org/0000-0002-7690-8360>), Sam Albers [ctb] (<https://orcid.org/0000-0002-9270-7884>), Nick Brown [ctb] (<https://orcid.org/0000-0002-2719-0671>)
Maintainer: Steffi LaZerte <steffi@steffi.ca>

Diff between weathercan versions 0.4.0 dated 2020-09-02 and 0.5.0 dated 2021-01-15

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Package taxizedb updated to version 0.3.0 with previous version 0.2.2 dated 2020-08-26

Title: Tools for Working with 'Taxonomic' Databases
Description: Tools for working with 'taxonomic' databases, including utilities for downloading databases, loading them into various 'SQL' databases, cleaning up files, and providing a 'SQL' connection that can be used to do 'SQL' queries directly or used in 'dplyr'.
Author: Scott Chamberlain [aut, cre], Zebulun Arendsee [aut]
Maintainer: Scott Chamberlain <myrmecocystus+r@gmail.com>

Diff between taxizedb versions 0.2.2 dated 2020-08-26 and 0.3.0 dated 2021-01-15

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Package rFIA updated to version 0.3.1 with previous version 0.3.0 dated 2020-12-07

Title: Space-Time Estimation of Forest Variables using the FIA Database
Description: The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatio-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted estimators to improve population, change, and ratio estimates.
Author: Hunter Stanke [aut, cre], Andrew Finley [aut]
Maintainer: Hunter Stanke <stankehu@msu.edu>

Diff between rFIA versions 0.3.0 dated 2020-12-07 and 0.3.1 dated 2021-01-15

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Package bdvis updated to version 0.2.28 with previous version 0.2.22 dated 2018-09-26

Title: Biodiversity Data Visualizations
Description: Provides a set of functions to create basic visualizations to quickly preview different aspects of biodiversity information such as inventory completeness, extent of coverage (taxonomic, temporal and geographic), gaps and biases.
Author: Vijay Barve [aut, cre] (<https://orcid.org/0000-0002-4852-2567>), Javier Otegui [aut] (<https://orcid.org/0000-0002-8997-3559>)
Maintainer: Vijay Barve <vijay.barve@gmail.com>

Diff between bdvis versions 0.2.22 dated 2018-09-26 and 0.2.28 dated 2021-01-15

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