Mon, 12 Apr 2021

Package readsparse updated to version 0.1.2-1 with previous version 0.1.2 dated 2021-04-07

Title: Read and Write Sparse Matrices in 'SVMLight' and 'LibSVM' Formats
Description: Read and write labelled sparse matrices in text format as used by software such as 'SVMLight', 'LibSVM', 'ThunderSVM', 'LibFM', 'xLearn', 'XGBoost', 'LightGBM', and others. Supports labelled data for regression, classification (binary, multi-class, multi-label), and ranking (with 'qid' field), and can handle header metadata and comments in files.
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>

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Package msmtools updated to version 2.0.1 with previous version 2.0.0 dated 2021-04-07

Title: Building Augmented Data to Run Multi-State Models with 'msm' Package
Description: A fast and general method for restructuring classical longitudinal data into augmented ones. The reason for this is to facilitate the modeling of longitudinal data under a multi-state framework using the 'msm' package.
Author: Francesco Grossetti [aut, cre] (<https://orcid.org/0000-0002-5130-7745>)
Maintainer: Francesco Grossetti <francesco.grossetti@unibocconi.it>

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Package regmed updated to version 2.0.4 with previous version 1.1.0 dated 2020-06-05

Title: Regularized Mediation Analysis
Description: Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).
Author: Jason Sinnwell [aut, cre] (<https://orcid.org/0000-0003-1964-5522>), Daniel Schaid [aut] (<https://orcid.org/0000-0003-1457-6433>)
Maintainer: Jason Sinnwell <sinnwell.jason@mayo.edu>

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Package mlr3misc updated to version 0.9.0 with previous version 0.8.0 dated 2021-03-19

Title: Helper Functions for 'mlr3'
Description: Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>)
Maintainer: Michel Lang <michellang@gmail.com>

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Package dyngen updated to version 1.0.1 with previous version 1.0.0 dated 2021-02-23

Title: A Multi-Modal Simulator for Spearheading Single-Cell Omics Analyses
Description: A novel, multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. 'dyngen' is more flexible than current single-cell simulation engines. It allows better method development and benchmarking, thereby stimulating development and testing of novel computational methods. Cannoodt et al. (2020) <doi:10.1101/2020.02.06.936971>.
Author: Robrecht Cannoodt [aut, cre, cph] (<https://orcid.org/0000-0003-3641-729X>), Wouter Saelens [aut] (<https://orcid.org/0000-0002-7114-6248>)
Maintainer: Robrecht Cannoodt <rcannood@gmail.com>

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Package radsafer updated to version 2.2.5 with previous version 2.2.4 dated 2021-01-04

Title: Radiation Safety
Description: Provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) <doi:10.2172/1419730>).
Author: Mark Hogue <mark.hogue.chp@gmail.com>
Maintainer: Mark Hogue <mark.hogue.chp@gmail.com>

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Package troopdata updated to version 0.1.3 with previous version 0.1.2 dated 2021-03-02

Title: Tools for Analyzing Cross-National Military Deployment and Basing Data
Description: These functions generate data frames on troop deployments and military basing using U.S. Department of Defense data on overseas military deployments. This package provides functions for pulling country-year troop deployment and basing data. Subsequent versions will hopefully include cross-national data on deploying countries.
Author: Michael Flynn [aut, cre] (<https://orcid.org/0000-0002-6064-740X>)
Maintainer: Michael Flynn <meflynn@ksu.edu>

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Package Microsoft365R updated to version 2.1.0 with previous version 2.0.0 dated 2021-02-26

Title: Interface to the 'Microsoft 365' Suite of Cloud Services
Description: An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders.
Author: Hong Ooi [aut, cre], Microsoft [cph]
Maintainer: Hong Ooi <hongooi73@gmail.com>

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Package ggstatsplot updated to version 0.7.2 with previous version 0.7.1 dated 2021-03-11

Title: 'ggplot2' Based Plots with Statistical Details
Description: Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.
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 campfin updated to version 1.0.7 with previous version 1.0.6 dated 2021-03-04

Title: Wrangle Campaign Finance Data
Description: Explore and normalize American campaign finance data. Created by the Investigative Reporting Workshop to facilitate work on The Accountability Project, an effort to collect public data into a central, standard database that is more easily searched: <https://publicaccountability.org/>.
Author: Kiernan Nicholls [aut, cre, cph], Investigative Reporting Workshop [cph], Yanqi Xu [aut], Schuyler Erle [cph]
Maintainer: Kiernan Nicholls <kiernann@protonmail.com>

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Package predictoR updated to version 1.1.9 with previous version 1.1.7 dated 2021-03-04

Title: Predictive Data Analysis System
Description: Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
Author: Oldemar Rodriguez [aut, cre], Andrés Navarro [ctb, prg], Diego Jiménez [ctb, prg], Joseline Quirós [ctb, prg]
Maintainer: Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr>

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Package loa updated to version 0.2.47.1 with previous version 0.2.46.3 dated 2021-01-16

Title: Lattice Options and Add-Ins
Description: Various plots and functions that make use of the lattice/trellis plotting framework. The plots, which include loaPlot(), RgoogleMapsPlot() and trianglePlot(), use panelPal(), a function that extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel and panel-to-key synchronization/management.
Author: Karl Ropkins
Maintainer: Karl Ropkins <karl.ropkins@gmail.com>

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Package GJRM updated to version 0.2-4 with previous version 0.2-3 dated 2020-09-07

Title: Generalised Joint Regression Modelling
Description: Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Author: Giampiero Marra <giampiero.marra@ucl.ac.uk> and Rosalba Radice <rosalba.radice@city.ac.uk>
Maintainer: Giampiero Marra <giampiero.marra@ucl.ac.uk>

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Package garchmodels updated to version 0.1.1 with previous version 0.1.0 dated 2021-04-10

Title: The 'Tidymodels' Extension for GARCH Models
Description: Garch framework for use with the 'tidymodels' ecosystem. It includes both univariate and multivariate methods from the 'rugarch' and 'rmgarch' packages. These models include DCC-Garch, Copula Garch and Go-GARCH among others.
Author: Alberto Almuiña [aut, cre]
Maintainer: Alberto Almuiña <albertogonzalezalmuinha@gmail.com>

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Package fwildclusterboot updated to version 0.3.3 with previous version 0.3.2 dated 2021-02-26

Title: Fast Wild Cluster Bootstrap Inference for Linear Regression Models
Description: Implementation of the fast algorithm for wild cluster bootstrap inference developed in Roodman et al (2019, STATA Journal) for linear regression models <https://journals.sagepub.com/doi/full/10.1177/1536867X19830877>, which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples - as long as the number of bootstrapping clusters is not too large. Multiway clustering, regression weights, bootstrap weights, fixed effects and subcluster bootstrapping are supported. Further, both restricted (WCR) and unrestricted (WCU) bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe').
Author: Alexander Fischer [aut, cre], David Roodman [aut], Achim Zeileis [ctb] (Author of included sandwich fragments), Nathaniel Graham [ctb] (Contributor to included sandwich fragments), Susanne Koell [ctb] (Contributor to included sandwich fragments), Laurent Berge [ctb] (Author of included fixest fragments), Sebastian Krantz [ctb]
Maintainer: Alexander Fischer <alexander-fischer1801@t-online.de>

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Package clock updated to version 0.2.0 with previous version 0.1.0 dated 2021-03-31

Title: Date-Time Types and Tools
Description: Provides a comprehensive library for date-time manipulations using a new family of orthogonal date-time classes (durations, time points, zoned-times, and calendars) that partition responsibilities so that the complexities of time zones are only considered when they are really needed. Capabilities include: date-time parsing, formatting, arithmetic, extraction and updating of components, and rounding.
Author: Davis Vaughan [aut, cre], Howard Hinnant [cph] (Author of the included date library), RStudio [cph, fnd]
Maintainer: Davis Vaughan <davis@rstudio.com>

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Package babynames updated to version 1.0.1 with previous version 1.0.0 dated 2019-01-12

Title: US Baby Names 1880-2017
Description: US baby names provided by the SSA. This package contains all names used for at least 5 children of either sex.
Author: Hadley Wickham [aut, cre], RStudio [cph]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between babynames versions 1.0.0 dated 2019-01-12 and 1.0.1 dated 2021-04-12

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New package varrank with initial version 0.4
Package: varrank
Title: Heuristics Tools Based on Mutual Information for Variable Ranking
Version: 0.4
Authors@R: c(person("Gilles", "Kratzer", role = c("aut", "cre"), email = "gilles.kratzer@math.uzh.ch", comment = c(ORCID = "0000-0002-5929-8935")), person("Reinhard", "Furrer", role = c("ctb"), email = "reinhard.furrer@math.uzh.ch", comment = c(ORCID = "0000-0002-6319-2332")))
Author: Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>), Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>)
Maintainer: Gilles Kratzer <gilles.kratzer@math.uzh.ch>
Description: A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) <doi:10.1109/72.298224>. Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.
Encoding: UTF-8
License: GPL-3
LazyData: true
Suggests: Boruta, FSelector, caret, e1071, mlbench, psych, varSelRF, gplots, entropy, testthat, knitr, markdown
URL: https://www.math.uzh.ch/pages/varrank/
Imports: stats, FNN, grDevices
Depends: R (>= 3.5.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-04-12 09:31:50 UTC; Kratzer
Repository: CRAN
Date/Publication: 2021-04-12 16:50:02 UTC

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Package Sojourn updated to version 1.0.2 with previous version 0.1.0 dated 2019-05-06

Title: Apply Sojourn Methods for Processing ActiGraph Accelerometer Data
Description: Provides a simple way for utilizing Sojourn methods for accelerometer processing, as detailed in Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014) <doi:10.1249/MSS.0b013e3182a42a2d>, Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016) <doi:10.1249/MSS.0000000000000915>, and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>. The accompanying Sojourn.Data package, if not available on CRAN, can be accessed from <https://github.com/paulhibbing/Sojourn.Data>.
Author: Paul R. Hibbing [aut, cre], Kate Lyden [aut], Isaac J. Schwabacher [aut]
Maintainer: Paul R. Hibbing <paulhibbing@gmail.com>

Diff between Sojourn versions 0.1.0 dated 2019-05-06 and 1.0.2 dated 2021-04-12

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New package NFP with initial version 0.99.4
Package: NFP
Title: Network Fingerprint Framework in R
Version: 0.99.4
Authors@R: c( person("Yang", "Cao",email = "caoyang.name@gmail.com",role = c("aut","cre" )), person("Fei", "Li", email = "pittacus@gmail.com", role = "aut") )
Maintainer: Yang Cao <caoyang.name@gmail.com>
Description: An implementation of the network fingerprint framework that introduced in paper "Network fingerprint: a knowledge-based characterization of biomedical networks" (Cui, 2015) <doi:10.1038/srep13286>. This method worked by making systematic comparisons to a set of well-studied "basic networks", measuring both the functional and topological similarity. A biological could be characterized as a spectrum-like vector consisting of similarities to basic networks. It shows great potential in biological network study.
Depends: R (>= 3.2.0), graph
Imports: igraph, stringr, magrittr, plyr, ggplot2, apcluster, KEGGgraph, dplyr, tidyr, methods
Suggests: knitr, testthat, graphite, NFPdata, rmarkdown
Additional_repositories: https://yiluheihei.github.io/datarepo/
VignetteBuilder: knitr
License: GPL (>= 2)
LazyData: yes
LazyDataCompression: xz
NeedsCompilation: no
URL: https://github.com/yiluheihei/NFP
BugReports: https://github.com/yiluheihei/NFP/issues
Packaged: 2021-04-12 11:23:16 UTC; caoyang
Author: Yang Cao [aut, cre], Fei Li [aut]
Repository: CRAN
Date/Publication: 2021-04-12 16:30:02 UTC

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Package plotKML updated to version 0.8-0 with previous version 0.6-1 dated 2020-03-09

Title: Visualization of Spatial and Spatio-Temporal Objects in Google Earth
Description: Writes sp-class, spacetime-class, raster-class and similar spatial and spatio-temporal objects to KML following some basic cartographic rules.
Author: Tomislav Hengl [cre, aut], Andrea Gilardi [ctb], Pierre Roudier [ctb], Dylan Beaudette [ctb], Edzer Pebesma [ctb], Michael Blaschek [ctb]
Maintainer: Tomislav Hengl <tom.hengl@opengeohub.org>

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Package nycflights13 updated to version 1.0.2 with previous version 1.0.1 dated 2019-09-16

Title: Flights that Departed NYC in 2013
Description: Airline on-time data for all flights departing NYC in 2013. Also includes useful 'metadata' on airlines, airports, weather, and planes.
Author: Hadley Wickham [aut, cre], RStudio [cph]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between nycflights13 versions 1.0.1 dated 2019-09-16 and 1.0.2 dated 2021-04-12

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Package inum updated to version 1.0-4 with previous version 1.0-3 dated 2021-02-08

Title: Interval and Enum-Type Representation of Vectors
Description: Enum-type representation of vectors and representation of intervals, including a method of coercing variables in data frames.
Author: Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between inum versions 1.0-3 dated 2021-02-08 and 1.0-4 dated 2021-04-12

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Package GWmodel updated to version 2.2-5 with previous version 2.2-4 dated 2021-02-17

Title: Geographically-Weighted Models
Description: Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.
Author: Binbin Lu[aut], Paul Harris[aut], Martin Charlton[aut], Chris Brunsdon[aut], Tomoki Nakaya[aut], Daisuke Murakami[aut],Isabella Gollini[ctb], Yigong Hu[ctb], Fiona H Evans[ctb]
Maintainer: Binbin Lu <binbinlu@whu.edu.cn>

Diff between GWmodel versions 2.2-4 dated 2021-02-17 and 2.2-5 dated 2021-04-12

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Package fbst updated to version 1.5 with previous version 1.4 dated 2021-03-09

Title: The Full Bayesian Evidence Test, Full Bayesian Significance Test and the e-Value
Description: Provides access to a range of functions for computing and visualizing the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative, and the Full Bayesian Evidence Test (FBET) and the (generalized) Bayesian evidence value for testing a composite (or interval) hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available. For details on the computation and theory of the FBST see <arXiv:2005.13181>.
Author: Riko Kelter
Maintainer: Riko Kelter <riko.kelter@uni-siegen.de>

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Package TreeTools updated to version 1.4.3 with previous version 1.4.2 dated 2021-01-26

Title: Create, Modify and Analyse Phylogenetic Trees
Description: Efficient implementations of functions for the creation, modification and analysis of phylogenetic trees. Applications include: generation of trees with specified shapes; analysis of tree shape; rooting of trees and extraction of subtrees; calculation and depiction of node support; calculation of ancestor-descendant relationships; import and export of trees from Newick, Nexus (Maddison et al. 1997) <doi:10.1093/sysbio/46.4.590>, and TNT <http://www.lillo.org.ar/phylogeny/tnt/> formats; and analysis of splits and cladistic information.
Author: Martin R. Smith [aut, cre, cph] (<https://orcid.org/0000-0001-5660-1727>), Emmanuel Paradis [cph] (<https://orcid.org/0000-0003-3092-2199>)
Maintainer: Martin R. Smith <martin.smith@durham.ac.uk>

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2016-09-14 0.1.0

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

2020-03-25 0.1.1
2020-02-29 0.1.0

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Package tsibble updated to version 1.0.1 with previous version 1.0.0 dated 2021-02-20

Title: Tidy Temporal Data Frames and Tools
Description: Provides a 'tbl_ts' class (the 'tsibble') for temporal data in an data- and model-oriented format. The 'tsibble' provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods.
Author: Earo Wang [aut, cre] (<https://orcid.org/0000-0001-6448-5260>), Di Cook [aut, ths] (<https://orcid.org/0000-0002-3813-7155>), Rob Hyndman [aut, ths] (<https://orcid.org/0000-0002-2140-5352>), Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>), Tyler Smith [ctb], Wil Davis [ctb]
Maintainer: Earo Wang <earo.wang@gmail.com>

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Package maSAE updated to version 2.0.3 with previous version 2.0.2 dated 2020-11-02

Title: Mandallaz' Model-Assisted Small Area Estimators
Description: An S4 implementation of the unbiased extension of the model- assisted synthetic-regression estimator proposed by Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013) <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014) <DOI:10.1139/cjfr-2013-0449>. It yields smaller variances than the standard bias correction, the generalised regression estimator.
Author: Andreas Dominik Cullmann [aut, cre], Daniel Mandallaz [ctb], Alexander Francis Massey [ctb]
Maintainer: Andreas Dominik Cullmann <fvafrcu@mailbox.org>

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Package esquisse updated to version 1.0.0 with previous version 0.3.1 dated 2020-09-27

Title: Explore and Visualize Your Data Interactively
Description: A 'shiny' gadget to create 'ggplot2' figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.
Author: Fanny Meyer [aut], Victor Perrier [aut, cre], Ian Carroll [ctb] (Faceting support), Xiangnan Dang [ctb] (Facets rows and cols, X/Y limits)
Maintainer: Victor Perrier <victor.perrier@dreamrs.fr>

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Package eq5d updated to version 0.9.0 with previous version 0.8.1 dated 2021-02-09

Title: Methods for Analysing 'EQ-5D' Data and Calculating 'EQ-5D' Index Scores
Description: EQ-5D is a popular health related quality of life instrument used in the clinical and economic evaluation of health care. Developed by the EuroQol group <https://euroqol.org/>, the instrument consists of two components: health state description and evaluation. For the description component a subject self-rates their health in terms of five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression using either a three-level (EQ-5D-3L, <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/>) or a five-level (EQ-5D-5L, <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/>) scale. Frequently the scores on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. The eq5d package provides methods to calculate index scores from a subject's dimension scores. 28 TTO and 11 VAS EQ-5D-3L value sets including those for countries in Szende et al (2007) <doi:10.1007/1-4020-5511-0> and Szende et al (2014) <doi:10.1007/978-94-007-7596-1>, 27 EQ-5D-5L EQ-VT value sets from the EuroQol website, the EQ-5D-5L crosswalk value sets developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> as well as the crosswalk value set for Russia. Two EQ-5D-Y value sets are also included. Methods are also included for the analysis of EQ-5D profiles along with a shiny web tool to enable the calculation, visualisation and automated statistical analysis of EQ-5D data via a web browser using EQ-5D dimension scores stored in CSV or Excel files.
Author: Fraser Morton [aut, cre], Jagtar Singh Nijjar [aut]
Maintainer: Fraser Morton <fraser.morton@glasgow.ac.uk>

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New package ssaBSS with initial version 0.1
Package: ssaBSS
Title: Stationary Subspace Analysis
Version: 0.1
Date: 2021-04-06
Authors@R: c(person(given = "Markus", family = "Matilainen", role = c("cre","aut"), email = "markus.matilainen@outlook.com", comment = c(ORCID = "0000-0002-5597-2670")), person(given = "Lea", family = "Flumian", role = "aut"), person(given = "Klaus", family = "Nordhausen", role = c("aut"), comment = c(ORCID = "0000-0002-3758-8501")), person(given = "Sara", family = "Taskinen", role = "aut", comment = c(ORCID = "0000-0001-9470-7258")))
Maintainer: Markus Matilainen <markus.matilainen@outlook.com>
Depends: tsBSS (>= 0.5.3), ICtest (>= 0.3-4), JADE (>= 2.0-2), BSSprep, ggplot2
Imports: xts, zoo
Description: Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2021-04-09 12:45:49 UTC; manmat
Author: Markus Matilainen [cre, aut] (<https://orcid.org/0000-0002-5597-2670>), Lea Flumian [aut], Klaus Nordhausen [aut] (<https://orcid.org/0000-0002-3758-8501>), Sara Taskinen [aut] (<https://orcid.org/0000-0001-9470-7258>)
Repository: CRAN
Date/Publication: 2021-04-12 07:40:02 UTC

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New package spatPomp with initial version 0.21.0.0
Package: spatPomp
Title: Inference for Spatiotemporal Partially Observed Markov Processes
Version: 0.21.0.0
Date: 2021-04-09
Authors@R: c(person("Kidus", "Asfaw", email = "kasfaw@umich.edu", role = c("aut", "cre")), person("Aaron A.", "King", email = "kingaa@umich.edu", role = c("aut")), person("Edward", "Ionides", email = "ionides@umich.edu", role = c("aut")), person("Joonha", "Park", email = "j.park@ku.edu", role = c("ctb")), person("Allister", "Ho", email = "allistho@umich.edu", role = c("ctb")))
URL: https://github.com/kidusasfaw/spatPomp
Description: Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. To do so, it relies on and extends a number of facilities that the 'pomp' package provides for inference on time series data using partially-observed Markov process (POMP) models. Implemented methods include filtering and inference methods in Park and Ionides (2020) <doi:10.1007/s11222-020-09957-3>, Rebeschini and van Handel (2015) <doi:10.1214/14-AAP1061>, Evensen and van Leeuwen (1996) <doi:10.1029/94JC00572> and Ionides et al. (2021) <arXiv:2002.05211v2>. Pre-print statistical software article: Asfaw et al. (2021) <arXiv:2101.01157>.
SystemRequirements: For Windows users, Rtools (see https://cran.r-project.org/bin/windows/Rtools/).
License: GPL-3
Encoding: UTF-8
LazyData: true
Contact: kasfaw at umich dot edu
Depends: pomp (>= 3.3), R(>= 4.0.0), methods
LinkingTo: pomp
Suggests: testthat
Imports: doParallel (>= 1.0.11), parallel, foreach, dplyr, tidyr, stringr, ggplot2, abind, rlang, magrittr
Collate: 'spatPomp_class.R' 'abf.R' 'abfir.R' 'get_covariate_names.R' 'as_data_frame.R' 'as_spatPomp.R' 'bm.R' 'bpfilter.R' 'city_data_UK.R' 'skeleton_spec.R' 'safecall.R' 'rprocess_spec.R' 'pstop.R' 'undefined.R' 'spatPomp.R' 'dunit_measure.R' 'enkf.R' 'eunit_measure.R' 'gbm.R' 'girf.R' 'iter_filter.R' 'ienkf.R' 'igirf.R' 'iubf.R' 'loglik.R' 'lorenz.R' 'mcap.R' 'measles.R' 'measlesUK.R' 'munit_measure.R' 'package.R' 'pipe.R' 'plot.R' 'print.R' 'runit_measure.R' 'simulate.R' 'spatPomp_Csnippet.R' 'spatPomp_workhorses.R' 'unit_names.R' 'vec_dmeasure.R' 'vec_rmeasure.R' 'vunit_measure.R'
NeedsCompilation: yes
Packaged: 2021-04-09 20:10:06 UTC; kasfaw
Author: Kidus Asfaw [aut, cre], Aaron A. King [aut], Edward Ionides [aut], Joonha Park [ctb], Allister Ho [ctb]
Maintainer: Kidus Asfaw <kasfaw@umich.edu>
Repository: CRAN
Date/Publication: 2021-04-12 08:40:02 UTC

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Package sfsmisc updated to version 1.1-11 with previous version 1.1-10 dated 2021-03-29

Title: Utilities from 'Seminar fuer Statistik' ETH Zurich
Description: Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes, an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().
Author: Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Werner Stahel [ctb] (Functions: compresid2way(), f.robftest(), last(), p.scales(), p.dnorm()), Andreas Ruckstuhl [ctb] (Functions: p.arrows(), p.profileTraces(), p.res.2x()), Christian Keller [ctb] (Functions: histBxp(), p.tachoPlot()), Kjetil Halvorsen [ctb] (Functions: KSd(), ecdf.ksCI()), Alain Hauser [ctb] (Functions: cairoSwd(), is.whole(), toLatex.numeric()*), Christoph Buser [ctb] (to function Duplicated()), Lorenz Gygax [ctb] (to function p.res.2fact()), Bill Venables [ctb] (Functions: empty.dimnames(), primes()), Tony Plate [ctb] (to inv.seq()), Isabelle Fl<fc>ckiger [ctb], Marcel Wolbers [ctb], Markus Keller [ctb], Sandrine Dudoit [ctb], Jane Fridlyand [ctb], Greg Snow [ctb] (to loessDemo()), Henrik Aa. Nielsen [ctb] (to loessDemo()), Vincent Carey [ctb], Ben Bolker [ctb], Philippe Grosjean [ctb], Fr<e9>d<e9>ric Ibanez [ctb], Caterina Savi [ctb], Charles Geyer [ctb], Jens Oehlschl<e4>gel [ctb]
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

Diff between sfsmisc versions 1.1-10 dated 2021-03-29 and 1.1-11 dated 2021-04-12

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Package RPostgres updated to version 1.3.2 with previous version 1.3.1 dated 2021-01-19

Title: 'Rcpp' Interface to 'PostgreSQL'
Description: Fully 'DBI'-compliant 'Rcpp'-backed interface to 'PostgreSQL' <https://www.postgresql.org/>, an open-source relational database.
Author: Hadley Wickham [aut], Jeroen Ooms [aut], Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), RStudio [cph], R Consortium [fnd], Tomoaki Nishiyama [ctb] (Code for encoding vectors into strings derived from RPostgreSQL)
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

Diff between RPostgres versions 1.3.1 dated 2021-01-19 and 1.3.2 dated 2021-04-12

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Package modelbased updated to version 0.6.0 with previous version 0.5.1 dated 2021-01-27

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>, @Dom_Makowski), Daniel Lüdecke [aut] (<https://orcid.org/0000-0002-8895-3206>), Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>), Indrajeet Patil [ctb] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets)
Maintainer: Dominique Makowski <dom.makowski@gmail.com>

Diff between modelbased versions 0.5.1 dated 2021-01-27 and 0.6.0 dated 2021-04-12

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New package misaem with initial version 1.0.1
Package: misaem
Title: Linear Regression and Logistic Regression with Missing Covariates
Version: 1.0.1
Date: 2021-04-07
Authors@R: c( person("Wei", "Jiang", role = "aut", email = "wei.jiang@polytechnique.edu"), person("Pavlo","Mozharovskyi", role = "ctb", email = "pavlo.mozharovskyi@telecom-paristech.fr"), person("Julie","Josse", role = c("aut","cre"), email = "julie.josserennes@gmail.com"), person("Imke", "Mayer", role = "ctb", email = "mayer.imke@gmail.com"))
Description: Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) <doi:10.1016/j.csda.2019.106907>.
Depends: R (>= 3.4.0)
Encoding: UTF-8
License: GPL-3
URL: https://github.com/julierennes/misaem
Imports: mvtnorm, stats, MASS, norm, methods
Suggests: knitr, rmarkdown, mice
LazyData: false
VignetteBuilder: knitr
NeedsCompilation: no
Author: Wei Jiang [aut], Pavlo Mozharovskyi [ctb], Julie Josse [aut, cre], Imke Mayer [ctb]
Maintainer: Julie Josse <julie.josserennes@gmail.com>
Repository: CRAN
Packaged: 2021-04-09 13:00:17 UTC; imke
Date/Publication: 2021-04-12 08:10:02 UTC

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New package mailmerge with initial version 0.2.0
Package: mailmerge
Title: Mail Merge Using R Markdown Documents and 'gmailr'
Version: 0.2.0
Authors@R: person(given = "Andrie", family = "de Vries", role = c("aut", "cre"), email = "apdevries@gmail.com")
Description: Mail merge using markdown documents and gmail. With this package you can parse markdown documents as the body of email, and the 'yaml' header to specify the subject line of the email. Any '{}' braces in the email will be encoded with 'glue::glue()'. You can preview the email in the RStudio viewer pane, and send (draft) email using 'gmailr'.
License: MIT + file LICENSE
URL: https://github.com/andrie/mailmerge
BugReports: https://github.com/andrie/mailmerge/issues
Depends: R (>= 3.3)
Imports: commonmark, gmailr, rstudioapi, googledrive, rmarkdown, dplyr, glue, googlesheets4, magrittr, withr, purrr, utils, fs, lifecycle, shiny, miniUI
Suggests: spelling, covr, knitr, testthat (>= 2.1.0), here
VignetteBuilder: knitr
RdMacros: lifecycle
Encoding: UTF-8
Language: en-US
NeedsCompilation: no
Packaged: 2021-04-09 11:51:18 UTC; apdev
Author: Andrie de Vries [aut, cre]
Maintainer: Andrie de Vries <apdevries@gmail.com>
Repository: CRAN
Date/Publication: 2021-04-12 07:20:03 UTC

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New package imabc with initial version 1.0.0
Package: imabc
Title: Incremental Mixture Approximate Bayesian Computation (IMABC)
Version: 1.0.0
Authors@R: c( person( given = "Christopher, E.", family = "Maerzluft", role = c("aut", "cre"), email = "cmaerzlu@rand.org" ), person( given = "Carolyn", family = "Rutter", role = c("aut", "cph"), email = "crutter@rand.org", comment = c(ORCID = "0000-0002-4396-8594") ), person( given = "Jonathan", family = "Ozik", role = c("aut"), email = "jozik@anl.gov", comment = c(ORCID = "0000-0002-3495-6735") ), person( given = "Nicholson", family = "Collier", role = c("aut"), email = "ncollier@anl.gov", comment = c(ORCID = "0000-0002-2376-4156") ) )
Description: Provides functionality to perform a likelihood-free method for estimating the parameters of complex models that results in a simulated sample from the posterior distribution of model parameters given targets. The method begins with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution of model parameters. Accepted points result in model predictions that are within the initially specified tolerance intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) <arXiv:1804.02090>.
License: GPL-3
URL: https://github.com/carolyner/imabc
BugReports: https://github.com/carolyner/imabc/issues
Depends: R (>= 3.2.0)
Imports: MASS, data.table, foreach, parallel, truncnorm, lhs, methods, stats, utils
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-04-09 17:26:40 UTC; cmaerzlu
Author: Christopher, E. Maerzluft [aut, cre], Carolyn Rutter [aut, cph] (<https://orcid.org/0000-0002-4396-8594>), Jonathan Ozik [aut] (<https://orcid.org/0000-0002-3495-6735>), Nicholson Collier [aut] (<https://orcid.org/0000-0002-2376-4156>)
Maintainer: "Christopher, E. Maerzluft" <cmaerzlu@rand.org>
Repository: CRAN
Date/Publication: 2021-04-12 08:30:03 UTC

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New package edl with initial version 1.0
Package: edl
Version: 1.0
Date: 2021-04-01
Title: Toolbox for Error-Driven Learning Simulations with Two-Layer Networks
Authors@R: c( person("Jacolien", "van Rij", email="j.c.van.rij@rug.nl", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7445-5647")), person("Dorothée", "Hoppe", email="d.b.hoppe@rug.nl", role = c("aut")))
Maintainer: Jacolien van Rij <j.c.van.rij@rug.nl>
Description: Error-driven learning (based on the Widrow & Hoff (1960)<https://isl.stanford.edu/~widrow/papers/c1960adaptiveswitching.pdf> learning rule, and essentially the same as Rescorla-Wagner's learning equations (Rescorla & Wagner, 1972, ISBN: 0390718017), which are also at the core of Naive Discrimination Learning, (Baayen et al, 2011, <doi:10.1037/a0023851>) can be used to explain bottom-up human learning (Hoppe et al, <doi:10.31234/osf.io/py5kd>), but is also at the core of artificial neural networks applications in the form of the Delta rule. This package provides a set of functions for building small-scale simulations to investigate the dynamics of error-driven learning and it's interaction with the structure of the input. For modeling error-driven learning using the Rescorla-Wagner equations the package 'ndl' (Baayen et al, 2011, <doi:10.1037/a0023851>) is available on CRAN at <https://cran.r-project.org/package=ndl>. However, the package currently only allows tracing of a cue-outcome combination, rather than returning the learned networks. To fill this gap, we implemented a new package with a few functions that facilitate inspection of the networks for small error driven learning simulations. Note that our functions are not optimized for training large data sets (no parallel processing), as they are intended for small scale simulations and course examples. (Consider the python implementation 'pyndl' <https://pyndl.readthedocs.io/en/latest/> for that purpose.)
License: GPL (>= 2)
LazyData: true
Depends: R (>= 4.0.0), plotfunctions (>= 1.4), data.table
VignetteBuilder: knitr
Suggests: knitr, rmarkdown
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-04-09 12:54:36 UTC; jacolien
Author: Jacolien van Rij [aut, cre] (<https://orcid.org/0000-0001-7445-5647>), Dorothée Hoppe [aut]
Repository: CRAN
Date/Publication: 2021-04-12 07:50:02 UTC

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New package accucor with initial version 0.2.4
Package: accucor
Title: Natural Abundance Correction of Mass Spectrometer Data
Version: 0.2.4
Authors@R: c( person(given = "Xiaoyang", family = "Su", email = "xs137@rwjms.rutgers.edu", role = "aut", comment = c(ORCID = "0000-0001-8081-1396")), person(given = "Lance", family = "Parsons", email = "lparsons@princeton.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8521-714X")), person(given = "Yujue", family = "Wang", email = "yw429@rwjms.rutgers.edu", role = "ctb", comment = c(ORCID = "0000-0001-7088-1729")), person(given = "Princeton University", role = "cph"))
Description: An isotope natural abundance correction algorithm that is needed especially for high resolution mass spectrometers. Supports correction for 13C, 2H and 15N.
URL: https://github.com/XiaoyangSu/AccuCor
BugReports: https://github.com/XiaoyangSu/AccuCor/issues
License: MIT + file LICENSE
Encoding: UTF-8
Imports: gsubfn, nnls, dplyr, stringr, readxl, readr, rlang, tibble, writexl, CHNOSZ
Suggests: testthat
NeedsCompilation: no
Packaged: 2021-04-10 01:30:33 UTC; lparsons
Author: Xiaoyang Su [aut] (<https://orcid.org/0000-0001-8081-1396>), Lance Parsons [aut, cre] (<https://orcid.org/0000-0002-8521-714X>), Yujue Wang [ctb] (<https://orcid.org/0000-0001-7088-1729>), Princeton University [cph]
Maintainer: Lance Parsons <lparsons@princeton.edu>
Repository: CRAN
Date/Publication: 2021-04-12 08:50:02 UTC

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

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

2020-09-18 1.1

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Package msae updated to version 0.1.3 with previous version 0.1.2 dated 2020-06-05

Title: Multivariate Fay Herriot Models for Small Area Estimation
Description: Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.
Author: Novia Permatasari, Azka Ubaidillah
Maintainer: Novia Permatasari <16.9335@stis.ac.id>

Diff between msae versions 0.1.2 dated 2020-06-05 and 0.1.3 dated 2021-04-12

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Package clinDR updated to version 2.3.5 with previous version 2.3 dated 2020-07-01

Title: Simulation and Analysis Tools for Clinical Dose Response Modeling
Description: Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references.
Author: Neal Thomas [aut, cre] (<https://orcid.org/0000-0002-1915-8487>), Jing Wu [aut], Mike K. Smith [aut]
Maintainer: Neal Thomas <snthomas99@gmail.com>

Diff between clinDR versions 2.3 dated 2020-07-01 and 2.3.5 dated 2021-04-12

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