Tue, 22 Sep 2020

New package ERSA with initial version 0.1.3
Package: ERSA
Type: Package
Title: Exploratory Regression 'Shiny' App
Version: 0.1.3
Date: 2020-9-22
Author: Catherine B. Hurley
Maintainer: Catherine B. Hurley <catherine.hurley@mu.ie>
Description: Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.
License: GPL (>= 2.0)
Encoding: UTF-8
LazyData: true
Imports: shiny, miniUI,RColorBrewer, ggplot2, car, leaps, broom, dplyr, tidyr, purrr, combinat,stats, methods
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-09-22 19:54:07 UTC; catherine
Repository: CRAN
Date/Publication: 2020-09-22 23:00:02 UTC

More information about ERSA at CRAN
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Package tables updated to version 0.9.6 with previous version 0.9.4 dated 2020-08-28

Title: Formula-Driven Table Generation
Description: Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.
Author: Duncan Murdoch
Maintainer: Duncan Murdoch <murdoch.duncan@gmail.com>

Diff between tables versions 0.9.4 dated 2020-08-28 and 0.9.6 dated 2020-09-22

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Package shinyFeedback updated to version 0.3.0 with previous version 0.2.0 dated 2020-04-29

Title: Display User Feedback in Shiny Apps
Description: Easily display user feedback in Shiny apps.
Author: Andy Merlino [aut, cre], Patrick Howard [aut]
Maintainer: Andy Merlino <andy.merlino@tychobra.com>

Diff between shinyFeedback versions 0.2.0 dated 2020-04-29 and 0.3.0 dated 2020-09-22

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More information about shinyFeedback at CRAN
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Package rticles updated to version 0.16 with previous version 0.15 dated 2020-08-26

Title: Article Formats for R Markdown
Description: A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.
Author: JJ Allaire [aut], Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>), R Foundation [aut, cph], Hadley Wickham [aut], Journal of Statistical Software [aut, cph], RStudio [cph], Ramnath Vaidyanathan [aut, cph], Association for Computing Machinery [aut, cph], Carl Boettiger [aut, cph], Elsevier [aut, cph], Karl Broman [aut, cph], Kirill Mueller [aut, cph], Bastiaan Quast [aut, cph], Randall Pruim [aut, cph], Ben Marwick [aut, cph], Charlotte Wickham [aut, cph], Oliver Keyes [aut, cph], Miao Yu [aut, cph], Daniel Emaasit [aut, cph], Thierry Onkelinx [aut, cph], Alessandro Gasparini [aut, cph] (<https://orcid.org/0000-0002-8319-7624>), Marc-Andre Desautels [aut, cph], Dominik Leutnant [aut, cph] (<https://orcid.org/0000-0003-3293-2315>), MDPI [aut, cph], Taylor and Francis [aut, cph], Oğuzhan Öğreden [aut] (<https://orcid.org/0000-0002-9949-3348>), Dalton Hance [aut], Daniel Nüst [aut, cph] (<https://orcid.org/0000-0002-0024-5046>), Petter Uvesten [aut, cph], Elio Campitelli [aut, cph] (<https://orcid.org/0000-0002-7742-9230>), John Muschelli [aut, cph] (<https://orcid.org/0000-0001-6469-1750>), Alex Hayes [aut] (<https://orcid.org/0000-0002-4985-5160>), Zhian N. Kamvar [aut, cph] (<https://orcid.org/0000-0003-1458-7108>), Noam Ross [aut, cph] (<https://orcid.org/0000-0002-2136-0000>), Robrecht Cannoodt [aut, cph] (<https://orcid.org/0000-0003-3641-729X>, rcannood), Duncan Luguern [aut], David M. Kaplan [aut, ctb] (<https://orcid.org/0000-0001-6087-359X>, dmkaplan2000), Sebastian Kreutzer [aut] (<https://orcid.org/0000-0002-0734-2199>), Shixiang Wang [aut, ctb] (<https://orcid.org/0000-0001-9855-7357>), Jay Hesselberth [aut, ctb] (<https://orcid.org/0000-0002-6299-179X>), Alfredo Hernández [ctb] (<https://orcid.org/0000-0002-2660-4545>), Christophe Dervieux [aut] (<https://orcid.org/0000-0003-4474-2498>)
Maintainer: Yihui Xie <xie@yihui.name>

Diff between rticles versions 0.15 dated 2020-08-26 and 0.16 dated 2020-09-22

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Package leafpop updated to version 0.0.6 with previous version 0.0.5 dated 2019-12-09

Title: Include Tables, Images and Graphs in Leaflet Pop-Ups
Description: Creates 'HTML' strings to embed tables, images or graphs in pop-ups of interactive maps created with packages like 'leaflet' or 'mapview'. Handles local images located on the file system or via remote URL. Handles graphs created with 'lattice' or 'ggplot2' as well as interactive plots created with 'htmlwidgets'.
Author: Tim Appelhans [cre, aut], Florian Detsch [aut]
Maintainer: Tim Appelhans <tim.appelhans@gmail.com>

Diff between leafpop versions 0.0.5 dated 2019-12-09 and 0.0.6 dated 2020-09-22

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Package nat updated to version 1.8.16 with previous version 1.8.14 dated 2020-02-07

Title: NeuroAnatomy Toolbox for Analysis of 3D Image Data
Description: NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D biological image data, especially traced neurons. Reads and writes 3D images in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf format. Traced neurons can be imported from and written to SWC and 'Amira' LineSet and SkeletonGraph formats. These data can then be visualised in 3D via 'rgl', manipulated including applying calculated registrations, e.g. using the 'CMTK' registration suite, and analysed. There is also a simple representation for neurons that have been subjected to 3D skeletonisation but not formally traced; this allows morphological comparison between neurons including searches and clustering (via the 'nat.nblast' extension package).
Author: Gregory Jefferis [aut, cre] (<https://orcid.org/0000-0002-0587-9355>), James Manton [aut] (<https://orcid.org/0000-0001-9260-3156>)
Maintainer: Gregory Jefferis <jefferis@gmail.com>

Diff between nat versions 1.8.14 dated 2020-02-07 and 1.8.16 dated 2020-09-22

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More information about nat at CRAN
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Package knitr updated to version 1.30 with previous version 1.29 dated 2020-06-23

Title: A General-Purpose Package for Dynamic Report Generation in R
Description: Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.
Author: Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>), Adam Vogt [ctb], Alastair Andrew [ctb], Alex Zvoleff [ctb], Andre Simon [ctb] (the CSS files under inst/themes/ were derived from the Highlight package http://www.andre-simon.de), Aron Atkins [ctb], Aaron Wolen [ctb], Ashley Manton [ctb], Atsushi Yasumoto [ctb] (<https://orcid.org/0000-0002-8335-495X>), Ben Baumer [ctb], Brian Diggs [ctb], Brian Zhang [ctb], Cassio Pereira [ctb], Christophe Dervieux [ctb], David Hugh-Jones [ctb], David Robinson [ctb], Doug Hemken [ctb], Duncan Murdoch [ctb], Elio Campitelli [ctb], Ellis Hughes [ctb], Emily Riederer [ctb], Fabian Hirschmann [ctb], Fitch Simeon [ctb], Forest Fang [ctb], Frank E Harrell Jr [ctb] (the Sweavel package at inst/misc/Sweavel.sty), Garrick Aden-Buie [ctb], Gregoire Detrez [ctb], Hadley Wickham [ctb], Hao Zhu [ctb], Heewon Jeon [ctb], Henrik Bengtsson [ctb], Hiroaki Yutani [ctb], Ian Lyttle [ctb], Hodges Daniel [ctb], Jake Burkhead [ctb], James Manton [ctb], Jared Lander [ctb], Jason Punyon [ctb], Javier Luraschi [ctb], Jeff Arnold [ctb], Jenny Bryan [ctb], Jeremy Ashkenas [ctb, cph] (the CSS file at inst/misc/docco-classic.css), Jeremy Stephens [ctb], Jim Hester [ctb], Joe Cheng [ctb], Johannes Ranke [ctb], John Honaker [ctb], John Muschelli [ctb], Jonathan Keane [ctb], JJ Allaire [ctb], Johan Toloe [ctb], Jonathan Sidi [ctb], Joseph Larmarange [ctb], Julien Barnier [ctb], Kaiyin Zhong [ctb], Kamil Slowikowski [ctb], Karl Forner [ctb], Kevin K. Smith [ctb], Kirill Mueller [ctb], Kohske Takahashi [ctb], Lorenz Walthert [ctb], Lucas Gallindo [ctb], Marius Hofert [ctb], Martin Modrák [ctb], Michael Chirico [ctb], Michael Friendly [ctb], Michal Bojanowski [ctb], Michel Kuhlmann [ctb], Miller Patrick [ctb], Nacho Caballero [ctb], Nick Salkowski [ctb], Niels Richard Hansen [ctb], Noam Ross [ctb], Obada Mahdi [ctb], Qiang Li [ctb], Ramnath Vaidyanathan [ctb], Richard Cotton [ctb], Robert Krzyzanowski [ctb], Romain Francois [ctb], Ruaridh Williamson [ctb], Scott Kostyshak [ctb], Sebastian Meyer [ctb], Sietse Brouwer [ctb], Simon de Bernard [ctb], Sylvain Rousseau [ctb], Taiyun Wei [ctb], Thibaut Assus [ctb], Thibaut Lamadon [ctb], Thomas Leeper [ctb], Tim Mastny [ctb], Tom Torsney-Weir [ctb], Trevor Davis [ctb], Viktoras Veitas [ctb], Weicheng Zhu [ctb], Wush Wu [ctb], Zachary Foster [ctb]
Maintainer: Yihui Xie <xie@yihui.name>

Diff between knitr versions 1.29 dated 2020-06-23 and 1.30 dated 2020-09-22

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Package future updated to version 1.19.1 with previous version 1.18.0 dated 2020-07-09

Title: Unified Parallel and Distributed Processing in R for Everyone
Description: The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between future versions 1.18.0 dated 2020-07-09 and 1.19.1 dated 2020-09-22

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More information about future at CRAN
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New package gutenbergr with initial version 0.2.0
Package: gutenbergr
Type: Package
Title: Download and Process Public Domain Works from Project Gutenberg
Version: 0.2.0
Authors@R: person("David", "Robinson", email = "admiral.david@gmail.com", role = c("aut", "cre"))
Description: Download and process public domain works in the Project Gutenberg collection <http://www.gutenberg.org/>. Includes metadata for all Project Gutenberg works, so that they can be searched and retrieved.
License: GPL-2
LazyData: TRUE
Maintainer: David Robinson <admiral.david@gmail.com>
URL: https://docs.ropensci.org/gutenbergr/, https://github.com/ropensci/gutenbergr
BugReports: https://github.com/ropensci/gutenbergr/issues
VignetteBuilder: knitr
Depends: R (>= 2.10)
Imports: dplyr, readr, purrr, urltools, stringr, lazyeval
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat, tidytext, ggplot2, tidyr, curl
Language: en-US
NeedsCompilation: no
Packaged: 2020-09-21 20:27:53 UTC; davidrobinson
Author: David Robinson [aut, cre]
Repository: CRAN
Date/Publication: 2020-09-22 17:00:02 UTC

More information about gutenbergr at CRAN
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Package withr updated to version 2.3.0 with previous version 2.2.0 dated 2020-04-20

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 2.2.0 dated 2020-04-20 and 2.3.0 dated 2020-09-22

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Package TreeTools updated to version 1.3.0 with previous version 1.2.0 dated 2020-08-03

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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Package simTool updated to version 1.1.7 with previous version 1.1.6 dated 2020-05-17

Title: Conduct Simulation Studies with a Minimal Amount of Source Code
Description: Tool for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with it. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results.
Author: Marsel Scheer [aut, cre]
Maintainer: Marsel Scheer <scheer@freescience.de>

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Package Sim.DiffProc updated to version 4.7 with previous version 4.6 dated 2020-05-04

Title: Simulation of Diffusion Processes
Description: It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDE's. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.
Author: Arsalane Chouaib Guidoum [cre, aut], Kamal Boukhetala [aut]
Maintainer: Arsalane Chouaib Guidoum <acguidoum@usthb.dz>

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Package procmaps updated to version 0.0.3 with previous version 0.0.2 dated 2020-09-20

Title: Portable Address Space Mapping
Description: Portable '/proc/self/maps' as a data frame. Determine which library or other region is mapped to a specific address of a process. -- R packages can contain native code, compiled to shared libraries at build or installation time. When loaded, each shared library occupies a portion of the address space of the main process. When only a machine instruction pointer is available (e.g. from a backtrace during error inspection or profiling), the address space map determines which library this instruction pointer corresponds to.
Author: Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), R Consortium [fnd], Kostya Serebryany [ctb] (Bundled gperftools library), Sanjay Ghemawat [ctb] (Bundled gperftools library), Craig Silverstein [ctb] (Bundled gperftools library), Google Inc. [cph] (Bundled gperftools library)
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

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Package pcalg updated to version 2.6-12 with previous version 2.6-11 dated 2020-08-16

Title: Methods for Graphical Models and Causal Inference
Description: Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.
Author: Markus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel Schuerch [ctb], Marco Eigenmann [ctb]
Maintainer: Markus Kalisch <kalisch@stat.math.ethz.ch>

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Package mlr3fselect updated to version 0.3.0 with previous version 0.2.1 dated 2020-09-10

Title: Feature Selection for 'mlr3'
Description: Implements methods for feature selection with 'mlr3', e.g. random search and sequential selection. Various termination criteria can be set and combined. The class 'AutoFSelect' provides a convenient way to perform nested resampling in combination with 'mlr3'.
Author: Marc Becker [aut, cre] (<https://orcid.org/0000-0002-8115-0400>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>)
Maintainer: Marc Becker <marcbecker@posteo.de>

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Package DRomics updated to version 2.1-2 with previous version 2.0-1 dated 2019-09-16

Title: Dose Response for Omics
Description: Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of three replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated metabolomic data in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a Gaussian distribution for each dose or concentration, with a common standard error. For further details see Larras et al (2018) <DOI:10.1021/acs.est.8b04752> at <https://hal.archives-ouvertes.fr/hal-02309919>.
Author: Marie-Laure Delignette-Muller [aut], Elise Billoir [aut], Floriane Larras [ctb], Aurelie Siberchicot [aut, cre]
Maintainer: Aurelie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>

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 DRomics-2.1-2/DRomics/tests/testthat/test_drcfit.R                           |    9 
 56 files changed, 1609 insertions(+), 552 deletions(-)

More information about DRomics at CRAN
Permanent link

New package BiasCorrector with initial version 0.1.2
Package: BiasCorrector
Title: A GUI to Correct Measurement Bias in DNA Methylation Analyses
Version: 0.1.2
Date: 2020-09-22
Authors@R: c(person(given = "Lorenz A.", family = "Kapsner", role = c("cre", "aut", "cph"), email = "lorenz.kapsner@gmail.com", comment = c(ORCID = "0000-0003-1866-860X")), person(given = "Evgeny A.", family = "Moskalev", role = "aut"))
Description: A GUI to correct measurement bias in DNA methylation analyses. The 'BiasCorrector' package just wraps the functions implemented in the 'R' package 'rBiasCorrection' into a shiny web application in order to make them more easily accessible.
License: GPL-3
URL: https://github.com/kapsner/BiasCorrector
BugReports: https://github.com/kapsner/BiasCorrector/issues
Depends: R (>= 2.10)
Imports: data.table, DT, magrittr, rBiasCorrection, shiny, shinydashboard, shinyjs
Suggests: lintr, testthat
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-22 11:51:59 UTC; user
Author: Lorenz A. Kapsner [cre, aut, cph] (<https://orcid.org/0000-0003-1866-860X>), Evgeny A. Moskalev [aut]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-22 15:20:21 UTC

More information about BiasCorrector at CRAN
Permanent link

Package azuremlsdk updated to version 1.10.0 with previous version 0.6.85 dated 2020-02-05

Title: Interface to the 'Azure Machine Learning' 'SDK'
Description: Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.
Author: Diondra Peck [cre, aut], Minna Xiao [aut], AzureML R SDK Team [ctb], Microsoft [cph, fnd], Google Inc. [cph] (Examples and Tutorials), The TensorFlow Authors [cph] (Examples and Tutorials), RStudio Inc. [cph] (Examples and Tutorials)
Maintainer: Diondra Peck <Diondra.Peck@microsoft.com>

Diff between azuremlsdk versions 0.6.85 dated 2020-02-05 and 1.10.0 dated 2020-09-22

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 azuremlsdk-0.6.85/azuremlsdk/man/generate_docker_file.Rd                                                 |only
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 azuremlsdk-0.6.85/azuremlsdk/vignettes/experiments-with-R                                                |only
 azuremlsdk-0.6.85/azuremlsdk/vignettes/hyperparameter-tune-with-keras/hyperparameter-tune-with-keras.Rmd |only
 azuremlsdk-0.6.85/azuremlsdk/vignettes/train-and-deploy-to-aci                                           |only
 azuremlsdk-0.6.85/azuremlsdk/vignettes/train-with-tensorflow/train-with-tensorflow.Rmd                   |only
 azuremlsdk-1.10.0/azuremlsdk/DESCRIPTION                                                                 |   27 
 azuremlsdk-1.10.0/azuremlsdk/MD5                                                                         |  486 +-
 azuremlsdk-1.10.0/azuremlsdk/NAMESPACE                                                                   |  325 -
 azuremlsdk-1.10.0/azuremlsdk/R/compute.R                                                                 |  763 ++--
 azuremlsdk-1.10.0/azuremlsdk/R/datasets.R                                                                | 1407 ++++---
 azuremlsdk-1.10.0/azuremlsdk/R/datastore.R                                                               |  770 ++--
 azuremlsdk-1.10.0/azuremlsdk/R/do_azureml_parallel.R                                                     |only
 azuremlsdk-1.10.0/azuremlsdk/R/environment.R                                                             |  579 +--
 azuremlsdk-1.10.0/azuremlsdk/R/estimator.R                                                               |  266 -
 azuremlsdk-1.10.0/azuremlsdk/R/experiment.R                                                              |  248 -
 azuremlsdk-1.10.0/azuremlsdk/R/hyperdrive.R                                                              | 1387 +++----
 azuremlsdk-1.10.0/azuremlsdk/R/install.R                                                                 |  139 
 azuremlsdk-1.10.0/azuremlsdk/R/keyvault.R                                                                |  144 
 azuremlsdk-1.10.0/azuremlsdk/R/model.R                                                                   | 1148 +++---
 azuremlsdk-1.10.0/azuremlsdk/R/modules.R                                                                 |   16 
 azuremlsdk-1.10.0/azuremlsdk/R/package.R                                                                 |   90 
 azuremlsdk-1.10.0/azuremlsdk/R/run.R                                                                     | 1850 +++++-----
 azuremlsdk-1.10.0/azuremlsdk/R/webservice-aci.R                                                          |  256 -
 azuremlsdk-1.10.0/azuremlsdk/R/webservice-aks.R                                                          |  558 +--
 azuremlsdk-1.10.0/azuremlsdk/R/webservice-local.R                                                        |  176 
 azuremlsdk-1.10.0/azuremlsdk/R/webservice.R                                                              |  387 +-
 azuremlsdk-1.10.0/azuremlsdk/R/workspace.R                                                               |  761 ++--
 azuremlsdk-1.10.0/azuremlsdk/README.md                                                                   |    4 
 azuremlsdk-1.10.0/azuremlsdk/build/vignette.rds                                                          |binary
 azuremlsdk-1.10.0/azuremlsdk/inst/doc/configuration.R                                                    |   35 
 azuremlsdk-1.10.0/azuremlsdk/inst/doc/configuration.Rmd                                                  |  262 -
 azuremlsdk-1.10.0/azuremlsdk/inst/doc/configuration.html                                                 |  117 
 azuremlsdk-1.10.0/azuremlsdk/inst/doc/deploy-to-aks.R                                                    |only
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 azuremlsdk-1.10.0/azuremlsdk/inst/doc/experiments-deep-dive.Rmd                                          |only
 azuremlsdk-1.10.0/azuremlsdk/inst/doc/experiments-deep-dive.html                                         |only
 azuremlsdk-1.10.0/azuremlsdk/inst/doc/hyperparameter-tune-with-keras.R                                   |only
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 azuremlsdk-1.10.0/azuremlsdk/inst/doc/installation.R                                                     |   17 
 azuremlsdk-1.10.0/azuremlsdk/inst/doc/installation.Rmd                                                   |  226 -
 azuremlsdk-1.10.0/azuremlsdk/inst/doc/installation.html                                                  |  139 
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 azuremlsdk-1.10.0/azuremlsdk/inst/widget/app.R                                                           |  130 
 azuremlsdk-1.10.0/azuremlsdk/man/aci_webservice_deployment_config.Rd                                     |  162 
 azuremlsdk-1.10.0/azuremlsdk/man/aks_webservice_deployment_config.Rd                                     |  319 -
 azuremlsdk-1.10.0/azuremlsdk/man/attach_aks_compute.Rd                                                   |  102 
 azuremlsdk-1.10.0/azuremlsdk/man/azureml.Rd                                                              |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/bandit_policy.Rd                                                        |  160 
 azuremlsdk-1.10.0/azuremlsdk/man/bayesian_parameter_sampling.Rd                                          |   92 
 azuremlsdk-1.10.0/azuremlsdk/man/cancel_run.Rd                                                           |   34 
 azuremlsdk-1.10.0/azuremlsdk/man/choice.Rd                                                               |   45 
 azuremlsdk-1.10.0/azuremlsdk/man/complete_run.Rd                                                         |   40 
 azuremlsdk-1.10.0/azuremlsdk/man/container_registry.Rd                                                   |   60 
 azuremlsdk-1.10.0/azuremlsdk/man/convert_to_dataset_with_csv_files.Rd                                    |   40 
 azuremlsdk-1.10.0/azuremlsdk/man/convert_to_dataset_with_parquet_files.Rd                                |   42 
 azuremlsdk-1.10.0/azuremlsdk/man/cran_package.Rd                                                         |only
 azuremlsdk-1.10.0/azuremlsdk/man/create_aks_compute.Rd                                                   |  189 -
 azuremlsdk-1.10.0/azuremlsdk/man/create_aml_compute.Rd                                                   |  197 -
 azuremlsdk-1.10.0/azuremlsdk/man/create_child_run.Rd                                                     |   49 
 azuremlsdk-1.10.0/azuremlsdk/man/create_child_runs.Rd                                                    |   47 
 azuremlsdk-1.10.0/azuremlsdk/man/create_file_dataset_from_files.Rd                                       |   45 
 azuremlsdk-1.10.0/azuremlsdk/man/create_tabular_dataset_from_delimited_files.Rd                          |  120 
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 azuremlsdk-1.10.0/azuremlsdk/man/create_tabular_dataset_from_parquet_files.Rd                            |   85 
 azuremlsdk-1.10.0/azuremlsdk/man/create_tabular_dataset_from_sql_query.Rd                                |   90 
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 azuremlsdk-1.10.0/azuremlsdk/man/data_type_double.Rd                                                     |   28 
 azuremlsdk-1.10.0/azuremlsdk/man/data_type_long.Rd                                                       |   28 
 azuremlsdk-1.10.0/azuremlsdk/man/data_type_string.Rd                                                     |   28 
 azuremlsdk-1.10.0/azuremlsdk/man/dataset_consumption_config.Rd                                           |only
 azuremlsdk-1.10.0/azuremlsdk/man/define_timestamp_columns_for_dataset.Rd                                 |   66 
 azuremlsdk-1.10.0/azuremlsdk/man/delete_compute.Rd                                                       |   50 
 azuremlsdk-1.10.0/azuremlsdk/man/delete_local_webservice.Rd                                              |   47 
 azuremlsdk-1.10.0/azuremlsdk/man/delete_model.Rd                                                         |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/delete_secrets.Rd                                                       |   40 
 azuremlsdk-1.10.0/azuremlsdk/man/delete_webservice.Rd                                                    |   42 
 azuremlsdk-1.10.0/azuremlsdk/man/delete_workspace.Rd                                                     |   53 
 azuremlsdk-1.10.0/azuremlsdk/man/deploy_model.Rd                                                         |  156 
 azuremlsdk-1.10.0/azuremlsdk/man/detach_aks_compute.Rd                                                   |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/download_file_from_run.Rd                                               |   59 
 azuremlsdk-1.10.0/azuremlsdk/man/download_files_from_run.Rd                                              |   74 
 azuremlsdk-1.10.0/azuremlsdk/man/download_from_datastore.Rd                                              |   61 
 azuremlsdk-1.10.0/azuremlsdk/man/download_from_file_dataset.Rd                                           |   53 
 azuremlsdk-1.10.0/azuremlsdk/man/download_model.Rd                                                       |   64 
 azuremlsdk-1.10.0/azuremlsdk/man/drop_columns_from_dataset.Rd                                            |   40 
 azuremlsdk-1.10.0/azuremlsdk/man/estimator.Rd                                                            |  234 -
 azuremlsdk-1.10.0/azuremlsdk/man/experiment.Rd                                                           |   60 
 azuremlsdk-1.10.0/azuremlsdk/man/filter_dataset_after_time.Rd                                            |   44 
 azuremlsdk-1.10.0/azuremlsdk/man/filter_dataset_before_time.Rd                                           |   44 
 azuremlsdk-1.10.0/azuremlsdk/man/filter_dataset_between_time.Rd                                          |   54 
 azuremlsdk-1.10.0/azuremlsdk/man/filter_dataset_from_recent_time.Rd                                      |   45 
 azuremlsdk-1.10.0/azuremlsdk/man/generate_entry_script.Rd                                                |only
 azuremlsdk-1.10.0/azuremlsdk/man/generate_new_webservice_key.Rd                                          |   48 
 azuremlsdk-1.10.0/azuremlsdk/man/get_aks_compute_credentials.Rd                                          |   34 
 azuremlsdk-1.10.0/azuremlsdk/man/get_best_run_by_primary_metric.Rd                                       |   67 
 azuremlsdk-1.10.0/azuremlsdk/man/get_child_run_hyperparameters.Rd                                        |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/get_child_run_metrics.Rd                                                |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/get_child_runs.Rd                                                       |   62 
 azuremlsdk-1.10.0/azuremlsdk/man/get_child_runs_sorted_by_primary_metric.Rd                              |   80 
 azuremlsdk-1.10.0/azuremlsdk/man/get_compute.Rd                                                          |   54 
 azuremlsdk-1.10.0/azuremlsdk/man/get_current_run.Rd                                                      |   48 
 azuremlsdk-1.10.0/azuremlsdk/man/get_dataset_by_id.Rd                                                    |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/get_dataset_by_name.Rd                                                  |   42 
 azuremlsdk-1.10.0/azuremlsdk/man/get_datastore.Rd                                                        |   45 
 azuremlsdk-1.10.0/azuremlsdk/man/get_default_datastore.Rd                                                |   81 
 azuremlsdk-1.10.0/azuremlsdk/man/get_default_keyvault.Rd                                                 |   44 
 azuremlsdk-1.10.0/azuremlsdk/man/get_environment.Rd                                                      |   56 
 azuremlsdk-1.10.0/azuremlsdk/man/get_file_dataset_paths.Rd                                               |   40 
 azuremlsdk-1.10.0/azuremlsdk/man/get_input_dataset_from_run.Rd                                           |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/get_model.Rd                                                            |   87 
 azuremlsdk-1.10.0/azuremlsdk/man/get_model_package_container_registry.Rd                                 |   80 
 azuremlsdk-1.10.0/azuremlsdk/man/get_model_package_creation_logs.Rd                                      |   46 
 azuremlsdk-1.10.0/azuremlsdk/man/get_run.Rd                                                              |   40 
 azuremlsdk-1.10.0/azuremlsdk/man/get_run_details.Rd                                                      |   67 
 azuremlsdk-1.10.0/azuremlsdk/man/get_run_details_with_logs.Rd                                            |   40 
 azuremlsdk-1.10.0/azuremlsdk/man/get_run_file_names.Rd                                                   |   42 
 azuremlsdk-1.10.0/azuremlsdk/man/get_run_metrics.Rd                                                      |   77 
 azuremlsdk-1.10.0/azuremlsdk/man/get_runs_in_experiment.Rd                                               |   63 
 azuremlsdk-1.10.0/azuremlsdk/man/get_secrets.Rd                                                          |   50 
 azuremlsdk-1.10.0/azuremlsdk/man/get_secrets_from_run.Rd                                                 |   72 
 azuremlsdk-1.10.0/azuremlsdk/man/get_webservice.Rd                                                       |   40 
 azuremlsdk-1.10.0/azuremlsdk/man/get_webservice_keys.Rd                                                  |   70 
 azuremlsdk-1.10.0/azuremlsdk/man/get_webservice_logs.Rd                                                  |   52 
 azuremlsdk-1.10.0/azuremlsdk/man/get_webservice_token.Rd                                                 |   69 
 azuremlsdk-1.10.0/azuremlsdk/man/get_workspace.Rd                                                        |   57 
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 azuremlsdk-1.10.0/azuremlsdk/man/github_package.Rd                                                       |only
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 azuremlsdk-1.10.0/azuremlsdk/man/hyperdrive_config.Rd                                                    |  207 -
 azuremlsdk-1.10.0/azuremlsdk/man/inference_config.Rd                                                     |  152 
 azuremlsdk-1.10.0/azuremlsdk/man/install_azureml.Rd                                                      |   60 
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 azuremlsdk-1.10.0/azuremlsdk/man/invoke_webservice.Rd                                                    |   78 
 azuremlsdk-1.10.0/azuremlsdk/man/keep_columns_from_dataset.Rd                                            |   50 
 azuremlsdk-1.10.0/azuremlsdk/man/list_nodes_in_aml_compute.Rd                                            |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/list_secrets.Rd                                                         |   36 
 azuremlsdk-1.10.0/azuremlsdk/man/list_supported_vm_sizes.Rd                                              |   42 
 azuremlsdk-1.10.0/azuremlsdk/man/list_workspaces.Rd                                                      |   50 
 azuremlsdk-1.10.0/azuremlsdk/man/load_dataset_into_data_frame.Rd                                         |   46 
 azuremlsdk-1.10.0/azuremlsdk/man/load_workspace_from_config.Rd                                           |   57 
 azuremlsdk-1.10.0/azuremlsdk/man/local_webservice_deployment_config.Rd                                   |   54 
 azuremlsdk-1.10.0/azuremlsdk/man/log_accuracy_table_to_run.Rd                                            |  152 
 azuremlsdk-1.10.0/azuremlsdk/man/log_confusion_matrix_to_run.Rd                                          |   48 
 azuremlsdk-1.10.0/azuremlsdk/man/log_image_to_run.Rd                                                     |   59 
 azuremlsdk-1.10.0/azuremlsdk/man/log_list_to_run.Rd                                                      |   58 
 azuremlsdk-1.10.0/azuremlsdk/man/log_metric_to_run.Rd                                                    |   62 
 azuremlsdk-1.10.0/azuremlsdk/man/log_predictions_to_run.Rd                                               |   82 
 azuremlsdk-1.10.0/azuremlsdk/man/log_residuals_to_run.Rd                                                 |   74 
 azuremlsdk-1.10.0/azuremlsdk/man/log_row_to_run.Rd                                                       |   86 
 azuremlsdk-1.10.0/azuremlsdk/man/log_table_to_run.Rd                                                     |   67 
 azuremlsdk-1.10.0/azuremlsdk/man/lognormal.Rd                                                            |   52 
 azuremlsdk-1.10.0/azuremlsdk/man/loguniform.Rd                                                           |   60 
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 azuremlsdk-1.10.0/azuremlsdk/man/merge_results.Rd                                                        |only
 azuremlsdk-1.10.0/azuremlsdk/man/mount_file_dataset.Rd                                                   |   54 
 azuremlsdk-1.10.0/azuremlsdk/man/normal.Rd                                                               |   54 
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 azuremlsdk-1.10.0/azuremlsdk/man/primary_metric_goal.Rd                                                  |   46 
 azuremlsdk-1.10.0/azuremlsdk/man/promote_headers_behavior.Rd                                             |   48 
 azuremlsdk-1.10.0/azuremlsdk/man/pull_model_package_image.Rd                                             |   60 
 azuremlsdk-1.10.0/azuremlsdk/man/qlognormal.Rd                                                           |   62 
 azuremlsdk-1.10.0/azuremlsdk/man/qloguniform.Rd                                                          |   62 
 azuremlsdk-1.10.0/azuremlsdk/man/qnormal.Rd                                                              |   56 
 azuremlsdk-1.10.0/azuremlsdk/man/quniform.Rd                                                             |   60 
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 azuremlsdk-1.10.0/azuremlsdk/man/randint.Rd                                                              |   64 
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 azuremlsdk-1.10.0/azuremlsdk/man/random_split_dataset.Rd                                                 |   44 
 azuremlsdk-1.10.0/azuremlsdk/man/register_azure_blob_container_datastore.Rd                              |  177 
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 azuremlsdk-1.10.0/azuremlsdk/man/register_azure_file_share_datastore.Rd                                  |  141 
 azuremlsdk-1.10.0/azuremlsdk/man/register_azure_postgre_sql_datastore.Rd                                 |   84 
 azuremlsdk-1.10.0/azuremlsdk/man/register_azure_sql_database_datastore.Rd                                |  111 
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 azuremlsdk-1.10.0/azuremlsdk/man/register_environment.Rd                                                 |   56 
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 azuremlsdk-1.10.0/azuremlsdk/man/set_default_datastore.Rd                                                |   41 
 azuremlsdk-1.10.0/azuremlsdk/man/set_secrets.Rd                                                          |   58 
 azuremlsdk-1.10.0/azuremlsdk/man/skip_from_dataset.Rd                                                    |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/split_tasks.Rd                                                          |only
 azuremlsdk-1.10.0/azuremlsdk/man/start_logging_run.Rd                                                    |   93 
 azuremlsdk-1.10.0/azuremlsdk/man/submit_child_run.Rd                                                     |   42 
 azuremlsdk-1.10.0/azuremlsdk/man/submit_experiment.Rd                                                    |   94 
 azuremlsdk-1.10.0/azuremlsdk/man/take_from_dataset.Rd                                                    |   38 
 azuremlsdk-1.10.0/azuremlsdk/man/take_sample_from_dataset.Rd                                             |   42 
 azuremlsdk-1.10.0/azuremlsdk/man/truncation_selection_policy.Rd                                          |  135 
 azuremlsdk-1.10.0/azuremlsdk/man/uniform.Rd                                                              |   52 
 azuremlsdk-1.10.0/azuremlsdk/man/unregister_all_dataset_versions.Rd                                      |   34 
 azuremlsdk-1.10.0/azuremlsdk/man/unregister_datastore.Rd                                                 |   36 
 azuremlsdk-1.10.0/azuremlsdk/man/update_aci_webservice.Rd                                                |  109 
 azuremlsdk-1.10.0/azuremlsdk/man/update_aks_webservice.Rd                                                |  243 -
 azuremlsdk-1.10.0/azuremlsdk/man/update_aml_compute.Rd                                                   |   58 
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Package sigr updated to version 1.1.1 with previous version 1.1.0 dated 2020-09-15

Title: Succinct and Correct Statistical Summaries for Reports
Description: Succinctly and correctly format statistical summaries of various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). The main purpose is unified reporting of experimental results, working around issue such as the difficulty of extracting model summary facts (such as with 'lm'/'glm'). This package also includes empirical tests, such as bootstrap estimates.
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

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Package neonstore updated to version 0.2.3 with previous version 0.2.2 dated 2020-09-08

Title: NEON Data Store
Description: The National Ecological Observatory Network (NEON) provides access to its numerous data products through its REST API, <https://data.neonscience.org/data-api/>. This package provides a high-level user interface for downloading and storing NEON data products. While each of NEON data products consist of hundreds or thousands of individual files. Unlike 'neonUtilities', this package will avoid repeated downloading, provides persistent storage, and improves performance. This package does not provide expose interactions with the individual low-level NEON API endpoints.
Author: Carl Boettiger [aut, cre] (<https://orcid.org/0000-0002-1642-628X>), Quinn Thomas [aut] (<https://orcid.org/0000-0003-1282-7825>), Christine Laney [aut] (<https://orcid.org/0000-0002-4944-2083>), Claire Lunch [aut] (<https://orcid.org/0000-0001-8753-6593>)
Maintainer: Carl Boettiger <cboettig@gmail.com>

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Package jsTreeR updated to version 1.0.0 with previous version 0.1.0 dated 2020-08-31

Title: A Wrapper of the JavaScript Library 'jsTree'
Description: Creates interactive trees that can be included in 'Shiny' apps and R markdown documents. A tree allows to represent hierarchical data (e.g. the contents of a directory). Similar to the 'shinyTree' package but offers more features and options, such as the grid extension, restricting the drag-and-drop behavior, and settings for the search functionality. It is possible to attach some data to the nodes of a tree and then to get these data in 'Shiny' when a node is selected. Also provides a 'Shiny' gadget allowing to manipulate one or more folders.
Author: Stéphane Laurent [aut, cre], jQuery contributors [ctb, cph] (jQuery), Ivan Bozhanov [ctb, cph] (jsTree), Vedran Opacic [ctb, cph] (jsTree bootstrap theme), Avi Deitcher [ctb, cph] (jsTreeGrid), Terence Eden [ctb, cph] (SuperTinyIcons)
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>

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Package LSX updated to version 0.9.2 with previous version 0.9.1 dated 2020-09-14

Title: Model for Semisupervised Text Analysis Based on Word Embeddings
Description: A word embeddings-based semisupervised models for document scaling Watanabe (2017) <doi:10.1177/0267323117695735>. LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>

Diff between LSX versions 0.9.1 dated 2020-09-14 and 0.9.2 dated 2020-09-22

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Package SWIM updated to version 0.2.2 with previous version 0.2.1 dated 2020-05-22

Title: Scenario Weights for Importance Measurement
Description: An efficient sensitivity analysis for stochastic models based on Monte Carlo samples. Provides weights on simulated scenarios from a stochastic model, such that stressed random variables fulfil given probabilistic constraints (e.g. specified values for risk measures), under the new scenario weights. Scenario weights are selected by constrained minimisation of the relative entropy to the baseline model. The 'SWIM' package is based on Pesenti S.M, Millossovich P., Tsanakas A. (2019) "Reverse Sensitivity Testing: What does it take to break the model", <openaccess.city.ac.uk/id/eprint/18896/>.
Author: Silvana M. Pesenti [aut, cre], Alberto Bettini [aut], Pietro Millossovich [aut], Andreas Tsanakas [aut]
Maintainer: Silvana M. Pesenti <swimpackage@gmail.com>

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Package SpatialPack updated to version 0.3-8196 with previous version 0.3-819 dated 2020-06-21

Title: Tools for Assessment the Association Between Two Spatial Processes
Description: Tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided.
Author: Felipe Osorio [aut, cre] (<https://orcid.org/0000-0002-4675-5201>), Ronny Vallejos [aut] (<https://orcid.org/0000-0001-5519-0946>), Francisco Cuevas [ctb], Diego Mancilla [ctb]
Maintainer: Felipe Osorio <felipe.osorios@usm.cl>

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Package SpatialExtremes updated to version 2.0-9 with previous version 2.0-8 dated 2020-03-09

Title: Modelling Spatial Extremes
Description: Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.
Author: Mathieu Ribatet [aut, cre], Richard Singleton [ctb], R Core team [ctb]
Maintainer: Mathieu Ribatet <mathieu.ribatet@ec-nantes.fr>

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Package smoothedLasso updated to version 1.4 with previous version 1.3 dated 2020-06-14

Title: A Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing
Description: We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates.
Author: Georg Hahn [aut,cre], Sharon M. Lutz [ctb], Nilanjana Laha [ctb], Christoph Lange [ctb]
Maintainer: Georg Hahn <ghahn@hsph.harvard.edu>

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Package rasciidoc updated to version 2.2.0 with previous version 2.1.3 dated 2020-08-16

Title: Create Reports Using R and 'asciidoc'
Description: Inspired by Karl Broman`s reader on using 'knitr' with 'asciidoc' (<http://kbroman.org/knitr_knutshell/pages/asciidoc.html>), this is merely a wrapper to 'knitr' and 'asciidoc'.
Author: Andreas Dominik Cullmann [aut, cre]
Maintainer: Andreas Dominik Cullmann <fvafrcu@mailbox.org>

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Package image.textlinedetector updated to version 0.1.2 with previous version 0.1.1 dated 2020-09-21

Title: Segment Images in Text Lines and Words
Description: Find text lines in scanned images and segment the lines into words. Includes implementations of the paper 'Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents' by Surinta O. et al (2014) <doi:10.1109/ICFHR.2014.37> available at <https://github.com/smeucci/LineSegm>, an implementation of 'A Statistical approach to line segmentation in handwritten documents' by Arivazhagan M. et al (2007) <doi:10.1117/12.704538>, and a wrapper for an image segmentation technique to detect words in text lines as described in the paper 'Scale Space Technique for Word Segmentation in Handwritten Documents' by Manmatha R. and Srimal N. (1999) paper at <doi:10.1007/3-540-48236-9_3>, wrapper for code available at <https://github.com/arthurflor23/text-segmentation>.
Author: Jan Wijffels [aut, cre, cph] (R wrapper), Vrije Universiteit Brussel - DIGI: Brussels Platform for Digital Humanities [cph] (R wrapper), Jeroen Ooms [ctb, cph] (More details in LICENSE.note file), Arthur Flôr [ctb, cph] (More details in LICENSE.note file), Saverio Meucci [ctb, cph] (More details in LICENSE.note file), Yeara Kozlov [ctb, cph] (More details in LICENSE.note file), Tino Weinkauf [ctb, cph] (More details in LICENSE.note file)
Maintainer: Jan Wijffels <jan.wijffels@vub.be>

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Package emba updated to version 0.1.7 with previous version 0.1.6 dated 2020-07-08

Title: Ensemble Boolean Model Biomarker Analysis
Description: Analysis and visualization of an ensemble of boolean models for biomarker discovery in cancer cell networks. The package allows to easily load the simulation data results of the DrugLogics software pipeline which predicts synergistic drug combinations in cancer cell lines (developed by the DrugLogics research group in NTNU). It has generic functions that can be used to split a boolean model dataset to model groups with regards to the models predictive performance (number of true positive predictions/Matthews correlation coefficient score) or synergy prediction based on a given set of gold standard synergies and find the average activity difference per network node between all model group pairs. Thus, given user-specific thresholds, important nodes (biomarkers) can be accessed in the sense that they make the models predict specific synergies (synergy biomarkers) or have better performance in general (performance biomarkers). Lastly, if the boolean models have a specific equation form and differ only in their link operator, link operator biomarkers can also be found.
Author: John Zobolas [aut, cph, cre] (<https://orcid.org/0000-0002-3609-8674>)
Maintainer: John Zobolas <bblodfon@gmail.com>

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New package usemodels with initial version 0.0.1
Package: usemodels
Title: Boilerplate Code for 'Tidymodels' Analyses
Version: 0.0.1
Authors@R: c( person( given = "Max", family = "Kuhn", email = "max@rstudio.com", comment = c(ORCID = "0000-0003-2402-136X"), role = c("aut", "cre") ), person("RStudio", role = "cph"))
Description: Code snippets to fit models using the tidymodels framework can be easily created for a given data set.
URL: https://tidymodels.github.io/usemodels/, https://github.com/tidymodels/usemodels
BugReports: https://github.com/tidymodels/usemodels/issues
License: MIT + file LICENSE
Suggests: testthat, spelling, covr, palmerpenguins
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Language: en-US
Imports: tune (>= 0.1.1), cli, recipes, rlang, purrr, dplyr, tidyr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2020-09-15 02:15:15 UTC; max
Author: Max Kuhn [aut, cre] (<https://orcid.org/0000-0003-2402-136X>), RStudio [cph]
Maintainer: Max Kuhn <max@rstudio.com>
Repository: CRAN
Date/Publication: 2020-09-22 09:30:03 UTC

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New package unnest with initial version 0.0.2
Package: unnest
Title: Unnest Hierarchical Data Structures
Version: 0.0.2
Authors@R: person(given = "Vitalie", family = "Spinu", role = c("aut", "cre"), email = "spinuvit@gmail.com")
Description: Fast flattening of hierarchical data structures (e.g. JSON and XML documents) into data.frames with a flexible spec language.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: data.table, dplyr, knitr, repurrrsive, rmarkdown, roxygen2, testthat, tibble, tidyr
VignetteBuilder: knitr
URL: https://github.com/vspinu/unnest
BugReports: https://github.com/vspinu/unnest/issues
NeedsCompilation: yes
Packaged: 2020-09-15 09:29:25 UTC; vspinu
Author: Vitalie Spinu [aut, cre]
Maintainer: Vitalie Spinu <spinuvit@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-22 09:40:02 UTC

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New package Riemann with initial version 0.1.0
Package: Riemann
Type: Package
Title: Learning with Data on Riemannian Manifolds
Version: 0.1.0
Authors@R: c(person("Kisung", "You", role = c("aut", "cre"), email = "kyoustat@gmail.com",comment=c(ORCID="0000-0002-8584-459X")))
Description: We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.
License: MIT + file LICENSE
Imports: Rcpp (>= 1.0.5), Rdpack, RiemBase, maotai, stats, utils
Encoding: UTF-8
URL: http://kyoustat.com/Riemann/
BugReports: https://github.com/kyoustat/Riemann/issues
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.1.1
RdMacros: Rdpack
NeedsCompilation: yes
Packaged: 2020-09-15 11:49:51 UTC; kisung
Author: Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>)
Maintainer: Kisung You <kyoustat@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-22 09:40:06 UTC

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New package MonteCarloSEM with initial version 0.0.1
Type: Package
Package: MonteCarloSEM
Title: Monte Carlo Data Simulation Package
Version: 0.0.1
Authors@R: person(given = "Fatih", family = "Orcan", role = c("aut", "cre"), email = "fatihorcan84@gmail.com", comment = c(ORCID = "0000-0003-1727-0456"))
Description: Monte Carlo simulation allows to test different conditions given to the correct structural equation models. This package runs Monte Carlo simulations under different conditions (such as sample size or normality of data). Within the package data sets can be simulated and run based on the given model. First, continuous and normal data sets are generated based on the given model. Later Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add non-normality if exists. When data generation is completed (or when generated data sets are given) model test can also be run.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.1.1
Imports: Matrix, stats, utils, lavaan
Copyright: Fatih Orcan, Trabzon University, Turkey
LazyData: true
NeedsCompilation: no
Packaged: 2020-09-15 17:26:50 UTC; FO
Author: Fatih Orcan [aut, cre] (<https://orcid.org/0000-0003-1727-0456>)
Maintainer: Fatih Orcan <fatihorcan84@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-22 09:50:12 UTC

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New package LifeInsuranceContracts with initial version 0.0.2
Package: LifeInsuranceContracts
Type: Package
Version: 0.0.2
Date: 2020-09-14
Title: Framework for Traditional Life Insurance Contracts
Description: R6 classes to model traditional life insurance contracts like annuities, whole life insurances or endowments. Such life insurance contracts provide a guaranteed interest and are not directly linked to the performance of a particular investment vehicle. However, they typically provide (discretionary) profit participation. This package provides a framework to model such contracts in a very generic (cash-flow-based) way and includes modelling profit participation schemes, dynamic increases or more general contract layers, as well as contract changes (like sum increases or premium waivers). All relevant quantities like premium decomposition, reserves and benefits over the whole contract period are calculated and potentially exported to excel. Mortalities are given using the 'MortalityTables' package.
Authors@R: c(person("Reinhold", "Kainhofer", role=c("aut", "cre"), email="reinhold@kainhofer.com"))
Author: Reinhold Kainhofer [aut, cre]
Maintainer: Reinhold Kainhofer <reinhold@kainhofer.com>
Encoding: UTF-8
Imports: R6, MortalityTables, objectProperties, lubridate, openxlsx, dplyr, scales, abind, stringr, methods, rlang
License: GPL (>= 2)
RoxygenNote: 7.1.1
Collate: 'HelperFunctions.R' 'InsuranceParameters.R' 'ProfitParticipation_Functions.R' 'ProfitParticipation.R' 'InsuranceTarif.R' 'InsuranceContract.R' 'addDataTableWorksheet.R' 'contractGrid.R' 'exportInsuranceContract_xlsx.R' 'showVmGlgExamples.R' 'exportInsuranceContractExample.R'
Suggests: knitr, rmarkdown, pander, kableExtra, magrittr, tibble, testthat
VignetteBuilder: knitr
URL: https://gitlab.open-tools.net/R/r-life-insurance-contracts
BugReports: https://gitlab.open-tools.net/R/r-life-insurance-contracts/-/issues
NeedsCompilation: no
Packaged: 2020-09-14 21:45:38 UTC; reinhold
Repository: CRAN
Date/Publication: 2020-09-22 09:10:02 UTC

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Package incidence2 updated to version 0.2.0 with previous version 0.1.0 dated 2020-09-07

Title: Compute, Handle and Plot Incidence of Dated Events
Description: Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence2' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Author: Tim Taylor [aut, cre] (<https://orcid.org/0000-0002-8587-7113>), Thibaut Jombart [aut], Zhian N. Kamvar [aut] (<https://orcid.org/0000-0003-1458-7108>), Rich FitzJohn [aut], Jun Cai [ctb] (<https://orcid.org/0000-0001-9495-1226>), Sangeeta Bhatia [ctb], Jakob Schumacher [ctb], Juliet R.C. Pulliam [ctb] (<https://orcid.org/0000-0003-3314-8223>)
Maintainer: Tim Taylor <tim.taylor@hiddenelephants.co.uk>

Diff between incidence2 versions 0.1.0 dated 2020-09-07 and 0.2.0 dated 2020-09-22

 DESCRIPTION                               |    8 ++---
 MD5                                       |   40 +++++++++++++--------------
 NAMESPACE                                 |    1 
 NEWS.md                                   |   11 +++++++
 R/dplyr.R                                 |   18 +++++++++++-
 R/incidence.R                             |   43 +++++++++++++-----------------
 R/make_incidence.R                        |    2 -
 R/plot.R                                  |   41 ++++++++++++++++------------
 R/scale_x_incidence.R                     |   12 ++++++--
 build/vignette.rds                        |binary
 inst/doc/Introduction.html                |   14 ++++-----
 inst/doc/customizing_incidence_plots.R    |    8 ++---
 inst/doc/customizing_incidence_plots.Rmd  |    8 ++---
 inst/doc/customizing_incidence_plots.html |   34 +++++++++++------------
 inst/doc/handling_incidence_objects.R     |    2 -
 inst/doc/handling_incidence_objects.Rmd   |    2 -
 inst/doc/handling_incidence_objects.html  |   28 +++++++++----------
 man/plot.incidence2.Rd                    |    8 ++---
 tests/testthat/test-incidence.R           |    9 ++++++
 vignettes/customizing_incidence_plots.Rmd |    8 ++---
 vignettes/handling_incidence_objects.Rmd  |    2 -
 21 files changed, 171 insertions(+), 128 deletions(-)

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New package gauseR with initial version 1.0
Package: gauseR
Title: Lotka-Volterra Models for Gause's 'Struggle for Existence'
Version: 1.0
Authors@R: c(person("Adam", "Clark", role = c("aut", "cre"), email = c("adam.tclark@gmail.com"), comment = c(ORCID = "0000-0002-8843-3278")), person("Lina", "Mühlbauer", role = c("aut")), person("Maximilienne", "Schulze", role = c("aut")))
Description: A collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at <http://hdl.handle.net/11299/204551>, and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. 'gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's 'Struggle for Existence' in the journal Ecology and Evolution.
Imports: deSolve, stats, graphics
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-15 13:55:51 UTC; adamt
Author: Adam Clark [aut, cre] (<https://orcid.org/0000-0002-8843-3278>), Lina Mühlbauer [aut], Maximilienne Schulze [aut]
Maintainer: Adam Clark <adam.tclark@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-22 09:50:07 UTC

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New package cat2cat with initial version 0.1.3
Package: cat2cat
Title: Mapping of a Categorical Variable in a Panel Dataset
Version: 0.1.3
Authors@R: person("Maciej", "Nasinski", email = "nasinski.maciej@gmail.com", role = c("aut", "cre"))
Maintainer: Maciej Nasinski <nasinski.maciej@gmail.com>
Description: There are offered automatic methods to map a categorical variable according to a specific encoding across different time points. The main rule is to replicate the observation if it could be assign to a few categories. Then using simple frequencies or statistical methods to approximate probabilities of being assign to each of them. This algorithm was invented and implemented in the paper by Nasinski, Majchrowska and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>).
Depends: R (>= 3.6)
License: GPL (>= 2)
URL: https://github.com/Polkas/cat2cat
BugReports: https://github.com/Polkas/cat2cat/issues
Encoding: UTF-8
Imports: methods, rlang, caret, progress, magrittr, randomForest, MASS, tidyr, dplyr, data.table
Suggests: knitr, rmarkdown, pacman, testthat
LazyData: true
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-09-15 17:55:13 UTC; maciej
Author: Maciej Nasinski [aut, cre]
Repository: CRAN
Date/Publication: 2020-09-22 10:00:02 UTC

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New package BayesianLaterality with initial version 0.1.0
Package: BayesianLaterality
Type: Package
Title: Predict Brain Asymmetry Based on Handedness and Dichotic Listening
Version: 0.1.0
Author: Oystein Sorensen
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>
Description: Functional differences between the cerebral hemispheres are a fundamental characteristic of the human brain. Researchers interested in studying these differences often infer underlying hemispheric dominance for a certain function (e.g., language) from laterality indices calculated from observed performance or brain activation measures . However, any inference from observed measures to latent (unobserved) classes has to consider the prior probability of class membership in the population. The provided functions implement a Bayesian model for predicting hemispheric dominance from observed laterality indices (Sorensen and Westerhausen, Laterality: Asymmetries of Body, Brain and Cognition, 2020, <doi:10.1080/1357650X.2020.1769124>).
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 2.10)
Imports: dplyr (>= 1.0.2), purrr (>= 0.3.4), rlang (>= 0.4.7), tidyr (>= 1.1.2), tmvtnorm (>= 1.4-10), Rdpack
Suggests: roxygen2, testthat (>= 2.1.0)
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2020-09-15 05:09:29 UTC; oyss
Repository: CRAN
Date/Publication: 2020-09-22 09:30:07 UTC

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New package tipitaka with initial version 0.1.0
Package: tipitaka
Type: Package
Title: A Package for Analyzing the Pali Canon
Version: 0.1.0
Authors@R: person(given = "Dan", family = "Zigmond", role = c("aut", "cre"), email = "djz@shmonk.com")
Description: Provides access to the complete Pali Canon, or Tipitaka, the canonical scripture for Theravadin Buddhists worldwide.
License: CC0
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 2.10)
Imports: stringr, dplyr, magrittr, stringi
NeedsCompilation: no
Packaged: 2020-09-13 22:42:30 UTC; danzigmond
Author: Dan Zigmond [aut, cre]
Maintainer: Dan Zigmond <djz@shmonk.com>
Repository: CRAN
Date/Publication: 2020-09-22 08:30:03 UTC

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New package testComplexity with initial version 0.1.0
Package: testComplexity
Title: Asymptotic Complexity Testing Framework
Version: 0.1.0
Author: Anirban Chetia <bloodraven166@gmail.com>
Maintainer: Anirban Cheta <bloodraven166@gmail.com>
Description: It classifies the asymptotic time/memory complexity class for an algorithm/function through the complexity classifiers, with data frames returned by the quantifiers. The data frames can be passed onto the plotting functions for obtaining a visual description of the benchmarks against data sizes if the user intends to diagnose the trend via the traditional method. For testing the algorithm against an expected time complexity class, the user can directly use the expect functions. Please check <https://anirban166.github.io/testComplexity/articles/testComplexity> to get started.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
URL: https://github.com/Anirban166/testComplexity
BugReports: https://github.com/Anirban166/testComplexity/issues
VignetteBuilder: knitr
Imports: boot, utils, microbenchmark, bench, ggplot2, stats, testthat (>= 2.1.0)
Suggests: covr, knitr, rmarkdown, PeakSegDP, PeakSegOptimal
NeedsCompilation: no
Packaged: 2020-09-14 05:21:00 UTC; HP
Repository: CRAN
Date/Publication: 2020-09-22 08:30:08 UTC

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New package SDCNway with initial version 1.0.0
Version: 1.0.0
Date: 2020-08-19
Title: Tools to Evaluate Disclosure Risk
Authors@R: c(person(given = "John", family = "Riddles", role = c("aut", "cre"), email = "JohnRiddles@westat.com"), person("Westat", role = "cph"))
Maintainer: John Riddles <JohnRiddles@westat.com>
Depends: R (>= 3.6.0), Rdpack
Imports: methods, plyr (>= 1.8.5), dplyr (>= 0.8.4), ggplot2 (>= 3.2.1), MASS (>= 3.6.0)
RdMacros: Rdpack
Description: Tools for calculating disclosure risk measures, including record-level measures primarily using exhaustive tabulation, as well as file-level measures using a loglinear model. Funded by the National Center for Education Statistics. See El Emam (2011) <doi:10.1186/gm239> for a description of risk measures.
Note: This publication was prepared for NCES under Contract No. ED-IES-12-D-0009/0005 with Sanametrix and Westat. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government.
Package: SDCNway
License: GPL-2
Encoding: UTF-8
LazyData: true
ByteCompile: true
NeedsCompilation: no
RoxygenNote: 7.1.0
Packaged: 2020-09-08 14:04:48 UTC; riddles_j
Author: John Riddles [aut, cre], Westat [cph]
Repository: CRAN
Date/Publication: 2020-09-22 08:40:07 UTC

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New package sanic with initial version 0.0.1
Package: sanic
Type: Package
Title: Solving Ax = b Nimbly in C++
Version: 0.0.1
Date: 2020-09-04
Authors@R: c(person("Nikolas", "Kuschnig", role = c("aut", "cre"), email = "nikolas.kuschnig@wu.ac.at", comment = c(ORCID = "0000-0002-6642-2543")))
Author: Nikolas Kuschnig [aut, cre] (<https://orcid.org/0000-0002-6642-2543>)
Maintainer: Nikolas Kuschnig <nikolas.kuschnig@wu.ac.at>
Description: Routines for solving large systems of linear equations in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Both dense and sparse problems are supported.
URL: https://github.com/nk027/sanic
BugReports: https://github.com/nk027/sanic/issues
Depends: R (>= 3.3.0)
Imports: Rcpp (>= 1.0.5), Matrix, methods
License: GPL-3
Encoding: UTF-8
LinkingTo: Rcpp, RcppEigen
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2020-09-14 09:39:36 UTC; nikolas
Repository: CRAN
Date/Publication: 2020-09-22 08:40:03 UTC

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New package rumidas with initial version 0.1.0
Package: rumidas
Title: Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and MEM-MIDAS
Version: 0.1.0
Authors@R: person("Vincenzo", "Candila", email="vincenzo.candila@uniroma1.it", role=c("aut", "cre"))
Description: Adds the MIxing Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components within the GARCH (Engle et al. (2013) <doi:10.1162/REST_a_00300>) and MEM (Engle (2002) <doi:10.1002/jae.683>) frameworks, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. Finally, an option to generate one-step-ahead volatility forecasts automatically divides the whole period into a training and testing samples.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
RdMacros: Rdpack
Depends: R (>= 4.0.0), maxLik (>= 1.3-8)
Imports: highfrequency (>= 0.6.5), roll (>= 1.1.4), xts (>= 0.12.0), tseries (>= 0.10.47), Rdpack (>= 1.0.0), lubridate (>= 1.7.9), zoo (>= 1.8.8), stats (>= 4.0.2), utils (>= 4.0.2)
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2020-09-14 13:40:42 UTC; Vincenzo
Author: Vincenzo Candila [aut, cre]
Maintainer: Vincenzo Candila <vincenzo.candila@uniroma1.it>
Repository: CRAN
Date/Publication: 2020-09-22 08:50:02 UTC

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New package mrregression with initial version 1.0.0
Package: mrregression
Type: Package
Title: Regression Analysis for Very Large Data Sets via Merge and Reduce
Version: 1.0.0
Author: Esther Denecke [aut], Leo N. Geppert [aut, cre], Steffen Maletz [ctb], R Core Team [ctb]
Maintainer: Leo N. Geppert <geppert@statistik.uni-dortmund.de>
Description: Frequentist and Bayesian linear regression for large data sets. Useful when the data does not fit into memory (for both frequentist and Bayesian regression), to make running time manageable (mainly for Bayesian regression), and to reduce the total running time because of reduced or less severe memory-spillover into the virtual memory. This is an implementation of Merge & Reduce for linear regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). 'Streaming statistical models via Merge & Reduce'. International Journal of Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.
Depends: R (>= 4.0.0), Rcpp (>= 1.0.5),
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: testthat (>= 2.3.2),
Imports: data.table (>= 1.12.8),
Enhances: rstan (>= 2.19.3),
NeedsCompilation: no
Packaged: 2020-09-13 15:47:51 UTC; leo
Repository: CRAN
Date/Publication: 2020-09-22 08:20:02 UTC

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Package lasso2 updated to version 1.2-21.1 with previous version 1.2-20 dated 2018-11-27

Title: L1 Constrained Estimation aka `lasso'
Description: Routines and documentation for solving regression problems while imposing an L1 constraint on the estimates, based on the algorithm of Osborne et al. (1998).
Author: Justin Lokhorst, Bill Venables and Berwin Turlach; port to R, tests etc: Martin Maechler <maechler@stat.math.ethz.ch>
Maintainer: Berwin Turlach <Berwin.Turlach@gmail.com>

Diff between lasso2 versions 1.2-20 dated 2018-11-27 and 1.2-21.1 dated 2020-09-22

 DESCRIPTION                  |   11 +++++------
 MD5                          |   16 ++++++++--------
 R/gl1ce.q                    |    2 +-
 R/l1ce.q                     |    2 +-
 build/vignette.rds           |binary
 data/Prostate.rda            |binary
 inst/doc/Manual-vignette.pdf |binary
 man/gl1ce.Rd                 |    6 ++----
 man/l1ce.Rd                  |    2 +-
 9 files changed, 18 insertions(+), 21 deletions(-)

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Package ICAMS updated to version 2.2.4 with previous version 2.2.3 dated 2020-09-18

Title: In-depth Characterization and Analysis of Mutational Signatures ('ICAMS')
Description: Analysis and visualization of experimentally elucidated mutational signatures -- the kind of analysis and visualization in Boot et al., "In-depth characterization of the cisplatin mutational signature in human cell lines and in esophageal and liver tumors", Genome Research 2018, <doi:10.1101/gr.230219.117> and "Characterization of colibactin-associated mutational signature in an Asian oral squamous cell carcinoma and in other mucosal tumor types", Genome Research 2020 <doi:10.1101/gr.255620.119>. 'ICAMS' stands for In-depth Characterization and Analysis of Mutational Signatures. 'ICAMS' has functions to read in variant call files (VCFs) and to collate the corresponding catalogs of mutational spectra and to analyze and plot catalogs of mutational spectra and signatures. Handles both "counts-based" and "density-based" catalogs of mutational spectra or signatures.
Author: Steve Rozen, Nanhai Jiang, Arnoud Boot, Mo Liu
Maintainer: Steve Rozen <steverozen@gmail.com>

Diff between ICAMS versions 2.2.3 dated 2020-09-18 and 2.2.4 dated 2020-09-22

 DESCRIPTION                              |    6 +++---
 MD5                                      |    9 +++++----
 NEWS.md                                  |only
 README.md                                |    6 +++---
 tests/testthat/test_1TransformCatalog.R  |    4 ++++
 tests/testthat/test_discarded_variants.R |    8 ++++++++
 6 files changed, 23 insertions(+), 10 deletions(-)

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New package BVARverse with initial version 0.0.1
Package: BVARverse
Type: Package
Title: Tidy Bayesian Vector Autoregression
Version: 0.0.1
Date: 2020-09-07
Authors@R: c(person("Lukas", "Vashold", role = c("aut", "cre"), email = "lukas.vashold@wu.ac.at", comment = c(ORCID = "0000-0002-3562-3414")), person("Nikolas", "Kuschnig", role = c("aut"), comment = c(ORCID = "0000-0002-6642-2543")))
Author: Lukas Vashold [aut, cre] (<https://orcid.org/0000-0002-3562-3414>), Nikolas Kuschnig [aut] (<https://orcid.org/0000-0002-6642-2543>)
Maintainer: Lukas Vashold <lukas.vashold@wu.ac.at>
Description: Functions to prepare tidy objects from estimated models via 'BVAR' (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and visualisation thereof. Bridges the gap between estimating models with 'BVAR' and plotting the results in a more sophisticated way with 'ggplot2' as well as passing them on in a tidy format.
URL: https://github.com/nk027/bvarverse
BugReports: https://github.com/nk027/bvarverse/issues
Depends: R (>= 3.5.0), BVAR
Imports: ggplot2, tidyr, generics, rlang
Suggests: tinytest
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-13 20:09:25 UTC; lukas
Repository: CRAN
Date/Publication: 2020-09-22 08:20:12 UTC

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New package bravo with initial version 1.0.3
Package: bravo
Type: Package
Title: Bayesian Screening and Variable Selection
Version: 1.0.3
Author: Dongjin Li [aut, cre], Somak Dutta [aut], Vivekananda Roy [ctb]
Maintainer: Dongjin Li <dongjl@iastate.edu>
Description: Performs Bayesian variable selection with embedded screening for ultra-high dimensional Gaussian linear regression models. The methodology is described in Li, Dutta and Roy (2020) <arXiv:2006.07561>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0.2), methods
Imports: Rcpp (>= 1.0.2), Matrix (>= 1.2-17), compiler
LinkingTo: Rcpp
NeedsCompilation: yes
RoxygenNote: 7.1.1
Packaged: 2020-09-04 03:18:30 UTC; liyangxiaobei
Repository: CRAN
Date/Publication: 2020-09-22 08:20:06 UTC

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Package seqmagick updated to version 0.1.4 with previous version 0.1.3 dated 2019-12-19

Title: Sequence Manipulation Utilities
Description: Supports reading and writing sequences for different formats (currently interleaved and sequential formats for 'FASTA' and 'PHYLIP'), file conversion, and manipulation (e.g. filter sequences that contain specify pattern, export consensus sequence from an alignment).
Author: Guangchuang Yu [aut, cre] (<https://orcid.org/0000-0002-6485-8781>)
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

Diff between seqmagick versions 0.1.3 dated 2019-12-19 and 0.1.4 dated 2020-09-22

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Package polmineR updated to version 0.8.5 with previous version 0.8.4 dated 2020-07-22

Title: Verbs and Nouns for Corpus Analysis
Description: Package for corpus analysis using the Corpus Workbench ('CWB', <http://cwb.sourceforge.net/>) as an efficient back end for indexing and querying large corpora. The package offers functionality to flexibly create subcorpora and to carry out basic statistical operations (count, co-occurrences etc.). The original full text of documents can be reconstructed and inspected at any time. Beyond that, the package is intended to serve as an interface to packages implementing advanced statistical procedures. Respective data structures (document-term matrices, term-co-occurrence matrices etc.) can be created based on the indexed corpora.
Author: Andreas Blaette [aut, cre] (<https://orcid.org/0000-0001-8970-8010>), Christoph Leonhardt [ctb]
Maintainer: Andreas Blaette <andreas.blaette@uni-due.de>

Diff between polmineR versions 0.8.4 dated 2020-07-22 and 0.8.5 dated 2020-09-22

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Package tinytex updated to version 0.26 with previous version 0.25 dated 2020-07-24

Title: Helper Functions to Install and Maintain TeX Live, and Compile LaTeX Documents
Description: Helper functions to install and maintain the 'LaTeX' distribution named 'TinyTeX' (<https://yihui.org/tinytex/>), a lightweight, cross-platform, portable, and easy-to-maintain version of 'TeX Live'. This package also contains helper functions to compile 'LaTeX' documents, and install missing 'LaTeX' packages automatically.
Author: Yihui Xie [aut, cre, cph] (<https://orcid.org/0000-0003-0645-5666>), RStudio, PBC [cph], Fernando Cagua [ctb], Ethan Heinzen [ctb]
Maintainer: Yihui Xie <xie@yihui.name>

Diff between tinytex versions 0.25 dated 2020-07-24 and 0.26 dated 2020-09-22

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Package TestDimorph updated to version 0.3.4 with previous version 0.3.3 dated 2020-09-20

Title: Analysis Of The Interpopulation Difference In Degree of Sexual Dimorphism Using Summary Statistics
Description: Provides two approaches of comparison; the univariate and the multivariate analysis in two or more populations. Since the main obstacle of performing systematic comparisons in anthropological studies is the absence of raw data, the current package offer a solution for this problem by allowing the use of published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of Greene, D. L. (1989) <doi:10.1002/ajpa.1330790113> and Konigsberg, L. W. (1991) <doi:10.1002/ajpa.1330840110>.
Author: Bassam A. Abulnoor [aut, cre] (<https://orcid.org/0000-0003-4351-2754>), MennattAllah H. Attia [aut] (<https://orcid.org/0000-0002-2304-532X>), Lyle W. Konigsberg [aut] (<https://orcid.org/0000-0003-4052-1575>)
Maintainer: Bassam A. Abulnoor <bas12@fayoum.edu.eg>

Diff between TestDimorph versions 0.3.3 dated 2020-09-20 and 0.3.4 dated 2020-09-22

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New package TestCor with initial version 0.0.2.1
Package: TestCor
Title: FWER and FDR Controlling Procedures for Multiple Correlation Tests
Version: 0.0.2.1
Date: 2020-09-18
Authors@R: c( person("Gannaz", "Irene", email = "irene.gannaz@insa-lyon.fr", role = c("aut","cre")))
Maintainer: Gannaz Irene <irene.gannaz@insa-lyon.fr>
Description: Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.
Depends: R (>= 3.4)
Encoding: UTF-8
License: GPL (>= 2)
Imports: Rcpp, MASS, stats
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.1.1
NeedsCompilation: yes
Author: Gannaz Irene [aut, cre]
Packaged: 2020-09-21 12:33:36 UTC; igannaz
Repository: CRAN
Date/Publication: 2020-09-22 06:30:25 UTC

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New package rBiasCorrection with initial version 0.2.2
Package: rBiasCorrection
Title: Correct Bias in DNA Methylation Analyses
Version: 0.2.2
Authors@R: c(person(given = "Lorenz A.", family = "Kapsner", role = c("cre", "aut", "cph"), email = "lorenz.kapsner@gmail.com", comment = c(ORCID = "0000-0003-1866-860X")), person(given = "Evgeny A.", family = "Moskalev", role = "aut"))
Description: Implementation of the algorithms (with minor modifications) to correct bias in quantitative DNA methylation analyses as described by Moskalev et al. (2011) <doi:10.1093/nar/gkr213>.
License: GPL-3
URL: https://github.com/kapsner/rBiasCorrection
BugReports: https://github.com/kapsner/rBiasCorrection/issues
Depends: R (>= 2.10)
Imports: data.table, future, future.apply, ggplot2, ggpubr, magrittr, nls2, polynom, stats
Suggests: knitr, lintr, microbenchmark, rmarkdown, testthat
VignetteBuilder: knitr
Date/Publication: 2020-09-22 06:30:07 UTC
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-13 19:52:45 UTC; user
Author: Lorenz A. Kapsner [cre, aut, cph] (<https://orcid.org/0000-0003-1866-860X>), Evgeny A. Moskalev [aut]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner@gmail.com>
Repository: CRAN

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Package FHDI updated to version 1.4.1 with previous version 1.4.0 dated 2020-04-23

Title: Fractional Hot Deck and Fully Efficient Fractional Imputation
Description: Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) <doi:10.1093/biomet/asq073>.
Author: Inho Cho [aut, cre], Jaekwang Kim [aut], Jongho Im [aut], Yicheng Yang [aut]
Maintainer: Inho Cho <icho@iastate.edu>

Diff between FHDI versions 1.4.0 dated 2020-04-23 and 1.4.1 dated 2020-09-22

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New package faux with initial version 0.0.1.5
Package: faux
Title: Simulation for Factorial Designs
Version: 0.0.1.5
Date: 2020-09-11
Authors@R: c( person( given = "Lisa", family = "DeBruine", role = c("aut", "cre"), email = "debruine@gmail.com", comment = c(ORCID = "0000-0002-7523-5539") ), person( given = "Anna", family = "Krystalli", role = c("ctb"), email = "annakrystalli@googlemail.com", comment = c(ORCID = "0000-0002-2378-4915") ), person( given = "Andrew", family = "Heiss", role = c("ctb"), email = "andrew@andrewheiss.com", comment = c(ORCID = "0000-0002-3948-3914") ))
Description: Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://debruine.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.
Depends: R (>= 3.2.4)
Imports: lme4, dplyr, ggplot2 (>= 3.3.0), jsonlite, truncnorm
License: MIT + file LICENSE
Suggests: testthat (>= 2.1.0), tidyr, knitr, rmarkdown, roxygen2, covr, cowplot, ggExtra, purrr, broom, broom.mixed, psych
VignetteBuilder: knitr
RoxygenNote: 7.1.1
Encoding: UTF-8
LazyData: true
URL: https://github.com/debruine/faux
BugReports: https://github.com/debruine/faux/issues
NeedsCompilation: no
Packaged: 2020-09-21 13:33:30 UTC; lisad
Author: Lisa DeBruine [aut, cre] (<https://orcid.org/0000-0002-7523-5539>), Anna Krystalli [ctb] (<https://orcid.org/0000-0002-2378-4915>), Andrew Heiss [ctb] (<https://orcid.org/0000-0002-3948-3914>)
Maintainer: Lisa DeBruine <debruine@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-22 06:30:13 UTC

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Package DRIP updated to version 1.6 with previous version 1.5 dated 2020-09-10

Title: Discontinuous Regression and Image Processing
Description: This is a collection of functions for discontinuous regression analysis and image processing (DRIP). This version removes the dependence on the GNU Scientific Library.
Author: Yicheng Kang <kangx276@umn.edu>
Maintainer: Yicheng Kang <kangx276@umn.edu>

Diff between DRIP versions 1.5 dated 2020-09-10 and 1.6 dated 2020-09-22

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Package brant updated to version 0.3-0 with previous version 0.2-0 dated 2018-01-10

Title: Test for Parallel Regression Assumption
Description: Tests the parallel regression assumption wit the brant test by Brant (1990) <doi: 10.2307/2532457> for ordinal logit models generated with the function polr() from the package 'MASS'.
Author: Benjamin Schlegel [aut,cre], Marco Steenbergen [aut]
Maintainer: Benjamin Schlegel <kontakt@benjaminschlegel.ch>

Diff between brant versions 0.2-0 dated 2018-01-10 and 0.3-0 dated 2020-09-22

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

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

2020-09-20 1.0.1

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Package scipub updated to version 1.2.0 with previous version 1.1.0 dated 2020-06-04

Title: Summarize Data for Scientific Publication
Description: Create and format tables and APA statistics for scientific publication. This includes making a 'Table 1' to summarize demographics across groups, correlation tables with significance indicated by stars, and extracting formatted statistical summarizes from simple tests for in-text notation. The package also includes functions for Winsorizing data based on a Z-statistic cutoff.
Author: David Pagliaccio [aut, cre] (<https://orcid.org/0000-0002-1214-1965>)
Maintainer: David Pagliaccio <david.pagliaccio@gmail.com>

Diff between scipub versions 1.1.0 dated 2020-06-04 and 1.2.0 dated 2020-09-22

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Package readtext updated to version 0.80 with previous version 0.76 dated 2020-03-04

Title: Import and Handling for Plain and Formatted Text Files
Description: Functions for importing and handling text files and formatted text files with additional meta-data, such including '.csv', '.tab', '.json', '.xml', '.html', '.pdf', '.doc', '.docx', '.rtf', '.xls', '.xlsx', and others.
Author: Kenneth Benoit [aut, cre, cph], Adam Obeng [aut], Kohei Watanabe [ctb], Akitaka Matsuo [ctb], Paul Nulty [ctb], Stefan Müller [ctb]
Maintainer: Kenneth Benoit <kbenoit@lse.ac.uk>

Diff between readtext versions 0.76 dated 2020-03-04 and 0.80 dated 2020-09-22

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Package rdbnomics updated to version 0.6.3 with previous version 0.6.2 dated 2020-07-06

Title: Download DBnomics Data
Description: R access to hundreds of millions data series from DBnomics API (<https://db.nomics.world/>).
Author: Sebastien Galais [cre, ctb], Thomas Brand [aut]
Maintainer: Sebastien Galais <s915.stem@gmail.com>

Diff between rdbnomics versions 0.6.2 dated 2020-07-06 and 0.6.3 dated 2020-09-22

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Package krige updated to version 0.5.7 with previous version 0.5.6 dated 2020-06-20

Title: Geospatial Kriging with Metropolis Sampling
Description: Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.
Author: Jason S. Byers [aut, cre], Le Bao [aut], Jamie Carson [aut], Jeff Gill [aut]
Maintainer: Jason S. Byers <jaybyers55@gmail.com>

Diff between krige versions 0.5.6 dated 2020-06-20 and 0.5.7 dated 2020-09-22

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