Sun, 22 Nov 2020

Package SwimmeR updated to version 0.6.0 with previous version 0.5.0 dated 2020-10-19

Title: Data Import, Cleaning, and Conversions for Swimming Results
Description: The goal for of the 'SwimmeR' package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions. To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, 'ISL' results, and (on a development basis) '.hy3' files. Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community. 'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data. This is a developmental package, not yet mature.
Author: Greg Pilgrim [aut, cre] (<https://orcid.org/0000-0001-7831-442X>), Caitlin Baldwin [ctb]
Maintainer: Greg Pilgrim <gpilgrim2670@gmail.com>

Diff between SwimmeR versions 0.5.0 dated 2020-10-19 and 0.6.0 dated 2020-11-22

 SwimmeR-0.5.0/SwimmeR/inst/extdata/ISL_2019_CollegePark_Day_1.pdf                                            |only
 SwimmeR-0.5.0/SwimmeR/inst/extdata/ISL_2020_Match_1.pdf                                                      |only
 SwimmeR-0.5.0/SwimmeR/inst/extdata/Meet-Results-Nassau-County-Championships-09Feb2011-002.hy3                |only
 SwimmeR-0.5.0/SwimmeR/inst/extdata/TX_OH_Results.csv                                                         |only
 SwimmeR-0.5.0/SwimmeR/inst/extdata/df_standard.csv                                                           |only
 SwimmeR-0.5.0/SwimmeR/inst/extdata/hex_logo.png                                                              |only
 SwimmeR-0.5.0/SwimmeR/inst/extdata/logo.png                                                                  |only
 SwimmeR-0.6.0/SwimmeR/DESCRIPTION                                                                            |    6 
 SwimmeR-0.6.0/SwimmeR/LICENSE                                                                                |    4 
 SwimmeR-0.6.0/SwimmeR/MD5                                                                                    |  158 
 SwimmeR-0.6.0/SwimmeR/NAMESPACE                                                                              |  147 
 SwimmeR-0.6.0/SwimmeR/NEWS.md                                                                                |   97 
 SwimmeR-0.6.0/SwimmeR/R/Course_Convert.R                                                                     |  182 
 SwimmeR-0.6.0/SwimmeR/R/Course_Convert_DF.R                                                                  |  208 
 SwimmeR-0.6.0/SwimmeR/R/King200Breast.R                                                                      |   28 
 SwimmeR-0.6.0/SwimmeR/R/Read_Results.R                                                                       |  126 
 SwimmeR-0.6.0/SwimmeR/R/SwimR.R                                                                              |   12 
 SwimmeR-0.6.0/SwimmeR/R/Swim_Parse.R                                                                         | 2971 +++++-----
 SwimmeR-0.6.0/SwimmeR/R/add_row_numbers.R                                                                    |   34 
 SwimmeR-0.6.0/SwimmeR/R/collect_relay_swimmers.R                                                             |only
 SwimmeR-0.6.0/SwimmeR/R/discard_errors.R                                                                     |only
 SwimmeR-0.6.0/SwimmeR/R/dive_place.R                                                                         |   76 
 SwimmeR-0.6.0/SwimmeR/R/draw_bracket.R                                                                       |  632 +-
 SwimmeR-0.6.0/SwimmeR/R/event_parse.R                                                                        |  118 
 SwimmeR-0.6.0/SwimmeR/R/event_parse_ISL.R                                                                    |  118 
 SwimmeR-0.6.0/SwimmeR/R/fill_down.R                                                                          |only
 SwimmeR-0.6.0/SwimmeR/R/fill_left.R                                                                          |only
 SwimmeR-0.6.0/SwimmeR/R/fold.R                                                                               |   60 
 SwimmeR-0.6.0/SwimmeR/R/get_mode.R                                                                           |   92 
 SwimmeR-0.6.0/SwimmeR/R/globals.R                                                                            |  123 
 SwimmeR-0.6.0/SwimmeR/R/hy3_places.R                                                                         |  154 
 SwimmeR-0.6.0/SwimmeR/R/hy3_times.R                                                                          |  158 
 SwimmeR-0.6.0/SwimmeR/R/interleave_results.R                                                                 |   72 
 SwimmeR-0.6.0/SwimmeR/R/is_link_broken.R                                                                     |only
 SwimmeR-0.6.0/SwimmeR/R/lines_sort.R                                                                         |only
 SwimmeR-0.6.0/SwimmeR/R/list_transform.R                                                                     |only
 SwimmeR-0.6.0/SwimmeR/R/mmss_format.R                                                                        |   52 
 SwimmeR-0.6.0/SwimmeR/R/not_in.R                                                                             |   48 
 SwimmeR-0.6.0/SwimmeR/R/parse_hy3.R                                                                          |  991 +--
 SwimmeR-0.6.0/SwimmeR/R/relay_aggregate.R                                                                    |   17 
 SwimmeR-0.6.0/SwimmeR/R/results_score.R                                                                      |  760 +-
 SwimmeR-0.6.0/SwimmeR/R/sec_format.R                                                                         |   56 
 SwimmeR-0.6.0/SwimmeR/R/sec_format_helper.R                                                                  |   56 
 SwimmeR-0.6.0/SwimmeR/R/splits_parse.R                                                                       |only
 SwimmeR-0.6.0/SwimmeR/R/splits_parse_ISL.R                                                                   |only
 SwimmeR-0.6.0/SwimmeR/R/splits_reform.R                                                                      |only
 SwimmeR-0.6.0/SwimmeR/R/swim_parse_ISL.R                                                                     |  720 +-
 SwimmeR-0.6.0/SwimmeR/R/swim_place.R                                                                         |   70 
 SwimmeR-0.6.0/SwimmeR/R/tie_rescore.R                                                                        |   82 
 SwimmeR-0.6.0/SwimmeR/R/utils-pipe.R                                                                         |   22 
 SwimmeR-0.6.0/SwimmeR/README.md                                                                              |  263 
 SwimmeR-0.6.0/SwimmeR/build/vignette.rds                                                                     |binary
 SwimmeR-0.6.0/SwimmeR/inst/CITATION                                                                          |only
 SwimmeR-0.6.0/SwimmeR/inst/doc/SwimmeR.R                                                                     |  239 
 SwimmeR-0.6.0/SwimmeR/inst/doc/SwimmeR.Rmd                                                                   |  464 -
 SwimmeR-0.6.0/SwimmeR/inst/doc/SwimmeR.html                                                                  | 1209 ++--
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 SwimmeR-0.6.0/SwimmeR/inst/extdata/TX_OH_Results.rds                                                         |only
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 SwimmeR-0.6.0/SwimmeR/inst/extdata/s2-results.pdf                                                            |only
 SwimmeR-0.6.0/SwimmeR/inst/extdata/ttsc10262018results-rev2.pdf                                              |only
 SwimmeR-0.6.0/SwimmeR/man/King200Breast.Rd                                                                   |   38 
 SwimmeR-0.6.0/SwimmeR/man/Read_Results.Rd                                                                    |   64 
 SwimmeR-0.6.0/SwimmeR/man/Swim_Parse.Rd                                                                      |  120 
 SwimmeR-0.6.0/SwimmeR/man/SwimmeR.Rd                                                                         |   26 
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 SwimmeR-0.6.0/SwimmeR/man/discard_errors.Rd                                                                  |only
 SwimmeR-0.6.0/SwimmeR/man/event_parse_ISL.Rd                                                                 |   46 
 SwimmeR-0.6.0/SwimmeR/man/fill_down.Rd                                                                       |only
 SwimmeR-0.6.0/SwimmeR/man/fill_left.Rd                                                                       |only
 SwimmeR-0.6.0/SwimmeR/man/get_mode.Rd                                                                        |   82 
 SwimmeR-0.6.0/SwimmeR/man/hy3_places.Rd                                                                      |   56 
 SwimmeR-0.6.0/SwimmeR/man/hy3_times.Rd                                                                       |   50 
 SwimmeR-0.6.0/SwimmeR/man/interleave_results.Rd                                                              |   54 
 SwimmeR-0.6.0/SwimmeR/man/is_link_broken.Rd                                                                  |only
 SwimmeR-0.6.0/SwimmeR/man/lines_sort.Rd                                                                      |only
 SwimmeR-0.6.0/SwimmeR/man/list_transform.Rd                                                                  |only
 SwimmeR-0.6.0/SwimmeR/man/mmss_format.Rd                                                                     |   60 
 SwimmeR-0.6.0/SwimmeR/man/parse_hy3.Rd                                                                       |   72 
 SwimmeR-0.6.0/SwimmeR/man/results_score.Rd                                                                   |  174 
 SwimmeR-0.6.0/SwimmeR/man/sec_format.Rd                                                                      |   62 
 SwimmeR-0.6.0/SwimmeR/man/sec_format_helper.Rd                                                               |   34 
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 SwimmeR-0.6.0/SwimmeR/man/splits_reform.Rd                                                                   |only
 SwimmeR-0.6.0/SwimmeR/man/swim_parse_ISL.Rd                                                                  |   72 
 SwimmeR-0.6.0/SwimmeR/tests/testthat.R                                                                       |    8 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-Course_Convert.R                                                   |    6 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-Course_Convert_DF.R                                                |   22 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-ISL.R                                                              |   96 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-Read_Results_works.R                                               |   44 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-Swim_Parse_works.R                                                 |  287 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-get_mode.R                                                         |   20 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-hy3_parse_works.R                                                  |   18 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-results_score_works.R                                              |  177 
 SwimmeR-0.6.0/SwimmeR/tests/testthat/test-splits.R                                                           |only
 SwimmeR-0.6.0/SwimmeR/vignettes/SwimmeR.Rmd                                                                  |  464 -
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Package npsf updated to version 0.8.0 with previous version 0.7.1 dated 2020-06-24

Title: Nonparametric and Stochastic Efficiency and Productivity Analysis
Description: Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.
Author: Oleg Badunenko [aut, cre], Pavlo Mozharovskyi [aut], Yaryna Kolomiytseva [aut]
Maintainer: Oleg Badunenko <oleg.badunenko@brunel.ac.uk>

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New package GWmodel with initial version 2.2-2
Package: GWmodel
Type: Package
Version: 2.2-2
Date: 2020-11-09
Title: Geographically-Weighted Models
Depends: R (>= 3.0.0),maptools (>= 0.5-2), robustbase,sp (> 1.4-0),Rcpp,spatialreg
Imports: methods, grDevices, stats,graphics,spacetime,spdep,FNN
LinkingTo: Rcpp, RcppArmadillo
Suggests: mvoutlier, RColorBrewer, gstat,spData
Description: Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.
Author: Binbin Lu[aut], Paul Harris[aut], Martin Charlton[aut], Chris Brunsdon[aut], Tomoki Nakaya[aut], Daisuke Murakami[aut],Isabella Gollini[ctb], Yigong Hu[ctb], Fiona H Evans[ctb]
Maintainer: Binbin Lu <binbinlu@whu.edu.cn>
License: GPL (>= 2)
Repository: CRAN
URL: http://gwr.nuim.ie/
NeedsCompilation: yes
SystemRequirements: GNU make
Packaged: 2020-11-13 13:20:08 UTC; Lenovo
Date/Publication: 2020-11-22 22:30:02 UTC

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Package tidymodels updated to version 0.1.2 with previous version 0.1.1 dated 2020-07-14

Title: Easily Install and Load the 'Tidymodels' Packages
Description: The tidy modeling "verse" is a collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse.
Author: Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph, fnd]
Maintainer: Max Kuhn <max@rstudio.com>

Diff between tidymodels versions 0.1.1 dated 2020-07-14 and 0.1.2 dated 2020-11-22

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New package kofdata with initial version 0.1.4
Package: kofdata
Type: Package
Version: 0.1.4
Title: Get Data from the 'KOF Datenservice' API
Description: Read Swiss time series data from the 'KOF Datenservice' API, <https://datenservice.kof.ethz.ch>. The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token.
Authors@R: c( person("Matthias", "Bannert", , "bannert@kof.ethz.ch", c("aut", "cre")), person("Severin", "Thoeni", , "thoenis@kof.ethz.ch", "aut"))
Depends: R (>= 3.0.0), jsonlite (>= 1.1), httr
Imports: xts, zoo
URL: https://github.com/KOF-ch/kofdata
BugReports: https://github.com/KOF-ch/kofdata/issues
Date: 2020-11-12
License: GPL-2
LazyData: true
RoxygenNote: 7.1.1
Suggests: testthat
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2020-11-11 23:41:41 UTC; mbannert
Author: Matthias Bannert [aut, cre], Severin Thoeni [aut]
Maintainer: Matthias Bannert <bannert@kof.ethz.ch>
Repository: CRAN
Date/Publication: 2020-11-22 22:00:02 UTC

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Package coxmeg updated to version 1.0.12 with previous version 1.0.11 dated 2020-05-05

Title: Cox Mixed-Effects Models for Genome-Wide Association Studies
Description: Fast algorithms for fitting a Cox mixed-effects model for e.g., genome-wide association studies. See Liang He and Alexander Kulminski (2019) <doi:10.1101/729285>.
Author: Liang He
Maintainer: Liang He <liang.he@duke.edu>

Diff between coxmeg versions 1.0.11 dated 2020-05-05 and 1.0.12 dated 2020-11-22

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Package PACVr updated to version 0.9.4 with previous version 0.9.2 dated 2020-02-20

Title: Plastome Assembly Coverage Visualization
Description: Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2020) <doi:10.1186/s12859-020-3475-0>.
Author: Michael Gruenstaeudl [aut, cre], Nils Jenke [ctb]
Maintainer: Michael Gruenstaeudl <m.gruenstaeudl@fu-berlin.de>

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New package modmarg with initial version 0.9.6
Package: modmarg
Title: Calculating Marginal Effects and Levels with Errors
Version: 0.9.6
Authors@R: c( person("Alex", "Gold", email = "alex.k.gold@gmail.com", role = "aut"), person("Nat", "Olin", email = "nathaniel.olin@gmail.com", role = "aut"), person("Annie", "Wang", email = "anniejw6@gmail.com", role = c("aut", "cre")) )
Description: Calculate predicted levels and marginal effects, using the delta method to calculate standard errors. This is an R-based version of the 'margins' command from Stata.
URL: https://github.com/anniejw6/modmarg
BugReports: https://github.com/anniejw6/modmarg/issues
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
Suggests: knitr, rmarkdown, testthat, sandwich, AER
VignetteBuilder: knitr
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-11-21 20:20:28 UTC; ajw
Author: Alex Gold [aut], Nat Olin [aut], Annie Wang [aut, cre]
Maintainer: Annie Wang <anniejw6@gmail.com>
Repository: CRAN
Date/Publication: 2020-11-22 21:00:02 UTC

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Package modelStudio updated to version 2.1.0 with previous version 2.0.0 dated 2020-09-10

Title: Interactive Studio for Explanatory Model Analysis
Description: Automate the explanatory analysis of machine learning predictive models. Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model agnostic, therefore compatible with most of the black box predictive models and frameworks. The main function computes various (instance and dataset level) model explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. Easily save the dashboard and share it with others. Tools for Explanatory Model Analysis unite with tools for Exploratory Data Analysis to give a broad overview of the model behavior.
Author: Hubert Baniecki [aut, cre] (<https://orcid.org/0000-0001-6661-5364>), Przemyslaw Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>), Piotr Piatyszek [ctb]
Maintainer: Hubert Baniecki <hbaniecki@gmail.com>

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Package clusterGeneration updated to version 1.3.6 with previous version 1.3.5 dated 2020-10-05

Title: Random Cluster Generation (with Specified Degree of Separation)
Description: We developed the clusterGeneration package to provide functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.
Author: Weiliang Qiu <stwxq@channing.harvard.edu>, Harry Joe <harry@stat.ubc.ca>.
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>

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More information about clusterGeneration at CRAN
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Package globals updated to version 0.14.0 with previous version 0.13.1 dated 2020-10-11

Title: Identify Global Objects in R Expressions
Description: Identifies global ("unknown" or "free") objects in R expressions by code inspection using various strategies (ordered, liberal, or conservative). The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in parallel, distributed compute environments.
Author: Henrik Bengtsson [aut, cre, cph], Davis Vaughan [ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between globals versions 0.13.1 dated 2020-10-11 and 0.14.0 dated 2020-11-22

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More information about globals at CRAN
Permanent link

Package GGIR updated to version 2.2-0 with previous version 2.1-0 dated 2020-08-27

Title: Raw Accelerometer Data Analysis
Description: A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' <https://www.activinsights.com/> and GENEA devices (not for sale), .csv-export data from 'Actigraph' <https://actigraphcorp.com> devices, and .cwa and .wav-format data from 'Axivity' <https://axivity.com>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format and has either no header or a two column header. Also the package allows for external function embedding.
Author: Vincent T van Hees [aut, cre], Zhou Fang [ctb], Jing Hua Zhao [ctb], Joe Heywood [ctb], Evgeny Mirkes [ctb], Severine Sabia [ctb], Joan Capdevila Pujol [ctb], Jairo H Migueles [ctb], Matthew R Patterson [ctb], Dan Jackson [ctb]
Maintainer: Vincent T van Hees <v.vanhees@accelting.com>

Diff between GGIR versions 2.1-0 dated 2020-08-27 and 2.2-0 dated 2020-11-22

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Package EpiNow2 updated to version 1.3.1 with previous version 1.2.1 dated 2020-10-20

Title: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters
Description: Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is under active development.
Author: Sam Abbott [aut, cre] (<https://orcid.org/0000-0001-8057-8037>), Joel Hellewell [aut] (<https://orcid.org/0000-0003-2683-0849>), Robin Thompson [aut], Katelyn Gostic [aut], Katharine Sherratt [aut], Hamada S. Badr [aut] (<https://orcid.org/0000-0002-9808-2344>), Michael DeWitt [aut] (<https://orcid.org/0000-0001-8940-1967>), Sophie Meakin [aut], James Munday [aut], Nikos Bosse [aut], Joe Hickson [aut], Paul Mee [ctb], Peter Ellis [ctb], EpiForecasts [aut], Sebastian Funk [aut]
Maintainer: Sam Abbott <sam.abbott@lshtm.ac.uk>

Diff between EpiNow2 versions 1.2.1 dated 2020-10-20 and 1.3.1 dated 2020-11-22

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Package cbinom updated to version 1.5 with previous version 1.3 dated 2018-06-27

Title: Continuous Analog of a Binomial Distribution
Description: Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete binomial with continuous size parameter and continuous support with x in [0, size + 1], following Ilienko (2013) <arXiv:1303.5990>.
Author: Dan Dalthorp
Maintainer: Dan Dalthorp <ddalthorp@protonmail.com>

Diff between cbinom versions 1.3 dated 2018-06-27 and 1.5 dated 2020-11-22

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Package BSL updated to version 3.2.0 with previous version 3.0.0 dated 2019-07-10

Title: Bayesian Synthetic Likelihood
Description: Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two shrinkage estimators (graphical lasso and Warton's estimator). uBSL (Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust to non-normal summary statistics. BSLmisspec (Frazier et al. 2019 <arXiv:1904.04551>) estimates the Gaussian synthetic likelihood whilst acknowledging that there may be incompatibility between the model and the observed summary statistic. Shrinkage estimation can help to decrease the number of model simulations when the dimension of the summary statistic is high (e.g., BSLasso, An et al. (2019) <doi:10.1080/10618600.2018.1537928>). Extensions to this package are planned.
Author: Ziwen An [aut, cre] (<https://orcid.org/0000-0002-9947-5182>), Leah F. South [aut] (<https://orcid.org/0000-0002-5646-2963>), Christopher C. Drovandi [aut] (<https://orcid.org/0000-0001-9222-8763>)
Maintainer: Ziwen An <ziwen.an@hdr.qut.edu.au>

Diff between BSL versions 3.0.0 dated 2019-07-10 and 3.2.0 dated 2020-11-22

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Package BatchGetSymbols updated to version 2.6 with previous version 2.5.9 dated 2020-11-17

Title: Downloads and Organizes Financial Data for Multiple Tickers
Description: Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.
Author: Marcelo Perlin [aut, cre]
Maintainer: Marcelo Perlin <marceloperlin@gmail.com>

Diff between BatchGetSymbols versions 2.5.9 dated 2020-11-17 and 2.6 dated 2020-11-22

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Package gausscov updated to version 0.0.10 with previous version 0.0.9 dated 2020-11-07

Title: The Gaussian Covariate Method for Variable Selection
Description: Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets. Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", 2020, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633>.
Author: Laurie Davies [aut, cre]
Maintainer: Laurie Davies <laurie.davies@uni-due.de>

Diff between gausscov versions 0.0.9 dated 2020-11-07 and 0.0.10 dated 2020-11-22

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Package ggpval updated to version 0.2.4 with previous version 0.2.3 dated 2019-09-10

Title: Annotate Statistical Tests for 'ggplot2'
Description: Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and adds the resulting p-values to the plot with an annotation bar. Visualizing group differences are frequently performed by boxplots, bar plots, etc. Statistical test results are often needed to be annotated on these plots. This package provides a convenient function that works on 'ggplot2' objects, performs the desired statistical test between groups of interest and annotates the test results on the plot.
Author: Jun Cheng [aut, cre]
Maintainer: Jun Cheng <s6juncheng@gmail.com>

Diff between ggpval versions 0.2.3 dated 2019-09-10 and 0.2.4 dated 2020-11-22

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Package fingertipscharts (with last version 0.0.11) was removed from CRAN

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

2020-06-05 0.0.11
2019-10-07 0.0.10
2019-09-09 0.0.9
2019-07-18 0.0.8
2019-05-13 0.0.6
2019-04-08 0.0.5
2019-02-07 0.0.4
2018-12-13 0.0.3
2018-09-14 0.0.2
2018-07-28 0.0.1

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Package FloodMapper (with last version 1.0) was removed from CRAN

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

2018-05-27 1.0

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Package LearnClust (with last version 1.0) was removed from CRAN

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

2020-09-30 1.0

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Package AWAPer (with last version 0.1.44) was removed from CRAN

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

2020-11-10 0.1.44
2020-11-07 0.1.43
2020-02-01 0.1.3

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Package LatticeDesign (with last version 2.0-1) was removed from CRAN

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

2020-11-19 2.0-1
2018-08-17 1.0-1

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Package exPrior (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:

2019-11-14 1.0.1
2019-11-13 1.0.0

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Package SQRL updated to version 0.7.4 with previous version 0.7.3 dated 2020-08-27

Title: Facilitates ODBC Database Interaction
Description: Facilitates interaction with ODBC data sources, by remembering and hiding communication settings and supporting parameterised multi-statement SQL with embedded R.
Author: Mike Lee
Maintainer: Mike Lee <random.deviate@gmail.com>

Diff between SQRL versions 0.7.3 dated 2020-08-27 and 0.7.4 dated 2020-11-22

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