Tue, 17 Mar 2020

Package gadget2 updated to version 2.3.5 with previous version 2.3.4 dated 2020-03-13

Title: Gadget is the Globally-Applicable Area Disaggregated General Ecosystem Toolbox
Description: A statistical ecosystem modelling package, taking many features of the ecosystem into account. Gadget works by running an internal model based on many parameters, and then comparing the data from the output of this model to real data to get a goodness-of-fit likelihood score. These parameters can then be adjusted, and the model re-run, until an optimum is found, which corresponds to the model with the lowest likelihood score. Gadget allows the user to include a number of features into an ecosystem model: One or more species, each of which may be split into multiple stocks; multiple areas with migration between areas; predation between and within species; maturation; reproduction and recruitment; multiple commercial and survey fleets taking catches from the populations. For more details see <https://hafro.github.io/gadget2>. This is the C++ Gadget2 runtime, making it available for R.
Author: Bjarki Thor Elvarsson [aut, cre], James Begley [aut], Hoskuldur Bjornsson [aut], Jamie Lentin [ctb], Gunnar Stefnasson [ctb], Lorna Taylor [ctb], Daniel Howell [ctb], Sigurdur Hannesson [ctb], Narfi Stefansson [aut], Hersir Sigurgeirsson [ctb], Morten Nygard Asnes [ctb], Kristin Froysa [ctb], Audbjorg Jakobsdottir [ctb], Jon Gudmundsson [ctb], Gudmundur Einarsson [ctb], Thordis Linda Thorarinsdottir [ctb], Kristjana Yr Jonsdottir [ctb], Mark G. Johnson [ctb, cph], Bill Goffe [ctb, cph], Marine and Freshwater Research Institute (Iceland) [cph]
Maintainer: Bjarki Thor Elvarsson <bjarki.elvarsson@hafogvatn.is>

Diff between gadget2 versions 2.3.4 dated 2020-03-13 and 2.3.5 dated 2020-03-17

 gadget2-2.3.4/gadget2/src/Makefile         |only
 gadget2-2.3.5/gadget2/DESCRIPTION          |    8 ++++----
 gadget2-2.3.5/gadget2/MD5                  |   10 +++++-----
 gadget2-2.3.5/gadget2/README.md            |   17 +++++++++++++++++
 gadget2-2.3.5/gadget2/src/Makevars         |only
 gadget2-2.3.5/gadget2/src/Makevars.win     |    4 ++--
 gadget2-2.3.5/gadget2/src/include/gadget.h |    2 +-
 7 files changed, 29 insertions(+), 12 deletions(-)

More information about gadget2 at CRAN
Permanent link

Package diffdf updated to version 1.0.4 with previous version 1.0.3 dated 2019-03-12

Title: Dataframe Difference Tool
Description: Functions for comparing two data.frames against each other. The core functionality is to provide a detailed breakdown of any differences between two data.frames as well as providing utility functions to help narrow down the source of problems and differences.
Author: Craig Gower-Page [cre, aut], Kieran Martin [aut]
Maintainer: Craig Gower-Page <craig.gower-page@roche.com>

Diff between diffdf versions 1.0.3 dated 2019-03-12 and 1.0.4 dated 2020-03-17

 DESCRIPTION                    |   14 
 MD5                            |   36 -
 NAMESPACE                      |    2 
 NEWS.md                        |    5 
 R/ascii_tables.R               |    4 
 R/diffdf.R                     |    1 
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 README.md                      |    4 
 build/vignette.rds             |binary
 inst/doc/diffdf-basic.R        |   41 -
 inst/doc/diffdf-basic.Rmd      |   17 
 inst/doc/diffdf-basic.html     |  938 +++++++++++++++++++++++++----------------
 man/cast_variables.Rd          |    9 
 man/compare_vectors.numeric.Rd |    8 
 man/diffdf.Rd                  |   14 
 man/generate_keyname.Rd        |    7 
 man/identify_differences.Rd    |   10 
 tests/testthat/test-core.R     |    2 
 vignettes/diffdf-basic.Rmd     |   17 
 19 files changed, 672 insertions(+), 461 deletions(-)

More information about diffdf at CRAN
Permanent link

Package vein updated to version 0.8.8 with previous version 0.8.7 dated 2020-02-18

Title: Vehicular Emissions Inventories
Description: Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>. Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the function inventory to create a structure of directories and template scripts.
Author: Sergio Ibarra-Espinosa [aut, cre] (<https://orcid.org/0000-0002-3162-1905>)
Maintainer: Sergio Ibarra-Espinosa <sergio.ibarra@usp.br>

Diff between vein versions 0.8.7 dated 2020-02-18 and 0.8.8 dated 2020-03-17

 DESCRIPTION                  |   16 --
 MD5                          |   80 ++++++-----
 NAMESPACE                    |    7 -
 NEWS.md                      |   12 +
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 R/GriddedEmissionsArray.R    |   65 +++++++--
 R/Vehicles.R                 |   47 ++++++
 R/age.R                      |    2 
 R/age_hdv.R                  |   68 ++++++---
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 R/temp_fact.R                |    9 +
 R/vein_notes.R               |  298 ++++++++++++++++++++++---------------------
 inst/README.Rmd              |    2 
 man/Emissions.Rd             |    8 -
 man/GriddedEmissionsArray.Rd |   14 +-
 man/Vehicles.Rd              |    4 
 man/age.Rd                   |    7 +
 man/age_hdv.Rd               |   12 +
 man/age_ldv.Rd               |   12 +
 man/age_moto.Rd              |   12 +
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 man/vein-package.Rd          |    5 
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 44 files changed, 954 insertions(+), 570 deletions(-)

More information about vein at CRAN
Permanent link

Package mongolite updated to version 2.2.0 with previous version 2.1.0 dated 2019-05-09

Title: Fast and Simple 'MongoDB' Client for R
Description: High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS. The online user manual provides an overview of the available methods in the package: <https://jeroen.github.io/mongolite/>.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), MongoDB, Inc [cph] (Bundled mongo-c-driver, see AUTHORS file)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between mongolite versions 2.1.0 dated 2019-05-09 and 2.2.0 dated 2020-03-17

 DESCRIPTION                                             |    8 
 MD5                                                     |  629 +++++----
 NEWS                                                    |    6 
 R/mongo.R                                               |    3 
 R/ssl.R                                                 |    7 
 cleanup                                                 |    2 
 configure                                               |   24 
 man/gridfs.Rd                                           |    8 
 man/mongo.Rd                                            |   12 
 man/mongo_options.Rd                                    |    3 
 man/ssl_options.Rd                                      |   12 
 src/Makevars.in                                         |    3 
 src/Makevars.win                                        |    3 
 src/bson/bcon.c                                         |    4 
 src/bson/bcon.h                                         |    4 
 src/bson/bson-atomic.c                                  |    2 
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 src/bson/bson-clock.h                                   |    8 
 src/bson/bson-compat.h                                  |    8 
 src/bson/bson-context-private.h                         |    4 
 src/bson/bson-context.c                                 |   12 
 src/bson/bson-context.h                                 |    6 
 src/bson/bson-decimal128.c                              |    8 
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 src/bson/bson-endian.h                                  |    8 
 src/bson/bson-error.c                                   |   12 
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 src/bson/bson-iso8601-private.h                         |    8 
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 src/bson/bson-md5.c                                     |    4 
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 src/bson/bson-value.c                                   |    8 
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 src/bson/bson-version-functions.c                       |    4 
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 src/bson/bson-writer.c                                  |    4 
 src/bson/bson-writer.h                                  |    4 
 src/bson/bson.c                                         |   23 
 src/bson/bson.h                                         |   46 
 src/client.c                                            |    2 
 src/common/common-b64.c                                 |   10 
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 src/common/common-prelude.h                             |    1 
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 src/jsonsl/jsonsl.c                                     |   22 
 src/mongoc/mongoc-aggregate-private.h                   |only
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 src/mongoc/mongoc-apm-private.h                         |    4 
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 src/mongoc/mongoc-array.c                               |    2 
 src/mongoc/mongoc-async-cmd-private.h                   |   14 
 src/mongoc/mongoc-async-cmd.c                           |   24 
 src/mongoc/mongoc-async-private.h                       |    4 
 src/mongoc/mongoc-async.c                               |   12 
 src/mongoc/mongoc-buffer-private.h                      |    4 
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 src/mongoc/mongoc-bulk-operation-private.h              |    8 
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 src/mongoc/mongoc-bulk-operation.h                      |    6 
 src/mongoc/mongoc-change-stream-private.h               |   22 
 src/mongoc/mongoc-change-stream.c                       |  212 ++-
 src/mongoc/mongoc-change-stream.h                       |    7 
 src/mongoc/mongoc-client-pool-private.h                 |    8 
 src/mongoc/mongoc-client-pool.c                         |   37 
 src/mongoc/mongoc-client-pool.h                         |   18 
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 src/mongoc/mongoc-client-side-encryption.c              |only
 src/mongoc/mongoc-client-side-encryption.h              |only
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 src/mongoc/mongoc-client.h                              |   39 
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 src/mongoc/mongoc-cluster-sspi-private.h                |    6 
 src/mongoc/mongoc-cluster-sspi.c                        |   41 
 src/mongoc/mongoc-cluster.c                             |  347 +++--
 src/mongoc/mongoc-cmd-private.h                         |   26 
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 src/mongoc/mongoc-collection.c                          |  328 +----
 src/mongoc/mongoc-collection.h                          |   22 
 src/mongoc/mongoc-compression-private.h                 |    5 
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 src/mongoc/mongoc-counters-private.h                    |    5 
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 src/mongoc/mongoc-cursor-private.h                      |   32 
 src/mongoc/mongoc-cursor.c                              |  107 +
 src/mongoc/mongoc-cursor.h                              |    6 
 src/mongoc/mongoc-cyrus-private.h                       |    8 
 src/mongoc/mongoc-cyrus.c                               |   10 
 src/mongoc/mongoc-database-private.h                    |   10 
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 src/mongoc/mongoc-flags.h                               |    2 
 src/mongoc/mongoc-gridfs-bucket-file-private.h          |    8 
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 src/mongoc/mongoc-gridfs-file-list.c                    |   20 
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 src/mongoc/mongoc-opcode.h                              |    2 
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 src/mongoc/mongoc-queue-private.h                       |    4 
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 src/mongoc/mongoc-rand-cng.c                            |    8 
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 src/mongoc/mongoc-stream-tls.c                          |   30 
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 src/mongoc/mongoc-thread-private.h                      |    6 
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 src/mongoc/mongoc-trace-private.h                       |    6 
 src/mongoc/mongoc-uri-private.h                         |   20 
 src/mongoc/mongoc-uri.c                                 | 1039 +++++++++++-----
 src/mongoc/mongoc-uri.h                                 |   49 
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 src/mongoc/mongoc-version-functions.c                   |    4 
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Package broomExtra updated to version 2.5.0 with previous version 2.0.0 dated 2020-02-22

Title: Enhancements for 'broom' and 'easystats' Package Families
Description: Provides helper functions that assist in data analysis workflows involving regression analyses. The goal is to combine the functionality offered by different set of packages ('broom', 'broom.mixed', 'parameters', and 'performance') through a common syntax to return tidy dataframes containing model parameters and performance measure summaries. The 'grouped_' variants of the generics provides a convenient way to execute functions across a combination of grouping variable(s) in a dataframe.
Author: Indrajeet Patil [aut, cre, ctb] (<https://orcid.org/0000-0003-1995-6531>)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package schrute updated to version 0.2.0 with previous version 0.1.1 dated 2020-01-08

Title: The Entire Transcript from the Office in Tidy Format
Description: The complete scripts from the American version of the Office television show in tibble format. Use this package to analyze and have fun with text from the best series of all time.
Author: Brad Lindblad [aut, cre], Lucas Graybuck [ctb]
Maintainer: Brad Lindblad <bradley.lindblad@gmail.com>

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Package ggraph updated to version 2.0.2 with previous version 2.0.1 dated 2020-02-07

Title: An Implementation of Grammar of Graphics for Graphs and Networks
Description: The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. ggraph is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer.
Author: Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), RStudio [cph]
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>

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Package tensorregress updated to version 2.0 with previous version 1.1 dated 2020-02-13

Title: Generalized Tensor Regression with Covariates on Multiple Modes
Description: Implement the generalized tensor regression in Xu, Hu and Wang (2019) <arXiv:1910.09499>. Solve tensor-response regression given covariates on multiple modes with alternating updating algorithm.
Author: Zhuoyan Xu [aut, cre, cph], Jiaxin Hu [aut, cph], Miaoyan Wang [aut, cph]
Maintainer: Zhuoyan Xu <zxu444@stat.wisc.edu>

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Package groupedstats updated to version 0.2.1 with previous version 0.2.0 dated 2020-02-28

Title: Grouped Statistical Analyses in a Tidy Way
Description: Collection of functions to run statistical tests across all combinations of multiple grouping variables.
Author: Indrajeet Patil [aut, cre] (<https://orcid.org/0000-0003-1995-6531>)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package geouy updated to version 0.1.8 with previous version 0.1.7 dated 2020-03-04

Title: Geographic Information of Uruguay
Description: The toolbox have functions to load and process geographic information for Uruguay. And extra-function to get address coordinates and orthophotos through the uruguayan 'IDE' API <https://www.gub.uy/infraestructura-datos-espaciales/tramites-y-servicios/servicios/servicio-direcciones-geograficas>.
Author: Richard Detomasi [aut, cre] (<https://orcid.org/0000-0002-6725-0261>), Ministerio de Desarrollo Social, Uruguay (MIDES) [dtc], Infraestrutura de Datos Espaciales, Uruguay (IDE) [dtc], Servicio Geográfico Militar, Uruguay (SGM) [dtc], Ministerio de Transporte y Obras Públicas, Uruguay (MTOP) [dtc], Instituto Nacional de Estadística, Uruguay (INE) [dtc], Instituto Geográfico Militar, Uruguay (IGM) [dtc], Ministerio de Vivienda, Ordenamiento Territorial y Medio Ambiente, Uruguay (MVOTMA) [dtc]
Maintainer: Richard Detomasi <richard.detomasi@gmail.com>

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Package yardstick updated to version 0.0.6 with previous version 0.0.5 dated 2020-01-23

Title: Tidy Characterizations of Model Performance
Description: Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).
Author: Max Kuhn [aut], Davis Vaughan [aut, cre], RStudio [cph]
Maintainer: Davis Vaughan <davis@rstudio.com>

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Package rtemps updated to version 0.4.0 with previous version 0.3.0 dated 2020-03-08

Title: R Templates for Reproducible Data Analyses
Description: A collection of R Markdown templates for nicely structured, reproducible data analyses in R. The templates have embedded examples on how to write citations, footnotes, equations and use colored message/info boxes, how to cross-reference different parts/sections in the report, provide a nice table of contents (toc) with a References section and proper R session information as well as examples using DT tables and ggplot2 graphs. The bookdown Lite template theme supports code folding.
Author: John Zobolas [aut, cph, cre] (<https://orcid.org/0000-0002-3609-8674>)
Maintainer: John Zobolas <bblodfon@gmail.com>

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Package kza updated to version 4.1.0.1 with previous version 4.1.0 dated 2018-10-28

Title: Kolmogorov-Zurbenko Adaptive Filters
Description: Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
Author: Brian Close <brian.close@gmail.com>, Igor Zurbenko <IZurbenko@albany.edu> and Mingzeng Sun <msun@albany.edu>
Maintainer: Brian Close <brian.close@gmail.com>

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New package hesim with initial version 0.2.3
Package: hesim
Type: Package
Title: Health-Economic Simulation Modeling and Decision Analysis
Version: 0.2.3
Authors@R: c( person("Devin", "Incerti", , "devin.incerti@gmail.com", role = c("aut", "cre")), person("Jeroen P.", "Jansen", role = "aut"), person(given = "R Core Team", role = "ctb", comment="hesim uses some slightly modified C functions from base R") )
Description: Parameterize, simulate, and analyze health-economic simulation models. Supports N-state partitioned survival models (Glasziou et al. 1990) <doi:10.1002/sim.4780091106> and continuous time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014> parameterized using survival or multi-state modeling (de Wreede et al. 2011, Jackson 2015) <doi:10.18637/jss.v038.i07>, <doi:10.18637/jss.v070.i08>. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, <doi:10.1111/j.1524-4733.2008.00358.x>. Simulation code written in C++ to boost performance.
URL: https://github.com/hesim-dev/hesim
BugReports: https://github.com/hesim-dev/hesim/issues
License: GPL-3
LazyData: true
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.5.0)
Imports: data.table, flexsurv, MASS, Rcpp (>= 0.12.16), R6, stats, survival
Suggests: covr, ggplot2, knitr, mstate, msm, numDeriv, pracma, rmarkdown, scales, testthat, truncnorm
VignetteBuilder: knitr
RoxygenNote: 7.0.1
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2020-03-16 23:41:31 UTC; devin.incerti
Author: Devin Incerti [aut, cre], Jeroen P. Jansen [aut], R Core Team [ctb] (hesim uses some slightly modified C functions from base R)
Maintainer: Devin Incerti <devin.incerti@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-17 19:00:05 UTC

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Package bimets updated to version 1.4.3 with previous version 1.4.2 dated 2019-10-19

Title: Time Series and Econometric Modeling
Description: Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, endogenous targeting and model renormalization.
Author: Andrea Luciani [aut, cre], Roberto Stok [aut], Bank of Italy [cph]
Maintainer: Andrea Luciani <andrea.luciani@bancaditalia.it>

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Package metan updated to version 1.4.0 with previous version 1.3.0 dated 2020-02-11

Title: Multi Environment Trials Analysis
Description: Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, joint Regression Analysis by Eberhart & Russel (1966) (<doi:10.2135/cropsci1966.0011183X000600010011x>), ecovalence by Wricke (1965), genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method <doi:10.12702/1984-7033.v04n01a02>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute scores by Olivoto et al. (2019a) <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>. Non-parametric methods includes superiority index by Lin & Binns (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic stability by Huehn (1990) <https://link.springer.com/article/10.1007/BF00024241>, TOP third statistic by Fox et al. (1990) <doi:10.1007/BF00040364>, geometric adaptability index described by Shahbazi (2019) <doi:10.1016/j.scienta.2019.04.047>. Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.
Author: Tiago Olivoto [aut, cre, cph] (<https://orcid.org/0000-0002-0241-9636>)
Maintainer: Tiago Olivoto <tiagoolivoto@gmail.com>

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Package febr updated to version 1.1.0 with previous version 1.0.3 dated 2020-01-25

Title: Free Brazilian Repository for Open Soil Data
Description: Making the access to the Free Brazilian Repository for Open Soil Data <http://www.ufsm.br/febr/> as easy as possible.
Author: Alessandro Samuel-Rosa [aut, cre] (<https://orcid.org/0000-0003-0877-1320>), Universidade Federal de Santa Maria [fnd], Universidade Tecnológica Federal do Paraná [fnd]
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Package SqlRender updated to version 1.6.4 with previous version 1.6.3 dated 2020-01-13

Title: Rendering Parameterized SQL and Translation to Dialects
Description: A rendering tool for parameterized SQL that also translates into different SQL dialects. These dialects include 'Microsoft Sql Server', 'Oracle', 'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', and 'SQLite'.
Author: Martijn Schuemie [aut, cre], Marc Suchard [aut]
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Package modifiedmk updated to version 1.5.0 with previous version 1.4.0 dated 2019-06-14

Title: Modified Versions of Mann Kendall and Spearman's Rho Trend Tests
Description: Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size. Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>. Kendall, M. (1975). Multivariate analysis. Charles Griffin&Company Ltd,. sen, P. K. (1968).<doi:10.2307/2285891>. Önöz, B., & Bayazit, M. (2012) <doi:10.1002/hyp.8438>. Hamed, K. H. (2009).<doi:10.1016/j.jhydrol.2009.01.040>. Yue, S., & Wang, C. Y. (2002) <doi:10.1029/2001WR000861>. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) <doi:10.1002/hyp.1095>. Hamed, K. H., & Ramachandra Rao, A. (1998) <doi:10.1016/S0022-1694(97)00125-X>. Yue, S., & Wang, C. Y. (2004) <doi:10.1023/B:WARM.0000043140.61082.60>.
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Package embed updated to version 0.0.6 with previous version 0.0.5 dated 2020-01-07

Title: Extra Recipes for Encoding Categorical Predictors
Description: Predictors can be converted to one or more numeric representations using simple generalized linear models <arXiv:1611.09477> or nonlinear models <arXiv:1604.06737>. All encoding methods are supervised.
Author: Max Kuhn [aut, cre], RStudio [cph]
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Package workflows updated to version 0.1.1 with previous version 0.1.0 dated 2019-12-30

Title: Modeling Workflows
Description: Managing both a 'parsnip' model and a preprocessor, such as a model formula or recipe from 'recipes', can often be challenging. The goal of 'workflows' is to streamline this process by bundling the model alongside the preprocessor, all within the same object.
Author: Davis Vaughan [aut, cre], RStudio [cph]
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Package RAthena updated to version 1.8.0 with previous version 1.7.1 dated 2020-02-16

Title: Connect to 'AWS Athena' using 'Boto3' ('DBI' Interface)
Description: Designed to be compatible with the R package 'DBI' (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this 'Python' 'Boto3' Software Development Kit ('SDK') <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html> is used as a driver.
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Package noctua updated to version 1.6.0 with previous version 1.5.1 dated 2020-02-16

Title: Connect to 'AWS Athena' using R 'AWS SDK' 'paws' ('DBI' Interface)
Description: Designed to be compatible with the 'R' package 'DBI' (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this the 'R' 'AWS' Software Development Kit ('SDK') 'paws' <https://github.com/paws-r/paws> is used as a driver.
Author: Dyfan Jones [aut, cre]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

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Package NNS updated to version 0.5.0 with previous version 0.4.9 dated 2020-02-13

Title: Nonlinear Nonparametric Statistics
Description: Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).
Author: Fred Viole
Maintainer: Fred Viole <ovvo.financial.systems@gmail.com>

Diff between NNS versions 0.4.9 dated 2020-02-13 and 0.5.0 dated 2020-03-17

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More information about NNS at CRAN
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Package languageserversetup updated to version 0.1.1 with previous version 0.1.0 dated 2020-03-14

Title: Automated Setup and Auto Run for R Language Server
Description: Allows to install the R 'languageserver' with all dependencies into a separate library and use that independent installation automatically when R is instantiated as a language server process. Useful for making language server seamless to use without running into package version conflicts.
Author: Jozef Hajnala [aut, cre]
Maintainer: Jozef Hajnala <jozef.hajnala@gmail.com>

Diff between languageserversetup versions 0.1.0 dated 2020-03-14 and 0.1.1 dated 2020-03-17

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Package comat updated to version 0.8.2 with previous version 0.7.2 dated 2019-11-12

Title: Creates Co-Occurrence Matrices of Spatial Data
Description: Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).
Author: Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>), Maximillian H.K. Hesselbarth [ctb] (Co-author of underlying C++ code for get_class_index_map(), get_unique_values(), and rcpp_get_coma() functions), Marco Sciaini [ctb] (Co-author of underlying C++ code for get_class_index_map(), get_unique_values(), and rcpp_get_coma() functions), Sebastian Hanss [ctb] (Co-author of underlying C++ code for get_class_index_map(), get_unique_values(), and rcpp_get_coma() functions)
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>

Diff between comat versions 0.7.2 dated 2019-11-12 and 0.8.2 dated 2020-03-17

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Package BayesianFROC updated to version 0.2.2 with previous version 0.2.1 dated 2020-01-19

Title: FROC Analysis by Bayesian Approaches
Description: Execute BayesianFROC::fit_GUI_Shiny() (or fit_GUI_Shiny_MRMC()) for a graphical user interface via Shiny. The free-response receiver operating characteristic (FROC) method is a generalization of receiver operating characteristic (ROC) analysis. However, Chakraborty's classical model is non-generative in the sense that it cannot synthesize data. This package aims to modify his models to be generative using a Bayesian approach, and to verify that our models fit practical datasets. We also develop a Bayesian model for comparing modalities. Chakraborty [1] defined a free-response receiver operating characteristic (FROC) model based on maximum likelihood (ML). However, his model is non-generative in the sense that it cannot generate FROC datasets. In signal detection theory, the number of true positives never exceeds the number of targets. However, this is not explained by any existing model. Thus, in this package, the author contributes to FROC theory by refining Chakraborty’s model to obtain models that are generative. This modification allows us to use FROC analysis in a general statistical scheme, and as a benefit, our generative model can be applied to calculations of posterior predictive p values that require generation of synthetic datasets from fitted models. Furthermore, this package presents new models for comparison of modalities. Modality comparison is a common problem in radiology, and has been studied extensively. However, in many medical studies, such problems are addressed with non-Bayesian methods such as ANOVA. As a supplementary topic, this work presents a Bayesian model that includes individual differences. With this model, we can account for differences between individual readers when comparing modalities, using Bayesian rather than ML-methods. The author found the existing FROC model in [1] to be non-generative for calculation of posterior predictive p values. Replacing the ML-based method with a Bayesian approach differs from standard practice but provides insight into the problems of existing methods. Please execute the following R scripts from the R (R studio) console, demo(demo_MRMC, package = "BayesianFROC"); demo(demo_srsc, package = "BayesianFROC"); demo(demo_stan, package = "BayesianFROC"); demo(demo_drawcurves_srsc, package = "BayesianFROC"); demo_Bayesian_FROC(); demo_Bayesian_FROC_without_pause(). References: [1] Dev Chakraborty (1989) <doi:10.1118/1.596358> Maximum likelihood analysis of free - response receiver operating characteristic (FROC) data. Pre-print: Issei Tsunoda; Generative Models for free-response receiver operating characteristic analysis. See the vignettes for more details.
Author: Issei Tsunoda [aut, cre]
Maintainer: Issei Tsunoda <tsunoda.issei1111@gmail.com>

Diff between BayesianFROC versions 0.2.1 dated 2020-01-19 and 0.2.2 dated 2020-03-17

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 BayesianFROC-0.2.2/BayesianFROC/R/StartupMessage.R                                                              |   34 
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 BayesianFROC-0.2.2/BayesianFROC/R/demo_Bayesian_FROC.R                                                          |   14 
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 BayesianFROC-0.2.2/BayesianFROC/R/ppp.R                                                                         |   38 
 BayesianFROC-0.2.2/BayesianFROC/R/sbc.R                                                                         |    6 
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 BayesianFROC-0.2.2/BayesianFROC/R/sortAUC.R                                                                     |    8 
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 BayesianFROC-0.2.2/BayesianFROC/R/viewdata.R                                                                    |    4 
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 BayesianFROC-0.2.2/BayesianFROC/man/StatisticForANOVA.Rd                                                        |   28 
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 BayesianFROC-0.2.2/BayesianFROC/man/clearWorkspace.Rd                                                           |   28 
 BayesianFROC-0.2.2/BayesianFROC/man/compare.Rd                                                                  |   31 
 BayesianFROC-0.2.2/BayesianFROC/man/comparison.Rd                                                               |   60 
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 BayesianFROC-0.2.2/BayesianFROC/man/create_dataList_MRMC.Rd                                                     |  694 +-
 BayesianFROC-0.2.2/BayesianFROC/man/create_dataset.Rd                                                           |   72 
 BayesianFROC-0.2.2/BayesianFROC/man/d.Rd                                                                        |   44 
 BayesianFROC-0.2.2/BayesianFROC/man/dark_theme.Rd                                                               |  110 
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 BayesianFROC-0.2.2/BayesianFROC/man/data.bad.fit.Rd                                                             |  198 
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 BayesianFROC-0.2.2/BayesianFROC/man/dataList.Chakra.4.Rd                                                        |  196 
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 BayesianFROC-0.2.2/BayesianFROC/man/dataList.High.Rd                                                            |   42 
 BayesianFROC-0.2.2/BayesianFROC/man/dataList.Low.Rd                                                             |   42 
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 BayesianFROC-0.2.2/BayesianFROC/man/dddddd.Rd                                                                   |  302 -
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 BayesianFROC-0.2.2/BayesianFROC/man/extractAUC.Rd                                                               |   65 
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 BayesianFROC-0.2.2/BayesianFROC/man/fit_GUI.Rd                                                                  |   70 
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 BayesianFROC-0.2.2/BayesianFROC/man/fit_GUI_MRMC_new.Rd                                                         |   46 
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 BayesianFROC-0.2.2/BayesianFROC/man/foo.Rd                                                                      |   26 
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 BayesianFROC-0.2.2/BayesianFROC/man/install_imports.Rd                                                          |   22 
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 BayesianFROC-0.2.2/BayesianFROC/man/make_TeX.Rd                                                                 |   28 
 BayesianFROC-0.2.2/BayesianFROC/man/make_true_parameter_MRMC.Rd                                                 |   32 
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 BayesianFROC-0.2.2/BayesianFROC/man/metadata_to_DrawCurve_MRMC.Rd                                               |   44 
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 BayesianFROC-0.2.2/BayesianFROC/man/mu.Rd                                                                       |   66 
 BayesianFROC-0.2.2/BayesianFROC/man/mu_truth.Rd                                                                 |   80 
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 BayesianFROC-0.2.2/BayesianFROC/man/p.Rd                                                                        |   28 
 BayesianFROC-0.2.2/BayesianFROC/man/p_truth.Rd                                                                  |   44 
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 BayesianFROC-0.2.2/BayesianFROC/man/plot_test.Rd                                                                |   26 
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 BayesianFROC-0.2.2/BayesianFROC/man/ppp_MRMC.Rd                                                                 |  171 
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 BayesianFROC-0.2.2/BayesianFROC/man/showGM.Rd                                                                   |   38 
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 BayesianFROC-0.2.2/BayesianFROC/man/size_of_return_value.Rd                                                     |   57 
 BayesianFROC-0.2.2/BayesianFROC/man/small_margin.Rd                                                             |  157 
 BayesianFROC-0.2.2/BayesianFROC/man/snippet_for_BayesianFROC.Rd                                                 |   38 
 BayesianFROC-0.2.2/BayesianFROC/man/sortAUC.Rd                                                                  |  126 
 BayesianFROC-0.2.2/BayesianFROC/man/stanfitExtended.Rd                                                          |  264 
 BayesianFROC-0.2.2/BayesianFROC/man/summarize_MRMC.Rd                                                           |   70 
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 BayesianFROC-0.2.2/BayesianFROC/man/summary_EAP_CI_srsc.Rd                                                      |  120 
 BayesianFROC-0.2.2/BayesianFROC/man/the_row_number_of_logical_vector.Rd                                         |   94 
 BayesianFROC-0.2.2/BayesianFROC/man/v.Rd                                                                        |   28 
 BayesianFROC-0.2.2/BayesianFROC/man/v_truth.Rd                                                                  |   44 
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 BayesianFROC-0.2.2/BayesianFROC/man/validation.dataset_srsc.Rd                                                  |  587 +-
 BayesianFROC-0.2.2/BayesianFROC/man/validation.draw_srsc.Rd                                                     |  120 
 BayesianFROC-0.2.2/BayesianFROC/man/viewdata.Rd                                                                 |  280 
 BayesianFROC-0.2.2/BayesianFROC/man/viewdata_MRMC.Rd                                                            |   88 
 BayesianFROC-0.2.2/BayesianFROC/man/viewdata_srsc.Rd                                                            |  116 
 BayesianFROC-0.2.2/BayesianFROC/man/waic.Rd                                                                     |  190 
 BayesianFROC-0.2.2/BayesianFROC/man/z.Rd                                                                        |   28 
 BayesianFROC-0.2.2/BayesianFROC/man/z_from_dz.Rd                                                                |  108 
 BayesianFROC-0.2.2/BayesianFROC/man/z_truth.Rd                                                                  |   44 
 BayesianFROC-0.2.2/BayesianFROC/vignettes/mystyle.css                                                           |only
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More information about BayesianFROC at CRAN
Permanent link

Package AlphaPart updated to version 0.8.0 with previous version 0.7.0 dated 2019-06-27

Title: Partition/Decomposition of Breeding Values by Paths of Information
Description: A software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) <doi:10.1017/S175173110800205X>. The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results.
Author: Gregor Gorjanc, Jana Obsteter
Maintainer: Gregor Gorjanc <gregor.gorjanc@roslin.ed.ac.uk>

Diff between AlphaPart versions 0.7.0 dated 2019-06-27 and 0.8.0 dated 2020-03-17

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 AlphaPart-0.8.0/AlphaPart/MD5                                                    |   86 ++--
 AlphaPart-0.8.0/AlphaPart/R/AlphaPart.R                                          |   34 +
 AlphaPart-0.8.0/AlphaPart/R/AlphaPartSum.R                                       |   16 
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 AlphaPart-0.8.0/AlphaPart/R/print.AlphaPart.R                                    |    4 
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More information about AlphaPart at CRAN
Permanent link

Package REddyProc updated to version 1.2.1 with previous version 1.2 dated 2019-03-30

Title: Post Processing of (Half-)Hourly Eddy-Covariance Measurements
Description: Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).
Author: Department for Biogeochemical Integration at MPI-BGC, Jena, Germany [cph], Thomas Wutzler [aut, cre], Markus Reichstein [aut], Antje Maria Moffat [aut, trl], Olaf Menzer [ctb], Mirco Migliavacca [aut], Kerstin Sickel [ctb, trl], Ladislav Šigut [ctb]
Maintainer: Thomas Wutzler <twutz@bgc-jena.mpg.de>

Diff between REddyProc versions 1.2 dated 2019-03-30 and 1.2.1 dated 2020-03-17

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 NEWS.md                                                        |    5 
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More information about REddyProc at CRAN
Permanent link

Package afpt updated to version 1.1.0.0 with previous version 1.0.0 dated 2017-09-01

Title: Tools for Modelling of Animal Flight Performance
Description: Allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model described in Klein Heerenbrink et al. (2015) <doi:10.1098/rspa.2014.0952>. Taking inspiration from the program 'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. 'afpt' can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions.
Author: Marco KleinHeerenbrink [aut, cre], Anders Hedenström [fnd]
Maintainer: Marco KleinHeerenbrink <Marco.KleinHeerenbrink@zoo.ox.ac.uk>

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Package xpose updated to version 0.4.8 with previous version 0.4.7 dated 2020-02-04

Title: Diagnostics for Pharmacometric Models
Description: Diagnostics for non-linear mixed-effects (population) models from 'NONMEM' <http://www.iconplc.com/innovation/nonmem/>. 'xpose' facilitates data import, creation of numerical run summary and provide 'ggplot2'-based graphics for data exploration and model diagnostics.
Author: Benjamin Guiastrennec [aut, cre, cph], Andrew C. Hooker [aut, cph], Sebastian Ueckert [aut, cph], Mike K. Smith [ctb], Mats O. Karlsson [aut, cph]
Maintainer: Benjamin Guiastrennec <guiastrennec@gmail.com>

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Package RKorAPClient updated to version 0.5.7 with previous version 0.5.6 dated 2020-03-13

Title: 'KorAP' Web Service Client Package
Description: A client package that makes the 'KorAP' web service API accessible from R. The corpus analysis platform 'KorAP' has been developed as a scientific tool to make potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo' or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify hypotheses and to find interesting patterns in real language use. The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code, as a programmatic alternative to the 'KorAP' web user-interface. You can learn more about 'KorAP' and use it directly on 'DeReKo' at <https://korap.ids-mannheim.de/>.
Author: Marc Kupietz [aut, cre], Nils Diewald [ctb], Leibniz Institute for the German Language [cph, fnd]
Maintainer: Marc Kupietz <kupietz@ids-mannheim.de>

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Package pwr updated to version 1.3-0 with previous version 1.2-2 dated 2018-03-03

Title: Basic Functions for Power Analysis
Description: Power analysis functions along the lines of Cohen (1988).
Author: Stephane Champely [aut], Claus Ekstrom [ctb], Peter Dalgaard [ctb], Jeffrey Gill [ctb], Stephan Weibelzahl [ctb], Aditya Anandkumar [ctb], Clay Ford [ctb], Robert Volcic [ctb], Helios De Rosario [cre]
Maintainer: Helios De Rosario <helios.derosario@gmail.com>

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Package prim updated to version 1.0.17 with previous version 1.0.16 dated 2015-09-08

Title: Patient Rule Induction Method (PRIM)
Description: Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.
Author: Tarn Duong <tarn.duong@gmail.com>
Maintainer: Tarn Duong <tarn.duong@gmail.com>

Diff between prim versions 1.0.16 dated 2015-09-08 and 1.0.17 dated 2020-03-17

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Package DoE.base updated to version 1.1-4 with previous version 1.1-3 dated 2019-05-03

Title: Full Factorials, Orthogonal Arrays and Base Utilities for DoE Packages
Description: Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.
Author: Ulrike Groemping [aut, cre], Boyko Amarov [ctb], Hongquan Xu [ctb]
Maintainer: Ulrike Groemping <groemping@beuth-hochschule.de>

Diff between DoE.base versions 1.1-3 dated 2019-05-03 and 1.1-4 dated 2020-03-17

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Package tabr updated to version 0.4.2 with previous version 0.4.1 dated 2020-02-09

Title: Music Notation Syntax, Manipulation, Analysis and Transcription in R
Description: Provides a music notation syntax and a collection of music programming functions for generating, manipulating, organizing and analyzing musical information in R. The music notation framework facilitates creating and analyzing music data in notation form. Music data can be viewed, manipulated and analyzed while in different forms of representation based around different data structures: strings and data frames. Each representation offers advantages over the other for different use cases. Music syntax can be entered directly and represented in character strings to minimize the formatting overhead of data entry by using simple data structures, for example when wanting to quickly enter and transcribe short pieces of music to sheet music or tablature. The package contains functions for directly performing various mathematical, logical and organizational operations and musical transformations on special object classes that facilitate working with music data and notation. The same music data can also be organized in tidy data frames, allowing for a more familiar and powerful approach to the analysis of large amounts of structured music data. Functions are available for mapping seamlessly between these data structures and their representations of musical information. The package also provides API wrapper functions for transcribing musical representations in R into guitar tablature ("tabs") and basic sheet music using the 'LilyPond' backend (<http://lilypond.org>). 'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. The package generates 'LilyPond' files from R code and can pass them to 'LilyPond' to be rendered into sheet music pdf files. The package offers nominal MIDI file output support in conjunction with rendering sheet music. The package can read MIDI files and attempts to structure the MIDI data to integrate as best as possible with the data structures and functionality found throughout the package.
Author: Matthew Leonawicz [aut, cre] (<https://orcid.org/0000-0001-9452-2771>)
Maintainer: Matthew Leonawicz <matt_leonawicz@esource.com>

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Package Numero updated to version 1.4.2 with previous version 1.4.1 dated 2020-02-05

Title: Statistical Framework to Define Subgroups in Complex Datasets
Description: High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Author: Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], Ville-Petteri Makinen [aut, cre]
Maintainer: Ville-Petteri Makinen <vpmakine@gmail.com>

Diff between Numero versions 1.4.1 dated 2020-02-05 and 1.4.2 dated 2020-03-17

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Package MOCCA updated to version 1.4 with previous version 1.3 dated 2018-06-08

Title: Multi-Objective Optimization for Collecting Cluster Alternatives
Description: Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.
Author: Johann Kraus <johann.kraus@uni-ulm.de>
Maintainer: Hans Kestler <hans.kestler@uni-ulm.de>

Diff between MOCCA versions 1.3 dated 2018-06-08 and 1.4 dated 2020-03-17

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Package GlmSimulatoR updated to version 0.2.1 with previous version 0.2 dated 2019-11-28

Title: Creates Ideal Data for Generalized Linear Models
Description: Have you ever struggled to find "good data" for a generalized linear model? Would you like to test how quickly statistics converge to parameters, or learn how picking different link functions affects model performance? This package creates ideal data for both common and novel generalized linear models so your questions can be empirically answered.
Author: Greg McMahan
Maintainer: Greg McMahan <gmcmacran@gmail.com>

Diff between GlmSimulatoR versions 0.2 dated 2019-11-28 and 0.2.1 dated 2020-03-17

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Package Rcpp updated to version 1.0.4 with previous version 1.0.3 dated 2019-11-08

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

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Package ModelMetrics updated to version 1.2.2.2 with previous version 1.2.2.1 dated 2020-01-13

Title: Rapid Calculation of Model Metrics
Description: Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.
Author: Tyler Hunt [aut, cre]
Maintainer: Tyler Hunt <thunt@snapfinance.com>

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Package clv updated to version 0.3-2.2 with previous version 0.3-2.1 dated 2013-11-11

Title: Cluster Validation Techniques
Description: Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs produced by functions coming from package "cluster". Package contains also functions and examples of usage for cluster stability approach that might be applied to algorithms implemented in "cluster" package as well as user defined clustering algorithms.
Author: Lukasz Nieweglowski <wookashn@gmail.com>
Maintainer: Lukasz Nieweglowski <wookashn@gmail.com>

Diff between clv versions 0.3-2.1 dated 2013-11-11 and 0.3-2.2 dated 2020-03-17

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