Thu, 15 Jul 2021

Package workflows updated to version 0.2.3 with previous version 0.2.2 dated 2021-03-10

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]
Maintainer: Davis Vaughan <davis@rstudio.com>

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Package simpleRCache updated to version 0.3.3 with previous version 0.3.2 dated 2017-04-09

Title: Simple R Cache
Description: Simple result caching in R based on R.cache. The global environment is not considered when caching results simplifying moving files between multiple instances of R. Relies on more base functions than R.cache (e.g. cached results are saved using saveRDS() and readRDS()).
Author: Augustin Luna, Vinodh Rajapakse
Maintainer: Augustin Luna <lunaa@cbio.mskcc.org>

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Package provExplainR updated to version 1.1 with previous version 1.0 dated 2020-10-17

Title: Compare Provenance Collections to Explain Changed Script Outputs
Description: Inspects provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output created by the execution of a script, and find differences between two provenance collections. Factors under examination included the hardware and software used to execute the script, versions of attached libraries, use of global variables, modified inputs and outputs, and changes in main and sourced scripts. Based on detected changes, 'provExplainR' can be used to study how these factors affect the behavior of the script and generate a promising diagnosis of the causes of different script results. More information about 'rdtLite' and associated tools is available at <https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, <doi:10.3390/informatics5010012>.
Author: Barbara Lerner [cre], Emery Boose [aut], Khanh Ngo [aut]
Maintainer: Barbara Lerner <blerner@mtholyoke.edu>

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Package metan updated to version 1.15.0 with previous version 1.14.0 dated 2021-06-07

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>, Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric adaptability index by Mohammadi & Amri (2008) <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>, 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>. 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 gmGeostats updated to version 0.10-8 with previous version 0.10-7 dated 2020-10-03

Title: Geostatistics for Compositional Analysis
Description: Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts data, compositional data, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. The methods mainly follow Tolosana-Delgado, Mueller and van den Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
Author: Raimon Tolosana-Delgado [aut] (<https://orcid.org/0000-0001-9847-0462>), Ute Mueller [aut], K. Gerald van den Boogaart [ctb, cre], Hassan Talebi [ctb, cph], Helmholtz-Zentrum Dresden-Rossendorf [cph], Edith Cowan University [cph]
Maintainer: K. Gerald van den Boogaart <support@boogaart.de>

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Package taxlist updated to version 0.2.2 with previous version 0.2.1 dated 2021-03-29

Title: Handling Taxonomic Lists
Description: Handling taxonomic lists through objects of class 'taxlist'. This package provides functions to import species lists from 'Turboveg' (<https://www.synbiosys.alterra.nl/turboveg/>) and the possibility to create backups from resulting R-objects. Also quick displays are implemented as summary-methods.
Author: Miguel Alvarez [aut, cre] (<https://orcid.org/0000-0003-1500-1834>), Zachary Foster [ctb] (<https://orcid.org/0000-0002-5075-0948>), Sam Levin [rev], Margaret Siple [rev]
Maintainer: Miguel Alvarez <kamapu78@gmail.com>

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Package MEDseq updated to version 1.3.0 with previous version 1.2.1 dated 2021-01-06

Title: Mixtures of Exponential-Distance Models with Covariates
Description: Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics. Sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>), Raffaella Piccarreta [ctb], Isobel Claire Gormley [ctb] (<https://orcid.org/0000-0001-7713-681X>)
Maintainer: Keefe Murphy <keefe.murphy@mu.ie>

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Package katex updated to version 1.1.0 with previous version 1.0.0 dated 2021-07-07

Title: Rendering Math to HTML, 'MathML', or R-Documentation Format
Description: Convert latex math expressions to HTML and 'MathML' for use in markdown documents or package manual pages. The rendering is done in R using the V8 engine (i.e. server-side), which eliminates the need for embedding the 'MathJax' library into your web pages. In addition a 'math-to-rd' wrapper is provided to automatically render beautiful math in R documentation files.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

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Package RJcluster updated to version 3.0.1 with previous version 3.0.0 dated 2021-07-07

Title: A Fast Clustering Algorithm for High Dimensional Data Based on the Gram Matrix Decomposition
Description: Clustering algorithm for high dimensional data. Assuming that P feature measurements on N objects are arranged in an N×P matrix X, this package provides clustering based on the left Gram matrix XX^T. To simulate test data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')". To cite this package, type 'citation("RJcluster")'.
Author: Shahina Rahman [aut], Valen E. Johnson [aut], Suhasini Subba Rao [aut], Rachael Shudde [aut, cre, trl]
Maintainer: Rachael Shudde <rachael.shudde@gmail.com>

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Package plinkQC updated to version 0.3.4 with previous version 0.3.3 dated 2021-02-08

Title: Genotype Quality Control with 'PLINK'
Description: Genotyping arrays enable the direct measurement of an individuals genotype at thousands of markers. 'plinkQC' facilitates genotype quality control for genetic association studies as described by Anderson and colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic statistics (e.g. missing genotyping rates per individual, allele frequencies per genetic marker) and relationship functions accessible from 'R' and generates a per-individual and per-marker quality control report. Individuals and markers that fail the quality control can subsequently be removed to generate a new, clean dataset. Removal of individuals based on relationship status is optimised to retain as many individuals as possible in the study.
Author: Hannah Meyer [aut, cre] (<https://orcid.org/0000-0003-4564-0899>)
Maintainer: Hannah Meyer <hannah.v.meyer@gmail.com>

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Package deepgp updated to version 0.2.1 with previous version 0.2.0 dated 2020-12-16

Title: Sequential Design for Deep Gaussian Processes using MCMC
Description: Performs model fitting and sequential design for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>. Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Sequential design criteria include integrated mean-squared error (IMSE), active learning Cohn (ALC), and expected improvement (EI). Covariance structure is based on inverse exponentiated squared euclidean distance. Applicable to noisy and deterministic functions. Incorporates SNOW parallelization and utilizes C under the hood.
Author: Annie Sauer <anniees@vt.edu>
Maintainer: Annie Sauer <anniees@vt.edu>

Diff between deepgp versions 0.2.0 dated 2020-12-16 and 0.2.1 dated 2021-07-15

 deepgp-0.2.0/deepgp/R/fit_models.R         |only
 deepgp-0.2.1/deepgp/DESCRIPTION            |   23 ++---
 deepgp-0.2.1/deepgp/MD5                    |   45 ++++++-----
 deepgp-0.2.1/deepgp/R/continue.R           |only
 deepgp-0.2.1/deepgp/R/deepgp-package.R     |   31 +++----
 deepgp-0.2.1/deepgp/R/fit.R                |only
 deepgp-0.2.1/deepgp/R/general.R            |   41 +++++-----
 deepgp-0.2.1/deepgp/R/mcmc.R               |only
 deepgp-0.2.1/deepgp/R/plot.R               |only
 deepgp-0.2.1/deepgp/R/predict.R            |only
 deepgp-0.2.1/deepgp/R/seq_design.R         |  117 ++++++++++++++++-------------
 deepgp-0.2.1/deepgp/R/trim.R               |only
 deepgp-0.2.1/deepgp/README.md              |   12 ++
 deepgp-0.2.1/deepgp/man/ALC.Rd             |   39 +++++----
 deepgp-0.2.1/deepgp/man/EI.Rd              |   16 ++-
 deepgp-0.2.1/deepgp/man/IMSE.Rd            |   24 +++--
 deepgp-0.2.1/deepgp/man/continue.Rd        |    2 
 deepgp-0.2.1/deepgp/man/deepgp-package.Rd  |   31 +++----
 deepgp-0.2.1/deepgp/man/fit_one_layer.Rd   |   36 ++++----
 deepgp-0.2.1/deepgp/man/fit_three_layer.Rd |   86 +++++++++++----------
 deepgp-0.2.1/deepgp/man/fit_two_layer.Rd   |   53 +++++++------
 deepgp-0.2.1/deepgp/man/plot.Rd            |    9 +-
 deepgp-0.2.1/deepgp/man/predict.Rd         |   78 ++++++++++---------
 deepgp-0.2.1/deepgp/man/rmse.Rd            |    7 -
 deepgp-0.2.1/deepgp/man/score.Rd           |   10 +-
 deepgp-0.2.1/deepgp/man/sq_dist.Rd         |   11 +-
 deepgp-0.2.1/deepgp/man/trim.Rd            |   10 +-
 27 files changed, 375 insertions(+), 306 deletions(-)

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Package microsimulation updated to version 1.3.5 with previous version 1.3.3 dated 2021-03-20

Title: Discrete Event Simulation in R and C++, with Tools for Cost-Effectiveness Analysis
Description: Discrete event simulation using both R and C++ (Karlsson et al 2016; <doi:10.1109/eScience.2016.7870915>). The C++ code is adapted from the SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for both event-oriented and process-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis.
Author: Mark Clements [aut, cre, cph], Alexandra Jauhiainen [aut], Andreas Karlsson [aut], Antonio Carzaniga [cph], University of Colorado [cph], Pierre L'Ecuyer [cph]
Maintainer: Mark Clements <mark.clements@ki.se>

Diff between microsimulation versions 1.3.3 dated 2021-03-20 and 1.3.5 dated 2021-07-15

 DESCRIPTION                     |   10 +++++---
 MD5                             |   29 ++++++++++++++-----------
 R/rcpp_hello_world.R            |   46 +++++++++++++++++++++-------------------
 configure.ac                    |   14 ++++++++++++
 inst/doc/index.html             |only
 inst/include/RngStream.cpp      |only
 inst/include/microsimulation.cc |only
 inst/include/microsimulation.h  |    6 ++---
 inst/include/siena/tprocess.h   |    2 -
 inst/include/ssim.cc            |only
 inst/include/tprocess.cc        |only
 man/Classes.Rd                  |   28 ++++++++++++------------
 man/Internal.Rd                 |   15 +++++++++++++
 man/Utilities.Rd                |   24 ++++++++++++++++++++
 man/discountedInterval.Rd       |    3 ++
 man/microsimulation-package.Rd  |    6 +----
 src/Makevars                    |    2 -
 src/Makevars.win                |    2 -
 18 files changed, 126 insertions(+), 61 deletions(-)

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Package garchx updated to version 1.3 with previous version 1.2 dated 2021-01-23

Title: Flexible and Robust GARCH-X Modelling
Description: Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2018) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time.
Author: Genaro Sucarrat [aut, cre]
Maintainer: Genaro Sucarrat <gsucarrat@gmail.com>

Diff between garchx versions 1.2 dated 2021-01-23 and 1.3 dated 2021-07-15

 DESCRIPTION            |    8 +-
 MD5                    |   18 +++--
 NAMESPACE              |    5 -
 NEWS                   |   21 ++++++
 R/garchx-internal.R    |    2 
 R/garchx-source-code.R |  161 ++++++++++++++++++++++++++++++++++++++++++-------
 man/coef.garchx.Rd     |    4 -
 man/garchx-package.Rd  |    4 -
 man/garchx.Rd          |   14 ++--
 man/gdiff.Rd           |only
 man/glag.Rd            |only
 11 files changed, 191 insertions(+), 46 deletions(-)

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New package shinyToastify with initial version 1.0.0
Package: shinyToastify
Title: Pretty Notifications for 'Shiny'
Version: 1.0.0
Authors@R: c( person(given = "Stéphane", family = "Laurent", role = c("aut", "cre"), email = "laurent_step@outlook.fr"), person(given = "Fadi", family = "Khadra", role = "cph", comment = "'React-Toastify' library (https://fkhadra.github.io/react-toastify/introduction)"))
Description: This is a wrapper of the 'React' library 'React-Toastify'. It allows to show some notifications (toasts) in 'Shiny' applications. There are options for the style, the position, the transition effect, and more.
License: GPL-3
Encoding: UTF-8
URL: https://github.com/stla/shinyToastify
BugReports: https://github.com/stla/shinyToastify/issues
Imports: htmltools, reactR, shiny, utils
NeedsCompilation: no
Packaged: 2021-07-15 10:16:11 UTC; SDL96354
Author: Stéphane Laurent [aut, cre], Fadi Khadra [cph] ('React-Toastify' library (https://fkhadra.github.io/react-toastify/introduction))
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>
Repository: CRAN
Date/Publication: 2021-07-15 11:40:02 UTC

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New package recometrics with initial version 0.1.2
Package: recometrics
Title: Evaluation Metrics for Implicit-Feedback Recommender Systems
Version: 0.1.2
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/recometrics
BugReports: https://github.com/david-cortes/recometrics
Description: Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation.
LinkingTo: Rcpp, float
Imports: Rcpp (>= 1.0.1), Matrix (>= 1.3-4), MatrixExtra, float, RhpcBLASctl, methods
Suggests: recommenderlab (>= 0.2-7), cmfrec, data.table, knitr, rmarkdown, kableExtra, testthat
VignetteBuilder: knitr
License: BSD_2_clause + file LICENSE
StagedInstall: TRUE
Biarch: TRUE
NeedsCompilation: yes
Packaged: 2021-07-12 18:22:36 UTC; david
Repository: CRAN
Date/Publication: 2021-07-15 11:10:02 UTC

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New package miRetrieve with initial version 1.3.0
Package: miRetrieve
Title: miRNA Text Mining in Abstracts
Version: 1.3.0
Authors@R: c(person(given = "Julian", family = "Friedrich", role = c("aut", "cre"), email = "julian.friedrich@medma.uni-heidelberg.de"), person(given = "Hans-Peter", family = "Hammes", role = "aut"), person(given = "Guido", family = "Krenning", role = "aut"))
Author: Julian Friedrich [aut, cre], Hans-Peter Hammes [aut], Guido Krenning [aut]
Maintainer: Julian Friedrich <julian.friedrich@medma.uni-heidelberg.de>
Description: Providing tools for microRNA (miRNA) text mining. miRetrieve summarizes miRNA literature by extracting, counting, and analyzing miRNA names, thus aiming at gaining biological insights into a large amount of text within a short period of time. To do so, miRetrieve uses regular expressions to extract miRNAs and tokenization to identify meaningful miRNA associations. In addition, miRetrieve uses the latest miRTarBase version 8.0 (Hsi-Yuan Huang et al. (2020) "miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database" <doi:10.1093/nar/gkz896>) to display field-specific miRNA-mRNA interactions. The most important functions are available as a Shiny web application under <https://miretrieve.shinyapps.io/miRetrieve/>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1.0)
Imports: dplyr (>= 1.0.7), forcats (>= 0.5.1), ggplot2 (>= 3.3.5), magrittr (>= 2.0.1), openxlsx (>= 4.2.4), plotly (>= 4.9.4.1), purrr (>= 0.3.4), readr (>= 1.4.0), readxl (>= 1.3.1), rlang (>= 0.4.11), scales (>= 1.1.1), stringr (>= 1.4.0), tidyr (>= 1.1.3), tidytext (>= 0.3.1), topicmodels (>= 0.2.12), wordcloud (>= 2.6), xml2 (>= 1.3.2), zoo (>= 1.8-9)
Suggests: kableExtra, knitr, reshape2, rmarkdown, testthat
NeedsCompilation: no
Packaged: 2021-07-14 20:31:13 UTC; Julian
Repository: CRAN
Date/Publication: 2021-07-15 11:30:02 UTC

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New package ICDS with initial version 0.1.2
Package: ICDS
Title: Identification of Cancer Dysfunctional Subpathway with Omics Data
Version: 0.1.2
Authors@R: c( person("Junwei", "Han", email = "hanjunwei1981@163.com", role = "cre"), person("Baotong", "Zheng", email = "btzheng1116@163.com", role = "aut"), person("Siyao", "Liu", email = "liusiyao29@163.com", role = "ctb"))
Maintainer: Junwei Han <hanjunwei1981@163.com>
Description: Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.
Depends: R (>= 2.10)
biocViews:
Imports: igraph, graphite, metap, methods, org.Hs.eg.db
Suggests: knitr, rmarkdown, prettydoc
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-07-15 02:48:16 UTC; mac
Author: Junwei Han [cre], Baotong Zheng [aut], Siyao Liu [ctb]
Repository: CRAN
Date/Publication: 2021-07-15 11:30:10 UTC

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New package hydroEvents with initial version 0.10
Package: hydroEvents
Title: Extract Event Statistics in Hydrologic Time Series
Version: 0.10
Author: Conrad Wasko [aut, cre], Danlu Guo [aut]
Maintainer: Conrad Wasko <conrad.wasko@gmail.com>
Description: Events from individual hydrologic time series are extracted, and events from multiple time series can be matched to each other. The key references for approach are: Tang & Carey (2017) <doi:10.1002/hyp.11185>, Kaur et al. (2017) <doi:10.1080/24705357.2016.1276418> and Ladson et al. (2013) <https://www.tandfonline.com/doi/abs/10.7158/13241583.2013.11465417>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2021-07-15 03:41:10 UTC; danlug
Repository: CRAN
Date/Publication: 2021-07-15 11:30:08 UTC

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Package visvow updated to version 1.3.3 with previous version 1.3.2 dated 2021-06-24

Title: Visible Vowels: Visualization of Vowel Variation
Description: Visualizes vowel variation in f0, F1, F2, F3 and duration.
Author: Wilbert Heeringa, Hans Van de Velde
Maintainer: Wilbert Heeringa <wjheeringa@gmail.com>

Diff between visvow versions 1.3.2 dated 2021-06-24 and 1.3.3 dated 2021-07-15

 DESCRIPTION                      |    6 ++--
 MD5                              |    8 +++---
 R/visvow.R                       |    4 +--
 build/partial.rdb                |binary
 tests/testthat/data/SussmanF.csv |   52 +++++++++++++++++++--------------------
 5 files changed, 35 insertions(+), 35 deletions(-)

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Package RobKF updated to version 1.0.2 with previous version 1.0.1 dated 2021-02-10

Title: Innovative and/or Additive Outlier Robust Kalman Filtering
Description: Implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.
Author: Alex TM Fisch [aut], Daniel Grose [aut, cre], Idris A Eckley [aut, ths], Paul Fearnhead [aut, ths], Lawrence Bardwell [aut, ctb]
Maintainer: Daniel Grose <dan.grose@lancaster.ac.uk>

Diff between RobKF versions 1.0.1 dated 2021-02-10 and 1.0.2 dated 2021-07-15

 DESCRIPTION         |   26 +++++++++++++-------------
 MD5                 |    6 +++---
 build/partial.rdb   |binary
 src/RcppExports.cpp |    5 +++++
 4 files changed, 21 insertions(+), 16 deletions(-)

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New package isoWater with initial version 1.0.0
Package: isoWater
Title: Discovery, Retrieval, and Analysis of Water Isotope Data
Version: 1.0.0
Authors@R: person("Gabe", "Bowen", email = "gabe.bowen@utah.edu", role = c("aut", "cre"))
Maintainer: Gabe Bowen <gabe.bowen@utah.edu>
Description: The wiDB...() functions provide an interface to the public API of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: build, check and submit queries, and receive and unpack responses. Data analysis functions support Bayesian inference of the source and source isotope composition of water samples that may have experienced evaporation. Algorithms adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).
Imports: R2jags, abind, R2WinBUGS, doParallel, foreach, httr, jsonlite
Depends: R (>= 3.5)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), covr
VignetteBuilder: knitr
License: GPL-3
Language: en-US
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-07-14 14:34:53 UTC; u0133977
Author: Gabe Bowen [aut, cre]
Repository: CRAN
Date/Publication: 2021-07-15 08:20:02 UTC

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New package discord with initial version 1.1.0
Package: discord
Title: Functions for Discordant Kinship Modeling
Version: 1.1.0
Date: 2021-07-14
Authors@R: c(person("S. Mason", "Garrison", email = "garrissm@wfu.edu", role = c("aut", "cre", "cph")), person("Jonathan", "Trattner", email = "jdt@jdtrat.com", role = "aut"), person("Cermet", "Ream", role = c("ctb")))
Description: Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Currently, the package contains data restructuring functions and functions for generating biometrically informed data for kin pairs.
URL: https://github.com/R-Computing-Lab/discord
License: GPL-3
LazyData: TRUE
Encoding: UTF-8
Depends: R (>= 2.10)
Imports: stats
Suggests: testthat
NeedsCompilation: no
Packaged: 2021-07-14 16:34:35 UTC; jt
Author: S. Mason Garrison [aut, cre, cph], Jonathan Trattner [aut], Cermet Ream [ctb]
Maintainer: S. Mason Garrison <garrissm@wfu.edu>
Repository: CRAN
Date/Publication: 2021-07-15 08:50:02 UTC

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New package shinyChakraUI with initial version 1.0.0
Package: shinyChakraUI
Title: A Wrapper of the 'React' Library 'Chakra UI' for 'Shiny'
Version: 1.0.0
Authors@R: c( person(given = "Stéphane", family = "Laurent", role = c("aut", "cre"), email = "laurent_step@outlook.fr"), person(given = "Segun", family = "Adebayo", role = "cph", comment = "'Chakra UI' library (https://chakra-ui.com/)"), person(given = "David", family = "Kaye", role = "ctb", comment = "'json-normalize.js'"), person(given = "RubyLouvre", role = "cph", comment = "'jsx-parser' library"))
Description: Makes the 'React' library 'Chakra UI' usable in 'Shiny' apps. 'Chakra UI' components include alert dialogs, drawers (sliding panels), menus, modals, popovers, sliders, and more.
License: GPL (>= 3)
Encoding: UTF-8
URL: https://github.com/stla/shinyChakraUI
BugReports: https://github.com/stla/shinyChakraUI/issues
Imports: htmltools, reactR, shiny, jsonlite, rlang, stringr, grDevices, utils, formatR
Suggests: testthat (>= 3.0.0), V8
NeedsCompilation: no
Packaged: 2021-07-14 13:50:42 UTC; SDL96354
Author: Stéphane Laurent [aut, cre], Segun Adebayo [cph] ('Chakra UI' library (https://chakra-ui.com/)), David Kaye [ctb] ('json-normalize.js'), RubyLouvre [cph] ('jsx-parser' library)
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>
Repository: CRAN
Date/Publication: 2021-07-15 07:50:03 UTC

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New package Rsgf with initial version 1.0.0
Package: Rsgf
Title: SGF (Smart Game File) File Format Import
Version: 1.0.0
Date: 2021-07-13
Author: Brad Cable
Maintainer: Brad Cable <brad@bcable.net>
Description: Import SGF (Smart Game File) into R.
Imports: ggplot2, methods, png, stringi, stringr, utils
License: MIT + file LICENSE
NeedsCompilation: no
Packaged: 2021-07-14 05:37:17 UTC; lister
Repository: CRAN
Date/Publication: 2021-07-15 07:20:02 UTC

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New package pathwayTMB with initial version 0.1.0
Package: pathwayTMB
Title: Pathway Based Tumor Mutational Burden
Version: 0.1.0
Maintainer: Junwei Han <hanjunwei1981@163.com>
Authors@R: c(person(given = "Junwei", family = "Han", email = "hanjunwei1981@163.com", role = c("aut", "cre", "cph")), person(given = "Xiangmei", family = "Li", role = "aut"))
Description: A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy.
License: GPL (>= 2)
Depends: R (>= 4.1.0)
Encoding: UTF-8
LazyData: true
biocViews:
Imports: maftools, clusterProfiler, BiocGenerics, GenomicFeatures, purrr, utils, glmnet, randomForest, stats, survival, survminer, caret, data.table, RColorBrewer, grDevices, pROC, graphics
VignetteBuilder: knitr,rmarkdown,qpdf
Suggests: stringi, knitr, rmarkdown, testthat, BiocManager, xfun, e1071, qpdf, tinytex, spelling
Language: en-US
NeedsCompilation: no
Packaged: 2021-07-14 12:12:36 UTC; DELL
Author: Junwei Han [aut, cre, cph], Xiangmei Li [aut]
Repository: CRAN
Date/Publication: 2021-07-15 07:50:07 UTC

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New package parqr with initial version 0.1.0
Package: parqr
Title: Read in Multi-Part Parquet Files
Version: 0.1.0
Authors@R: person("John", "Waller", email = "jhnwllr@gmail.com", role = c("aut", "cre"))
Description: Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.
URL: https://github.com/jhnwllr/parqr
License: CC0
BugReports: https://github.com/jhnwllr/parqr/issues
Encoding: UTF-8
Language: en-US
Imports: purrr, arrow, magrittr
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-07-14 12:31:05 UTC; ftw712
Author: John Waller [aut, cre]
Maintainer: John Waller <jhnwllr@gmail.com>
Repository: CRAN
Date/Publication: 2021-07-15 07:50:09 UTC

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New package DCLEAR with initial version 1.0.0
Package: DCLEAR
Version: 1.0.0
Date: 2021-07-13
Title: Distance Based Cell Lineage Reconstruction
Authors@R: c(person("Il-Youp", "Kwak", role = c("aut", "cre"), email = "ikwak2@cau.ac.kr"), person("Wuming", "Gong", role = "aut", email = "gongx030@umn.edu"))
Author: Il-Youp Kwak [aut, cre], Wuming Gong [aut]
Maintainer: Il-Youp Kwak <ikwak2@cau.ac.kr>
License: GPL-3
VignetteBuilder: knitr
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.6.0)
biocViews:
Encoding: UTF-8
Imports: BiocParallel, dplyr, Matrix, matrixStats, ape, phangorn, Rcpp, igraph, methods, purrr, stringr, tidyr, tidyverse, rBayesianOptimization, rlang
Suggests: knitr, rmarkdown, markdown
Description: R codes for distance based cell lineage reconstruction. Our methods won both sub-challenges 2 and 3 of the Allen Institute Cell Lineage Reconstruction DREAM Challenge in 2020. The challenge paper is Wuming et al. (2021) <doi:10.1016/j.cels.2021.05.008>.
URL: https://github.com/ikwak2/DCLEAR
NeedsCompilation: yes
Packaged: 2021-07-14 08:13:17 UTC; ikwak10
Repository: CRAN
Date/Publication: 2021-07-15 07:30:02 UTC

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New package bootPLS with initial version 0.9.9
Package: bootPLS
Version: 0.9.9
Date: 2021-07-14
Depends: R (>= 3.5.0)
biocViews:
Imports: boot, doParallel, foreach, plsRglm, pls, spls, bipartite, mvtnorm
Enhances:
Suggests: knitr, markdown, plsdof, prettydoc, rmarkdown
Title: Bootstrap Hyperparameter Selection for PLS Models and Extensions
Authors@R: c( person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@utt.fr", comment = c(ORCID = "0000-0002-0837-8281")), person(given = "Jeremy", family= "Magnanensi", role = c("aut"), email = "jeremy.magnanensi@gmail.com"), person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy-bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-4615-1512")))
Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Jeremy Magnanensi [aut], Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>)
Maintainer: Frederic Bertrand <frederic.bertrand@utt.fr>
Description: Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', accepted.).
License: GPL-3
Encoding: UTF-8
Classification/MSC: 62N01, 62N02, 62N03, 62N99
LazyData: true
VignetteBuilder: knitr
URL: https://fbertran.github.io/bootPLS/, https://github.com/fbertran/bootPLS/
BugReports: https://github.com/fbertran/bootPLS/issues/
NeedsCompilation: no
Packaged: 2021-07-14 09:56:36 UTC; fbertran
Repository: CRAN
Date/Publication: 2021-07-15 07:50:12 UTC

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New package SEofM with initial version 0.1.0
Package: SEofM
Title: Standard Error of Measurement
Version: 0.1.0
Authors@R: c(person("Zhicheng Du", role = c("aut","cre","cph"), email = "dgdzc@hotmail.com"), person("Yuantao Hao", role = "aut") )
Description: To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation). The methods used in this package are referenced from Zoran B. Popović (2017) <doi:10.21037/cdt.2017.03.12>.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2021-07-14 02:13:27 UTC; dgdzc
Author: Zhicheng Du [aut, cre, cph], Yuantao Hao [aut]
Maintainer: Zhicheng Du <dgdzc@hotmail.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2021-07-15 07:00:02 UTC

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Package ausplotsR updated to version 1.2.5 with previous version 1.2.4 dated 2021-04-18

Title: TERN AusPlots Analysis Package
Description: Extraction, preparation, visualisation and analysis of TERN AusPlots ecosystem monitoring data. Direct access to plot-based data on vegetation and soils across Australia, including physical sample barcode numbers. Simple function calls extract the data and merge them into species occurrence matrices for downstream analysis, or calculate things like basal area and fractional cover. TERN AusPlots is a national field plot-based ecosystem surveillance monitoring method and dataset for Australia. The data have been collected across a national network of plots and transects by the Terrestrial Ecosystem Research Network (TERN - <https://www.tern.org.au>), an Australian Government NCRIS-enabled project, and its Ecosystem Surveillance platform (<https://www.tern.org.au/tern-observatory/tern-ecosystem-surveillance/>).
Author: Greg Guerin [aut, cre], Tom Saleeba [aut], Samantha Munroe [aut], Bernardo Blanco-Martin [aut], Irene Martín-Forés [aut], Andrew Tokmakoff [aut]
Maintainer: Greg Guerin <ggueri01@gmail.com>

Diff between ausplotsR versions 1.2.4 dated 2021-04-18 and 1.2.5 dated 2021-07-15

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Package iMRMC updated to version 1.2.3 with previous version 1.2.2 dated 2020-03-24

Title: Multi-Reader, Multi-Case Analysis Methods (ROC, Agreement, and Other Metrics)
Description: Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. The initial package analyzes the reader-average area under the receiver operating characteristic (ROC) curve with U-statistics according to Gallas, Bandos, Samuelson, and Wagner 2009 <doi:10.1080/03610920802610084>. Additional functions analyze other endpoints with U-statistics (binary performance and score differences) following the work by Gallas, Pennello, and Myers 2007 <doi:10.1364/JOSAA.24.000B70>. Package development and documentation is at <https://github.com/DIDSR/iMRMC/tree/master>.
Author: Brandon Gallas
Maintainer: Brandon Gallas <Brandon.Gallas@fda.hhs.gov>

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Package NonCompart updated to version 0.4.9 with previous version 0.4.8 dated 2021-06-16

Title: Noncompartmental Analysis for Pharmacokinetic Data
Description: Conduct a noncompartmental analysis with industrial strength. Some features are 1) Use of CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
Author: Kyun-Seop Bae [aut]
Maintainer: Kyun-Seop Bae <k@acr.kr>

Diff between NonCompart versions 0.4.8 dated 2021-06-16 and 0.4.9 dated 2021-07-15

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Package countrycode updated to version 1.3.0 with previous version 1.2.0 dated 2020-05-22

Title: Convert Country Names and Country Codes
Description: Standardize country names, convert them into one of 40 different coding schemes, convert between coding schemes, and assign region descriptors.
Author: Vincent Arel-Bundock [aut, cre] (<https://orcid.org/0000-0003-2042-7063>), CJ Yetman [ctb] (<https://orcid.org/0000-0001-5099-9500>), Nils Enevoldsen [ctb] (<https://orcid.org/0000-0001-7195-4117>)
Maintainer: Vincent Arel-Bundock <vincent.arel-bundock@umontreal.ca>

Diff between countrycode versions 1.2.0 dated 2020-05-22 and 1.3.0 dated 2021-07-15

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Package clipp updated to version 1.0.1 with previous version 1.0.0 dated 2021-05-05

Title: Calculating Likelihoods by Pedigree Paring
Description: A fast and general implementation of the Elston-Stewart algorithm that can calculate the likelihoods of large and complex pedigrees. References for the Elston-Stewart algorithm are Elston & Stewart (1971) <doi:10.1159/000152448>, Lange & Elston (1975) <doi:10.1159/000152714> and Cannings et al. (1978) <doi:10.2307/1426718>.
Author: James Dowty [aut, cre], Kevin Wong [aut]
Maintainer: James Dowty <jgdowty@gmail.com>

Diff between clipp versions 1.0.0 dated 2021-05-05 and 1.0.1 dated 2021-07-15

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Package bayesplay updated to version 0.9.1 with previous version 0.9.0 dated 2021-06-17

Title: The Bayes Factor Playground
Description: A lightweight modelling syntax for defining likelihoods and priors and for computing Bayes factors for simple one parameter models. It includes functionality for computing and plotting priors, likelihoods, and model predictions. Additional functionality is included for computing and plotting posteriors.
Author: Lincoln J Colling [aut, cre] (<https://orcid.org/0000-0002-3572-7758>)
Maintainer: Lincoln J Colling <lincoln@colling.net.nz>

Diff between bayesplay versions 0.9.0 dated 2021-06-17 and 0.9.1 dated 2021-07-15

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