Thu, 02 Jul 2020

Package kirby21.base updated to version 1.7.3 with previous version 1.7.0 dated 2018-08-13

Title: Example Data from the Multi-Modal MRI 'Reproducibility' Resource
Description: Multi-modal magnetic resonance imaging ('MRI') data from the 'Kirby21' 'reproducibility' study <https://www.nitrc.org/projects/multimodal/>, including functional and structural imaging.
Author: John Muschelli <muschellij2@gmail.com>
Maintainer: John Muschelli <muschellij2@gmail.com>

Diff between kirby21.base versions 1.7.0 dated 2018-08-13 and 1.7.3 dated 2020-07-02

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Package unmarked updated to version 1.0.1 with previous version 1.0.0 dated 2020-05-04

Title: Models for Data from Unmarked Animals
Description: Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates.
Author: Richard Chandler [aut], Ken Kellner [aut], Ian Fiske [aut], David Miller [aut], Andy Royle [cre, aut], Jeff Hostetler [aut], Rebecca Hutchinson [aut], Adam Smith [aut], Marc Kery [ctb], Mike Meredith [ctb], Auriel Fournier [ctb], Ariel Muldoon [ctb], Chris Baker [ctb]
Maintainer: Andy Royle <aroyle@usgs.gov>

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Package nloptr updated to version 1.2.2.2 with previous version 1.2.2.1 dated 2020-03-11

Title: R Interface to NLopt
Description: Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See <http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction> for more information on the available algorithms. During installation of nloptr on Unix-based systems, the installer checks whether the NLopt library is installed on the system. If the NLopt library cannot be found, the code is compiled using the NLopt source included in the nloptr package.
Author: Jelmer Ypma [aut, cre], Steven G. Johnson [aut] (author of the NLopt C library), Hans W. Borchers [ctb], Dirk Eddelbuettel [ctb], Brian Ripley [ctb] (build process on multiple OS), Kurt Hornik [ctb] (build process on multiple OS), Julien Chiquet [ctb], Avraham Adler [ctb] (removal deprecated calls from tests, <https://orcid.org/0000-0002-3039-0703>)
Maintainer: Jelmer Ypma <uctpjyy@ucl.ac.uk>

Diff between nloptr versions 1.2.2.1 dated 2020-03-11 and 1.2.2.2 dated 2020-07-02

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Package MNLpred updated to version 0.0.3 with previous version 0.0.2 dated 2020-03-04

Title: Simulated Predicted Probabilities for Multinomial Logit Models
Description: Functions to easily return simulated predicted probabilities and first differences for multinomial logit models. It takes a specified scenario and a multinomial model to predict probabilities with a set of coefficients, drawn from a simulated sampling distribution. The simulated predictions allow for meaningful plots with means and confidence intervals. The methodological approach is based on the principles laid out by King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan (2016) <doi:10.1111/j.1540-5907.2012.00602.x>.
Author: Manuel Neumann [aut, cre]
Maintainer: Manuel Neumann <manuel.neumann@mzes.uni-mannheim.de>

Diff between MNLpred versions 0.0.2 dated 2020-03-04 and 0.0.3 dated 2020-07-02

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New package matrixpls with initial version 1.0.10
Encoding: UTF-8
Package: matrixpls
Type: Package
Title: Matrix-Based Partial Least Squares Estimation
Version: 1.0.10
Date: 2020-07-02
Authors@R: c(person("Mikko", "Rönkkö", role=c("aut", "cre"), email = "mikko.ronkko@jyu.fi"))
Maintainer: Mikko Rönkkö <mikko.ronkko@jyu.fi>
Description: Partial Least Squares Path Modeling algorithm and related algorithms. The algorithm implementations aim for computational efficiency using matrix algebra and covariance data. The package is designed toward Monte Carlo simulations and includes functions to perform simple Monte Carlo simulations.
URL: https://github.com/mronkko/matrixpls
BugReports: https://github.com/mronkko/matrixpls/issues
ByteCompile: TRUE
Imports: assertive, matrixcalc, lavaan, MASS, methods, psych
Suggests: simsem, RUnit, parallel, semPLS, boot, Matrix, ASGSCA, knitr, R.rsp
License: GPL-3
LazyLoad: yes
LazyData: true
RoxygenNote: 7.1.1
Collate: 'convCheck.R' 'estimator.R' 'innerEstim.R' 'matrixpls.R' 'matrixpls.boot.R' 'matrixpls.crossvalidate.R' 'matrixpls.optim.R' 'matrixpls.plspm.R' 'matrixpls.postestimation.R' 'matrixpls.predict.R' 'matrixpls.sempls.R' 'matrixpls.sim.R' 'matrixpls.util.R' 'outerEstim.R' 'paramEstimator.R' 'reliabilityEstim.R' 'signChange.R' 'weightFun.R' 'weightSign.R'
VignetteBuilder: R.rsp
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2020-07-02 15:12:12 UTC; mivaanro
Author: Mikko Rönkkö [aut, cre]
Repository: CRAN
Date/Publication: 2020-07-02 16:50:02 UTC

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New package dna with initial version 2.1-2
Package: dna
Version: 2.1-2
Date: 2020-06-30
Depends: R (>= 4.0.0)
Imports: methods
Suggests: igraph, lars
Authors@R: c(person(c("Ryan","S."), "Gill", role = c("aut", "cre"), email = "ryan.gill@louisville.edu"), person("Somnath", "Datta", role = "aut"), person("Susmita", "Datta", role = "aut"))
Description: Package for conducting differential network analysis from microarray data.
Title: Differential Network Analysis
LazyData: yes
License: GPL-2 | GPL-3
NeedsCompilation: yes
Packaged: 2020-06-30 23:42:23 UTC; ryan
Author: Ryan S. Gill [aut, cre], Somnath Datta [aut], Susmita Datta [aut]
Maintainer: Ryan S. Gill <ryan.gill@louisville.edu>
Repository: CRAN
Date/Publication: 2020-07-02 16:50:08 UTC

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Package DALEX updated to version 1.3.0 with previous version 1.2.1 dated 2020-04-25

Title: moDel Agnostic Language for Exploration and eXplanation
Description: Unverified black box model is the path to the failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack of direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) <arXiv:1806.08915>.
Author: Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>), Szymon Maksymiuk [aut], Hubert Baniecki [aut] (<https://orcid.org/0000-0001-6661-5364>)
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>

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Package cdcatR updated to version 1.0.1 with previous version 1.0.0 dated 2020-06-13

Title: Cognitive Diagnostic Computerized Adaptive Testing
Description: Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen (2009) <doi:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <doi:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <doi:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <doi:10.1027/1614-2241/a000131>.
Author: Miguel A. Sorrel [aut, cre, cph], Pablo Nájera [aut, cph], Francisco J. Abad [aut, cph]
Maintainer: Miguel A. Sorrel <miguel.sorrel@uam.es>

Diff between cdcatR versions 1.0.0 dated 2020-06-13 and 1.0.1 dated 2020-07-02

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Package vivo updated to version 0.2.0 with previous version 0.1.2 dated 2020-02-27

Title: Variable Importance via Oscillations
Description: Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile and global variable importance measure based on Partial Dependence Profiles.
Author: Anna Kozak [aut, cre], Przemyslaw Biecek [aut, ths]
Maintainer: Anna Kozak <anna1993kozak@gmail.com>

Diff between vivo versions 0.1.2 dated 2020-02-27 and 0.2.0 dated 2020-07-02

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Package metasens updated to version 0.4-1 with previous version 0.4-0 dated 2019-08-06

Title: Advanced Statistical Methods to Model and Adjust for Bias in Meta-Analysis
Description: The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 'Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>; - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>; - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>; - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>.
Author: Guido Schwarzer [cre, aut] (<https://orcid.org/0000-0001-6214-9087>), James R. Carpenter [aut] (<https://orcid.org/0000-0003-3890-6206>), Gerta Rücker [aut] (<https://orcid.org/0000-0002-2192-2560>)
Maintainer: Guido Schwarzer <sc@imbi.uni-freiburg.de>

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Package hardhat updated to version 0.1.4 with previous version 0.1.3 dated 2020-05-20

Title: Construct Modeling Packages
Description: Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input.
Author: Davis Vaughan [aut, cre], Max Kuhn [aut], RStudio [cph]
Maintainer: Davis Vaughan <davis@rstudio.com>

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Package Rlof updated to version 1.1.2 with previous version 1.1.1 dated 2015-09-17

Title: R Parallel Implementation of Local Outlier Factor(LOF)
Description: R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
Author: Yingsong Hu, Wayne Murray and Yin Shan, Australia.
Maintainer: Yingsong Hu <yingsonghu@hotmail.com>

Diff between Rlof versions 1.1.1 dated 2015-09-17 and 1.1.2 dated 2020-07-02

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Package landsepi updated to version 1.0.1 with previous version 1.0.0 dated 2020-07-01

Title: Landscape Epidemiology and Evolution
Description: A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes. Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.
Author: Loup Rimbaud [aut], Julien Papaix [aut], Jean-Francois Rey [cre], Jean-Loup Gaussen [ctb]
Maintainer: Jean-Francois Rey <jean-francois.rey@inrae.fr>

Diff between landsepi versions 1.0.0 dated 2020-07-01 and 1.0.1 dated 2020-07-02

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More information about landsepi at CRAN
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Package zetadiv updated to version 1.2.0 with previous version 1.1.1 dated 2018-05-17

Title: Functions to Compute Compositional Turnover Using Zeta Diversity
Description: Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes functions to compute zeta-diversity for a specific number of assemblages and to compute zeta-diversity for a range of numbers of assemblages. It also includes functions to explain how zeta-diversity varies with distance and with differences in environmental variables between assemblages, using generalised linear models, linear models with negative constraints, generalised additive models,shape constrained additive models, and I-splines.
Author: Guillaume Latombe [aut, cre], Melodie A. McGeoch [aut], David A. Nipperess [aut], Cang Hui [aut]
Maintainer: Guillaume Latombe <latombe.guillaume@gmail.com>

Diff between zetadiv versions 1.1.1 dated 2018-05-17 and 1.2.0 dated 2020-07-02

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 39 files changed, 3514 insertions(+), 2255 deletions(-)

More information about zetadiv at CRAN
Permanent link

Package valueEQ5D updated to version 0.5.2 with previous version 0.5.1 dated 2020-01-23

Title: Scoring the EQ-5D Descriptive System
Description: EQ-5D is a standard instrument (<https://euroqol.org/eq-5d-instruments/>) that measures the quality of life often used in clinical and economic evaluations of health care technologies. Both adult versions of EQ-5D (EQ-5D-3L and EQ-5D-5L) contain a descriptive system and visual analog scale. The descriptive system measures the patient's health in 5 dimensions: the 5L versions has 5 levels and 3L version has 3 levels. The descriptive system scores are usually converted to index values using country specific values sets (that incorporates the country preferences). This package allows the calculation of both descriptive system scores to the index value scores. The value sets for EQ-5D-3L are from the references mentioned in the website <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/valuation/> The value sets for EQ-5D-3L for a total of 31 countries are used for the valuation (see the user guide for a complete list of references). The value sets for EQ-5D-5L are obtained from references mentioned in the <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/valuation-standard-value-sets/> and other sources. The value sets for EQ-5D-5L for a total of 17 countries are used for the valuation (see the user guide for a complete list of references). The package can also be used to map 5L scores to 3L index values for 10 countries: Denmark, France, Germany, Japan, Netherlands, Spain, Thailand, UK, USA, and Zimbabwe. The value set and method for mapping are obtained from Van Hout et al (2012) <doi: 10.1016/j.jval.2012.02.008>.
Author: Sheeja Manchira Krishnan [aut, cre]
Maintainer: Sheeja Manchira Krishnan <sheejamk@gmail.com>

Diff between valueEQ5D versions 0.5.1 dated 2020-01-23 and 0.5.2 dated 2020-07-02

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More information about valueEQ5D at CRAN
Permanent link

New package rCAT with initial version 0.1.6
Package: rCAT
Title: Conservation Assessment Tools
Version: 0.1.6
Authors@R: c(person(given = "Justin", family = "Moat", role = c("aut", "cre"), email = "J.Moat@kew.org", comment = c(ORCID = "0000-0002-5513-3615")), person(given = "Steven", family = "Bachman", role = c("ctb"), email = "S.Bachman@kew.org", comment = c(ORCID = "")))
Depends: R (>= 3.3.0)
Suggests: rgeos, rgbif (>= 0.9.2), knitr,rmarkdown, R.rsp,testthat,spelling
Description: A set of tools and functions to help with species conservation assessments (Red List threat assessments). Includes Extent of occurrence, Area of Occupancy, Minimum Enclosing Rectangle, a geographic Projection Wizard and Species batch processing.
License: MIT + file LICENSE
Imports: pracma (>= 1.9.5), rgdal (>= 1.2-5), sp (>= 1.2-3), grDevices (>= 3.3.2)
Encoding: UTF-8
LazyData: true
Language: en-US
RoxygenNote: 7.1.0
VignetteBuilder: knitr
Date: 2020-07-01
NeedsCompilation: no
Packaged: 2020-07-02 11:27:02 UTC; jm22kg
Author: Justin Moat [aut, cre] (<https://orcid.org/0000-0002-5513-3615>), Steven Bachman [ctb]
Maintainer: Justin Moat <J.Moat@kew.org>
Repository: CRAN
Date/Publication: 2020-07-02 12:30:07 UTC

More information about rCAT at CRAN
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Package noctua updated to version 1.7.1 with previous version 1.7.0 dated 2020-05-14

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>

Diff between noctua versions 1.7.0 dated 2020-05-14 and 1.7.1 dated 2020-07-02

 DESCRIPTION                    |    8 ++++----
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 13 files changed, 88 insertions(+), 36 deletions(-)

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Package upsetjs updated to version 1.6.0 with previous version 1.3.1 dated 2020-06-21

Title: 'HTMLWidget' Wrapper of 'UpSet.js' for Exploring Large Set Intersections
Description: 'UpSet.js' is a re-implementation of 'UpSetR' to create interactive set visualizations for more than three sets. This is a 'htmlwidget' wrapper around the 'JavaScript' library 'UpSet.js'.
Author: Samuel Gratzl [aut, cre]
Maintainer: Samuel Gratzl <sam@sgratzl.com>

Diff between upsetjs versions 1.3.1 dated 2020-06-21 and 1.6.0 dated 2020-07-02

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More information about upsetjs at CRAN
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Package tidyquant updated to version 1.0.1 with previous version 1.0.0 dated 2020-03-04

Title: Tidy Quantitative Financial Analysis
Description: Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples.
Author: Matt Dancho [aut, cre], Davis Vaughan [aut]
Maintainer: Matt Dancho <mdancho@business-science.io>

Diff between tidyquant versions 1.0.0 dated 2020-03-04 and 1.0.1 dated 2020-07-02

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 50 files changed, 1605 insertions(+), 1347 deletions(-)

More information about tidyquant at CRAN
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New package smartmap with initial version 0.1.1
Type: Package
Package: smartmap
Title: Smartly Create Maps from R Objects
Version: 0.1.1
Authors@R: person(given = "Stefan", family = "Fleck", role = c("aut", "cre"), email = "stefan.b.fleck@gmail.com", comment = c(ORCID = "0000-0003-3344-9851"))
Maintainer: Stefan Fleck <stefan.b.fleck@gmail.com>
Description: Preview spatial data as 'leaflet' maps with minimal effort. smartmap is optimized for interactive use and distinguishes itself from similar packages because it does not need real spatial ('sp' or 'sf') objects an input; instead, it tries to automatically coerce everything that looks like spatial data to sf objects or leaflet maps. It - for example - supports direct mapping of: a vector containing a single coordinate pair, a two column matrix, a data.frame with longitude and latitude columns, or the path or URL to a (possibly compressed) 'shapefile'.
License: MIT + file LICENSE
Imports: leaflet, magrittr, sf, utils
Suggests: covr, testthat
Depends: R (>= 3.6.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1.9000
NeedsCompilation: no
Packaged: 2020-07-01 14:36:26 UTC; fleck
Author: Stefan Fleck [aut, cre] (<https://orcid.org/0000-0003-3344-9851>)
Repository: CRAN
Date/Publication: 2020-07-02 11:30:09 UTC

More information about smartmap at CRAN
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Package RAthena updated to version 1.9.1 with previous version 1.9.0 dated 2020-05-14

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.
Author: Dyfan Jones [aut, cre]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

Diff between RAthena versions 1.9.0 dated 2020-05-14 and 1.9.1 dated 2020-07-02

 DESCRIPTION                    |   10 +++++-----
 MD5                            |   22 +++++++++++-----------
 NEWS.md                        |    9 ++++++++-
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New package qgshiny with initial version 0.1.0
Package: qgshiny
Title: A 'shiny' Application for Active Learning Instruction in Introductory Quantitative Genetics
Version: 0.1.0
Authors@R: person(given = "Jeffrey", family = "Neyhart", role = c("aut", "cre"), email = "neyhartje@gmail.com", comment = c(ORCID = "0000-0002-1991-5310"))
Description: A 'shiny' application for teaching introductory quantitative genetics and plant breeding through interactive simulations. The application relies on established plant breeding and quantitative genetic theory found in Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427).
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Depends: R (>= 3.5.0)
Imports: stats, shiny, rmarkdown
NeedsCompilation: no
Packaged: 2020-07-01 14:14:51 UTC; jln54
Author: Jeffrey Neyhart [aut, cre] (<https://orcid.org/0000-0002-1991-5310>)
Maintainer: Jeffrey Neyhart <neyhartje@gmail.com>
Repository: CRAN
Date/Publication: 2020-07-02 11:20:02 UTC

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New package popgenr with initial version 0.1
Package: popgenr
Type: Package
Title: Accompaniment to the Book Population Genetics with R: An Introduction for Life Scientists
Version: 0.1
Author: Aki Jarl Laruson; Floyd Reed
Maintainer: Aki Jarl Laruson <akijarl@gmail.com>
Description: Provides several data sets and a linkage disequilibrium function to accompany the book "Population Genetics with R: An Introduction for Life Scientists".
License: CC0
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2020-07-01 14:05:27 UTC; root
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2020-07-02 11:20:06 UTC

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Package n1qn1 updated to version 6.0.1-9 with previous version 6.0.1-8 dated 2020-06-29

Title: Port of the 'Scilab' 'n1qn1' Modules for Un-constrained BFGS Optimization
Description: Provides 'Scilab' 'n1qn1', or Quasi-Newton BFGS "qn" without constraints and 'qnbd' or Quasi-Newton BFGS with constraints. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. Both algorithms are described in the 'Scilab' optimization documentation located at <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>.
Author: Matthew Fidler [aut, cre], Wenping Wang [aut], Claude Lemarechal [aut, ctb], Joseph Bonnans [ctb], Jean-Charles Gilbert [ctb], Claudia Sagastizabal [ctb], Stephen L. Campbell, [ctb], Jean-Philippe Chancelier [ctb], Ramine Nikoukhah [ctb], Dirk Eddelbuettel [ctb], Bruno Jofret [ctb], INRIA [cph]
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>

Diff between n1qn1 versions 6.0.1-8 dated 2020-06-29 and 6.0.1-9 dated 2020-07-02

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New package oysteR with initial version 0.0.3
Package: oysteR
Title: Scans R Projects for Vulnerable Third Party Dependencies
Version: 0.0.3
Authors@R: c(person(given = "Jeffry", family = "Hesse", role = "aut", email = "jhesse@sonatype.com"), person(given = "Brittany", family = "Belle", role = "aut", email = "bbelle@sonatype.com"), person(given = "Colin", family = "Gillespie", role = c("aut", "cre"), email = "csgillespie@gmail.com", comment = c(ORCID = "0000-0003-1787-0275")), person(given = "Dan", family = "Rollo", role = "aut", email = "drollo@sonatype.com"), person(given = "Sonatype", role = "cph"))
Maintainer: Colin Gillespie <csgillespie@gmail.com>
Description: Collects a list of your third party R packages, and scans them with the 'OSS' Index provided by 'Sonatype', reporting back on any vulnerabilities that are found in the third party packages you use.
License: Apache License 2.0 | file LICENSE
URL: https://github.com/sonatype-nexus-community/oysteR
BugReports: https://github.com/sonatype-nexus-community/oysteR/issues
Depends: R (>= 3.5.0)
Imports: cli, dplyr, glue, httr, purrr, rjson, tibble, tidyr, utils
Suggests: httptest, knitr, rmarkdown, testthat (>= 2.1.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-07-01 13:59:42 UTC; ncsg3
Author: Jeffry Hesse [aut], Brittany Belle [aut], Colin Gillespie [aut, cre] (<https://orcid.org/0000-0003-1787-0275>), Dan Rollo [aut], Sonatype [cph]
Repository: CRAN
Date/Publication: 2020-07-02 11:30:13 UTC

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New package multiMarker with initial version 1.0
Package: multiMarker
Version: 1.0
Date: 2020-05-29
Title: Latent Variable Model to Infer Food Intake from Multiple Biomarkers
Description: A latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates prediction of the intake along with its associated uncertainty. Details are in D'Angelo, et al. (2020) <arXiv:2006.02995>.
Authors@R: c( person("Silvia", "D\'Angelo", role = c("aut", "cre"), email = "silvia.dangelo@ucd.ie"), person("Claire", "Gormley", role = "ctb"), person("Lorraine", "Brennan", role = "ctb") )
Maintainer: Silvia D'Angelo <silvia.dangelo@ucd.ie>
Imports: truncnorm, ordinalNet
Depends: R (>= 3.0)
License: GPL (>= 2)
Encoding: UTF-8
ByteCompile: true
LazyData: true
NeedsCompilation: no
Packaged: 2020-06-09 15:09:16 UTC; Silvia
Author: Silvia D'Angelo [aut, cre], Claire Gormley [ctb], Lorraine Brennan [ctb]
Repository: CRAN
Date/Publication: 2020-07-02 11:10:07 UTC

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Package glmmTMB updated to version 1.0.2.1 with previous version 1.0.1 dated 2020-03-15

Title: Generalized Linear Mixed Models using Template Model Builder
Description: Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.
Author: Arni Magnusson [aut] (<https://orcid.org/0000-0003-2769-6741>), Hans Skaug [aut], Anders Nielsen [aut] (<https://orcid.org/0000-0001-9683-9262>), Casper Berg [aut] (<https://orcid.org/0000-0002-3812-5269>), Kasper Kristensen [aut], Martin Maechler [aut] (<https://orcid.org/0000-0002-8685-9910>), Koen van Bentham [aut], Ben Bolker [aut] (<https://orcid.org/0000-0002-2127-0443>), Nafis Sadat [ctb] (<https://orcid.org/0000-0001-5715-616X>), Daniel Lüdecke [ctb] (<https://orcid.org/0000-0002-8895-3206>), Russ Lenth [ctb], Joseph O'Brien [ctb] (<https://orcid.org/0000-0001-9851-5077>), Mollie Brooks [aut, cre] (<https://orcid.org/0000-0001-6963-8326>)
Maintainer: Mollie Brooks <mollieebrooks@gmail.com>

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Package genlasso updated to version 1.5 with previous version 1.4 dated 2019-07-11

Title: Path Algorithm for Generalized Lasso Problems
Description: Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed.
Author: Taylor B. Arnold and Ryan J. Tibshirani
Maintainer: Taylor Arnold <taylor.arnold@acm.org>

Diff between genlasso versions 1.4 dated 2019-07-11 and 1.5 dated 2020-07-02

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Package abn updated to version 2.2.2 with previous version 2.2.1 dated 2020-06-15

Title: Modelling Multivariate Data with Additive Bayesian Networks
Description: Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. 'abn' provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data. The additive formulation of these models is equivalent to multivariate generalised linear modelling (including mixed models with iid random effects). The usual term to describe this model selection process is structure discovery. The core functionality is concerned with model selection - determining the most robust empirical model of data from interdependent variables. Laplace approximations are used to estimate goodness of fit metrics and model parameters, and wrappers are also included to the INLA package which can be obtained from <http://www.r-inla.org>. The computing library JAGS <http://mcmc-jags.sourceforge.net> is used to simulate 'abn'-like data. A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website <http://r-bayesian-networks.org>.
Author: Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>), Fraser Ian Lewis [aut], Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>), Marta Pittavino [ctb] (<https://orcid.org/0000-0002-1232-1034>)
Maintainer: Gilles Kratzer <gilles.kratzer@gmail.com>

Diff between abn versions 2.2.1 dated 2020-06-15 and 2.2.2 dated 2020-07-02

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New package PWFSLSmoke with initial version 1.2.111
Type: Package
Package: PWFSLSmoke
Version: 1.2.111
Title: Utilities for Working with Air Quality Monitoring Data
Authors@R: c( person("Jonathan", "Callahan", email="jonathan.s.callahan@gmail.com", role=c("aut","cre")), person("Hans", "Martin", email="hansmrtn@gmail.com", role=c("aut")), person("Spencer", "Pease", email="spencerpease618@gmail.com", role=c("aut")), person("Helen", "Miller", email="helen@ohana.org", role=c("aut")), person("Zach", "Dingels", email="dingelsz@uw.edu", role=c("aut")), person("Rohan", "Aras", email="rohana@uw.edu", role=c("aut")), person("Jon", "Hagg", email="jhagg314@gmail.com", role=c("aut")), person("Jimin", "Kim", email="jk55@uw.edu", role=c("aut")), person("Rex", "Thompson", email="rexs.thompson@gmail.com", role=c("aut")), person("Alice", "Yang", email="wenhuy@uw.edu", role=c("aut")) )
Author: Jonathan Callahan [aut, cre], Hans Martin [aut], Spencer Pease [aut], Helen Miller [aut], Zach Dingels [aut], Rohan Aras [aut], Jon Hagg [aut], Jimin Kim [aut], Rex Thompson [aut], Alice Yang [aut]
Maintainer: Jonathan Callahan <jonathan.s.callahan@gmail.com>
Depends: R (>= 3.3.0), dplyr (>= 0.8.3), maps, MazamaCoreUtils (>= 0.4.3), MazamaSpatialUtils (>= 0.6.3)
Imports: curl, cluster, dygraphs (>= 1.1.1.4), geosphere, ggmap, glue, httr, jsonlite, leaflet (>= 1.0.0), lubridate, magrittr, mapproj, maptools, methods, openair, png, purrr, raster, RColorBrewer, rgdal, RgoogleMaps, readr, reshape2, rlang, sf, slippymath, sp, stringr, tibble, tidyr, xts
Suggests: knitr, lintr, rmarkdown, testthat (>= 2.1.0)
Description: Utilities for working with air quality monitoring data with a focus on small particulates (PM2.5) generated by wildfire smoke. Functions are provided for downloading available data from the United States 'EPA' <https://www.epa.gov/outdoor-air-quality-data> and it's 'AirNow' air quality site <https://www.airnow.gov>. Additional sources of PM2.5 data made accessible by the package include: 'AIRSIS' (password protected) <https://www.oceaneering.com/data-management/> and 'WRCC' <https://wrcc.dri.edu/cgi-bin/smoke.pl>. Data compilations are provided by 'PWFSL' <https://www.fs.fed.us/pnw/pwfsl/>.
License: GPL-3
VignetteBuilder: knitr
Repository: CRAN
URL: https://github.com/MazamaScience/PWFSLSmoke
BugReports: https://github.com/MazamaScience/PWFSLSmoke/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-07-01 18:30:11 UTC; jonathan
Date/Publication: 2020-07-02 10:30:02 UTC

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Package bmotif updated to version 2.0.1 with previous version 2.0.0 dated 2019-12-11

Title: Motif Analyses of Bipartite Networks
Description: Counts occurrences of motifs in bipartite networks, as well as the number of times each node or link appears in each unique position within motifs. Has support for both binary and weighted motifs: can calculate the mean weight of motifs and the standard deviation of their mean weights. Intended for use in ecology, but its methods are general and can be applied to any bipartite network.
Author: Benno Simmons [aut, cre], Michelle Sweering [aut], Maybritt Schillinger [aut], Riccardo Di Clemente [aut]
Maintainer: Benno Simmons <benno.simmons@gmail.com>

Diff between bmotif versions 2.0.0 dated 2019-12-11 and 2.0.1 dated 2020-07-02

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Package IndependenceTests (with last version 0.4) was removed from CRAN

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

2020-04-29 0.4
2020-04-20 0.3
2012-12-11 0.2
2011-04-04 0.1

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Package LeLogicielR (with last version 1.2.1) was removed from CRAN

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

2018-05-17 1.2.1
2012-04-21 1.2
2012-03-19 1.1
2011-04-15 1.0

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Package TRSbook (with last version 1.0.2) was removed from CRAN

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

2018-05-10 1.0.2
2014-11-05 1.0.1
2014-11-03 1.0

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Package rPACI updated to version 0.1.2 with previous version 0.1.1 dated 2019-02-11

Title: Placido Analysis of Corneal Irregularity
Description: Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk (Rowsey et al. (1981), <doi:10.1001/archopht.1981.03930011093022>, Rand et al. (1997), <doi:10.1016/S0886-3350(99)00355-7>), which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye. The rPACI package allows the calculation of the corneal irregularity indices described in Castro-Luna et al. (2020), <doi:10.1016/j.clae.2019.12.006>; Ramos-Lopez et al. (2013), <doi:10.1097/OPX.0b013e3182843f2a>; and Ramos-Lopez et al. (2011), <doi:10.1097/OPX.0b013e3182843f2a>. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician.
Author: Darío Ramos-López [aut, cre], Ana D. Maldonado [aut]
Maintainer: Darío Ramos-López <dario.ramos.lopez@urjc.es>

Diff between rPACI versions 0.1.1 dated 2019-02-11 and 0.1.2 dated 2020-07-02

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Package CSTools updated to version 3.1.0 with previous version 3.0.0 dated 2020-02-11

Title: Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales
Description: Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. This package was developed in the context of the ERA4CS project MEDSCOPE and the H2020 S2S4E project. Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>. Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>. Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>. Straus et al. (2007) <doi:10.1175/JCLI4070.1>. Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>. D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>. Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>. Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>.
Author: Nuria Perez-Zanon [aut, cre] (<https://orcid.org/0000-0001-8568-3071>), Louis-Philippe Caron [aut] (<https://orcid.org/0000-0001-5221-0147>), Carmen Alvarez-Castro [aut], Jost von Hardenberg [aut] (<https://orcid.org/0000-0002-5312-8070>), Llorenç LLedo [aut], Nicolau Manubens [aut], Eroteida Sanchez-Garcia [aut], Bert van Schaeybroeck [aut], Veronica Torralba [aut], Deborah Verfaillie [aut], Lauriane Batte [ctb], Filippo Cali Quaglia [ctb], Chihchung Chou [ctb], Nicola Cortesi [ctb], Susanna Corti [ctb], Paolo Davini [ctb], Marta Dominguez [ctb], Federico Fabiano [ctb], Ignazio Giuntoli [ctb], Raul Marcos [ctb], Niti Mishra [ctb], Jesus Peña [ctb], Francesc Roura-Adserias [ctb], Silvia Terzago [ctb], Danila Volpi [ctb], BSC-CNS [cph]
Maintainer: Nuria Perez-Zanon <nuria.perez@bsc.es>

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Package BayesSUR updated to version 1.2-0 with previous version 1.1-2 dated 2020-04-27

Title: Bayesian Seemingly Unrelated Regression
Description: Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Banterle et al. (2018) <doi:10.1101/467019>.
Author: Marco Banterle [aut], Zhi Zhao [aut, cre], Leonardo Bottolo [ctb], Sylvia Richardson [ctb], Waldir Leoncio [ctb], Alex Lewin [aut], Manuela Zucknick [ctb]
Maintainer: Zhi Zhao <zhi.zhao@medisin.uio.no>

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Package BayesianFROC updated to version 0.3.0 with previous version 0.2.3 dated 2020-04-27

Title: FROC Analysis by Bayesian Approaches
Description: Execute BayesianFROC::fit_GUI_Shiny() (or fit_GUI_Shiny_MRMC()) for a graphical user interface via Shiny. Provides new methods for the so-called Free-response Receiver Operating Characteristic (FROC) analysis. The ultimate aim of FROC analysis is to compare observer performances, which means comparing characteristics, such as area under the curve (AUC) or figure of merit (FOM). In this package, we only use the notion of AUC for modality comparison, where by "modality", we mean imaging methods such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ..., etc. So there is a problem that which imaging method is better to detect lesions from shadows in radiographs. To solve modality comparison issues, this package provides new methods using hierarchical Bayesian models proposed by the author of this package. Using this package, one can obtain at least one conclusion that which imaging methods are better for finding lesions in radiographs with the case of your data. Fitting FROC statistical models is sometimes not so good, it can easily confirm by drawing FROC curves and comparing these curves and the points constructed by False Positive fractions (FPFs) and True Positive Fractions (TPFs), we can validate the goodness of fit intuitively. Such validation is also implemented by the Chi square goodness of fit statistics in the Bayesian context which means that the parameter is not deterministic, thus by integrating it with the posterior predictive measure, we get a desired value. To compare modalities (imaging methods: MRI, CT, PET, ... , etc), we evaluate AUCs for each modality. FROC is developed by Dev Chakraborty, his FROC model in his 1989 paper relies on the maximal likelihood methodology. The author modified and provided the alternative Bayesian FROC model. Strictly speaking, his model does not coincide with models in this package. In FROC context, we means by multiple reader and multiple case (MRMC) the case of the number of reader or modality is two or more. The MRMC data is available for functions of this package. I hope that medical researchers use not only the frequentist method but also alternative Bayesian methods. In medical research, many problems are considered under only frequentist methods, such as the notion of p-values. But p-value is sometimes misunderstood. Bayesian methods provide very simple, direct, intuitive answer for research questions. Combining frequentist methods with Bayesian methods, we can obtain more reliable answer for research questions. 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: Dev Chakraborty (1989) <doi:10.1118/1.596358> Maximum likelihood analysis of free - response receiver operating characteristic (FROC) data. Pre-print: Issei Tsunoda; Bayesian Models for free-response receiver operating characteristic analysis.
Author: Issei Tsunoda [aut, cre]
Maintainer: Issei Tsunoda <tsunoda.issei1111@gmail.com>

Diff between BayesianFROC versions 0.2.3 dated 2020-04-27 and 0.3.0 dated 2020-07-02

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 BayesianFROC-0.3.0/BayesianFROC/MD5                                                                             |  238 -
 BayesianFROC-0.3.0/BayesianFROC/NEWS.md                                                                         |   65 
 BayesianFROC-0.3.0/BayesianFROC/R/AFROC.R                                                                       |   57 
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 BayesianFROC-0.3.0/BayesianFROC/R/ConfirmConvergence.R                                                          |    2 
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 BayesianFROC-0.3.0/BayesianFROC/R/DrawCurves.R                                                                  |   14 
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 BayesianFROC-0.3.0/BayesianFROC/R/chi_square_goodness_of_fit.R                                                  |   47 
 BayesianFROC-0.3.0/BayesianFROC/R/convertFromJafroc.R                                                           |    8 
 BayesianFROC-0.3.0/BayesianFROC/R/document_dataset_MRMC.R                                                       |    2 
 BayesianFROC-0.3.0/BayesianFROC/R/download.R                                                                    |only
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 BayesianFROC-0.3.0/BayesianFROC/R/extract_EAP_by_array.R                                                        |    4 
 BayesianFROC-0.3.0/BayesianFROC/R/extract_data_frame_from_dataList_MRMC.R                                       |    4 
 BayesianFROC-0.3.0/BayesianFROC/R/fffaaabbb.R                                                                   |   48 
 BayesianFROC-0.3.0/BayesianFROC/R/fit_Bayesian_FROC.R                                                           |  158 
 BayesianFROC-0.3.0/BayesianFROC/R/fit_GUI_Shiny.R                                                               | 1643 +++++-----
 BayesianFROC-0.3.0/BayesianFROC/R/fit_MRMC_versionTWO.R                                                         |  164 
 BayesianFROC-0.3.0/BayesianFROC/R/get_posterior_variance.R                                                      |   22 
 BayesianFROC-0.3.0/BayesianFROC/R/metadata.R                                                                    |    9 
 BayesianFROC-0.3.0/BayesianFROC/R/methods.R                                                                     |    2 
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 BayesianFROC-0.3.0/BayesianFROC/R/plotFROC.R                                                                    |  126 
 BayesianFROC-0.3.0/BayesianFROC/R/ppp.R                                                                         |    2 
 BayesianFROC-0.3.0/BayesianFROC/R/snippet_for_BayesianFROC.R                                                    |  150 
 BayesianFROC-0.3.0/BayesianFROC/R/stanfitExtended.R                                                             |   28 
 BayesianFROC-0.3.0/BayesianFROC/R/summary_EAP_CI_srsc.R                                                         |   88 
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 BayesianFROC-0.3.0/BayesianFROC/R/validation_error_srsc.R                                                       |   16 
 BayesianFROC-0.3.0/BayesianFROC/README.md                                                                       |  104 
 BayesianFROC-0.3.0/BayesianFROC/build/vignette.rds                                                              |binary
 BayesianFROC-0.3.0/BayesianFROC/demo/00Index                                                                    |    4 
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 BayesianFROC-0.3.0/BayesianFROC/inst/CITATION                                                                   |    8 
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 BayesianFROC-0.3.0/BayesianFROC/man/AFROC.Rd                                                                    |   51 
 BayesianFROC-0.3.0/BayesianFROC/man/Author_vs_classic_for_AUC.Rd                                                |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/BayesianFROC.Rd                                                             |  422 ++
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 BayesianFROC-0.3.0/BayesianFROC/man/ConfirmConvergence.Rd                                                       |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/CoronaVirus_Disease_2019.Rd                                                 |    9 
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 BayesianFROC-0.3.0/BayesianFROC/man/DrawCurves.Rd                                                               |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/DrawCurves_MRMC.Rd                                                          |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/DrawCurves_MRMC_pairwise.Rd                                                 |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/DrawCurves_MRMC_pairwise_BlackWhite.Rd                                      |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/DrawCurves_MRMC_pairwise_col.Rd                                             |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/DrawCurves_srsc.Rd                                                          |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/Draw_a_simulated_data_set.Rd                                                |    4 
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 BayesianFROC-0.3.0/BayesianFROC/man/Draw_an_area_of_AUC_for_srsc.Rd                                             |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/Empirical_FROC_via_ggplot.Rd                                                |    4 
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 BayesianFROC-0.3.0/BayesianFROC/man/check_hit_is_less_than_NL.Rd                                                |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/check_rhat.Rd                                                               |    2 
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 BayesianFROC-0.3.0/BayesianFROC/man/chi_square_goodness_of_fit.Rd                                               |    4 
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 BayesianFROC-0.3.0/BayesianFROC/man/comparison.Rd                                                               |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/convertFromJafroc.Rd                                                        |    8 
 BayesianFROC-0.3.0/BayesianFROC/man/create_dataList_MRMC.Rd                                                     |   34 
 BayesianFROC-0.3.0/BayesianFROC/man/dataList.one.modality.Rd                                                    |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/draw.CFP.CTP.from.dataList.Rd                                               |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/draw_latent_noise_distribution.Rd                                           |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/draw_latent_signal_distribution.Rd                                          |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/error_MRMC.Rd                                                               |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/error_message_on_imaging_device_rhat_values.Rd                              |    5 
 BayesianFROC-0.3.0/BayesianFROC/man/error_srsc.Rd                                                               |   10 
 BayesianFROC-0.3.0/BayesianFROC/man/extractAUC.Rd                                                               |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/extract_EAP_CI.Rd                                                           |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/extract_EAP_by_array.Rd                                                     |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/extract_data_frame_from_dataList_MRMC.Rd                                    |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/extract_estimates_MRMC.Rd                                                   |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/extract_parameters_from_replicated_models.Rd                                |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/fit_Bayesian_FROC.Rd                                                        |   30 
 BayesianFROC-0.3.0/BayesianFROC/man/fit_GUI_Shiny.Rd                                                            |    6 
 BayesianFROC-0.3.0/BayesianFROC/man/fit_MRMC.Rd                                                                 |   16 
 BayesianFROC-0.3.0/BayesianFROC/man/fit_MRMC_versionTWO.Rd                                                      |   26 
 BayesianFROC-0.3.0/BayesianFROC/man/fit_Null_hypothesis_model_to_.Rd                                            |   12 
 BayesianFROC-0.3.0/BayesianFROC/man/fit_a_model_to.Rd                                                           |  104 
 BayesianFROC-0.3.0/BayesianFROC/man/fit_srsc.Rd                                                                 |   15 
 BayesianFROC-0.3.0/BayesianFROC/man/get_posterior_variance.Rd                                                   |   24 
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 BayesianFROC-0.3.0/BayesianFROC/man/ggplotFROC.EAP.Rd                                                           |    6 
 BayesianFROC-0.3.0/BayesianFROC/man/ggplotFROC.Rd                                                               |    6 
 BayesianFROC-0.3.0/BayesianFROC/man/give_name_srsc_data.Rd                                                      |    4 
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 BayesianFROC-0.3.0/BayesianFROC/man/make_true_parameter_MRMC.Rd                                                 |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/metadata_to_DrawCurve_MRMC.Rd                                               |    2 
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 BayesianFROC-0.3.0/BayesianFROC/man/plot-stanfitExtended-missing-method.Rd                                      |   42 
 BayesianFROC-0.3.0/BayesianFROC/man/plot_FPF_and_TPF_from_a_dataset.Rd                                          |    4 
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 BayesianFROC-0.3.0/BayesianFROC/man/ppp.Rd                                                                      |    4 
 BayesianFROC-0.3.0/BayesianFROC/man/ppp_MRMC.Rd                                                                 |    2 
 BayesianFROC-0.3.0/BayesianFROC/man/ppp_srsc.Rd                                                                 |    2 
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 BayesianFROC-0.3.0/BayesianFROC/man/snippet_for_BayesianFROC.Rd                                                 |   11 
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Package nomclust updated to version 2.1.6 with previous version 2.1.5 dated 2020-06-22

Title: Hierarchical Cluster Analysis of Nominal Data
Description: Similarity measures for hierarchical clustering of objects characterized by nominal (categorical) variables. Evaluation criteria for nominal data clustering.
Author: Zdenek Sulc [aut, cre], Jana Cibulkova [aut], Hana Rezankova [aut]
Maintainer: Zdenek Sulc <zdenek.sulc@vse.cz>

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Package SLOPE updated to version 0.3.0 with previous version 0.2.1 dated 2020-04-16

Title: Sorted L1 Penalized Estimation
Description: Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm (Bogdan et al. (2015) <doi:10/gfgwzt>). Supported models include ordinary least-squares regression, binomial regression, multinomial regression, and Poisson regression. Both dense and sparse predictor matrices are supported. In addition, the package features predictor screening rules that enable fast and efficient solutions to high-dimensional problems.
Author: Johan Larsson [aut, cre] (<https://orcid.org/0000-0002-4029-5945>), Jonas Wallin [aut] (<https://orcid.org/0000-0003-0381-6593>), Malgorzata Bogdan [aut], Ewout van den Berg [aut], Chiara Sabatti [aut], Emmanuel Candes [aut], Evan Patterson [aut], Weijie Su [aut], Jerome Friedman [ctb] (code adapted from 'glmnet'), Trevor Hastie [ctb] (code adapted from 'glmnet'), Rob Tibshirani [ctb] (code adapted from 'glmnet'), Balasubramanian Narasimhan [ctb] (code adapted from 'glmnet'), Noah Simon [ctb] (code adapted from 'glmnet'), Junyang Qian [ctb] (code adapted from 'glmnet')
Maintainer: Johan Larsson <johan.larsson@stat.lu.se>

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Package SITH updated to version 1.0.1 with previous version 1.0.0 dated 2020-07-01

Title: A Spatial Model of Intra-Tumor Heterogeneity
Description: Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) <doi:10.1038/nature14971>. Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor.
Author: Phillip B. Nicol
Maintainer: Phillip B. Nicol <philnicol740@gmail.com>

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Package scDIFtest updated to version 0.1.1 with previous version 0.1.0 dated 2020-06-26

Title: Item-Wise Score-Based DIF Detection
Description: Detection of item-wise Differential Item Functioning (DIF) in fitted 'mirt', 'multipleGroup' or 'bfactor' models using score-based structural change tests. Under the hood the sctest() function from the 'strucchange' package is used.
Author: Dries Debeer [cre, aut]
Maintainer: Dries Debeer <debeer.dries@gmail.com>

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Package sand updated to version 2.0.0 with previous version 1.0.3 dated 2017-03-02

Title: Statistical Analysis of Network Data with R, 2nd Edition
Description: Data sets and code blocks for the book 'Statistical Analysis of Network Data with R, 2nd Edition'.
Author: Eric Kolaczyk [aut, cre], Gábor Csárdi [aut], Carolyn Kolaczyk [ctb]
Maintainer: Eric Kolaczyk <eric.kolaczyk@gmail.com>

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New package OpVaR with initial version 1.1.1
Package: OpVaR
Type: Package
Title: Statistical Methods for Modelling Operational Risk
Version: 1.1.1
Date: 2020-07-02
Author: Christina Zou [aut,cre], Marius Pfeuffer [aut], Matthias Fischer [aut], Kristina Dehler [ctb], Nicole Derfuss [ctb], Benedikt Graswald [ctb], Linda Moestel [ctb], Jixuan Wang [ctb], Leonie Wicht [ctb]
Maintainer: Christina Zou <christina.zou@maths.ox.ac.uk>
Description: Functions for computing the value-at-risk in compound Poisson models. The implementation comprises functions for modeling loss frequencies and loss severities with plain, mixed (Frigessi et al. (2012) <doi:10.1023/A:1024072610684>) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (Ergashev et al. (2013) <doi:10.21314/JOP.2013.131>). In particular, the parametrization of tail distributions includes the fitting of Tukey-type distributions (Kuo and Headrick (2014) <doi:10.1155/2014/645823>). Furthermore, the package contains the modeling of bivariate dependencies between loss severities and frequencies, Monte Carlo simulation for total loss estimation as well as a closed-form approximation based on Degen (2010) <doi:10.21314/JOP.2010.084> to determine the value-at-risk.
License: GPL-3
Imports: VineCopula, tea, actuar, truncnorm, ReIns, MASS, pracma, evmix
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-07-01 23:07:57 UTC; zizzy
Repository: CRAN
Date/Publication: 2020-07-02 07:30:03 UTC

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Package hydraulics updated to version 0.2.0 with previous version 0.1.0 dated 2020-05-18

Title: Basic Pipe and Open Channel Hydraulics
Description: Functions for basic hydraulic calculations related to water flow in circular pipes both flowing full (under pressure), and partially full (gravity flow), and trapezoidal open channels. For pressure flow this includes friction loss calculations by solving the Darcy-Weisbach equation for head loss, flow or diameter, and plotting a Moody diagram. The Darcy-Weisbach friction factor is calculated using the Colebrook (or Colebrook-White equation), the basis of the Moody diagram, the original citation being Colebrook (1939) <doi:10.1680/ijoti.1939.13150>. For gravity flow, the Manning equation is used, again solving for missing parameters. The derivation of and solutions using the Darcy-Weisbach equation and the Manning equation are outlined in many fluid mechanics texts such as Finnemore and Franzini (2002, ISBN:978-0072432022). For the Manning equation solutions, this package uses modifications of original code from the 'iemisc' package by Irucka Embry.
Author: Ed Maurer [aut, cre], Irucka Embry [aut, ctb] (iemisc code)
Maintainer: Ed Maurer <emaurer@scu.edu>

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Package graphsim updated to version 1.0.0 with previous version 0.1.2 dated 2020-03-16

Title: Simulate Expression Data from 'igraph' Networks
Description: Functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph' structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.
Author: S. Thomas Kelly [ctb], S. Thomas Kelly [aut, cre], Michael A. Black [aut, ths]
Maintainer: S. Thomas Kelly <tom.kelly@riken.jp>

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Package filling updated to version 0.2.2 with previous version 0.2.1 dated 2020-02-04

Title: Matrix Completion, Imputation, and Inpainting Methods
Description: Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.
Author: Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>)
Maintainer: Kisung You <kyoustat@gmail.com>

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Package DMCfun updated to version 0.12.1 with previous version 0.12.0 dated 2020-07-01

Title: Diffusion Model of Conflict (DMC) in Reaction Time Tasks
Description: DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>.
Author: Mackenzie G. Ian [cre, aut]
Maintainer: Mackenzie G. Ian <ian.mackenzie@uni-tuebingen.de>

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Package ijtiff updated to version 2.1.0 with previous version 2.0.5 dated 2020-04-06

Title: Comprehensive TIFF I/O with Full Support for 'ImageJ' TIFF Files
Description: General purpose TIFF file I/O for R users. Currently the only such package with read and write support for TIFF files with floating point (real-numbered) pixels, and the only package that can correctly import TIFF files that were saved from 'ImageJ' and write TIFF files than can be correctly read by 'ImageJ' <https://imagej.nih.gov/ij/>. Also supports text image I/O.
Author: Rory Nolan [aut, cre] (<https://orcid.org/0000-0002-5239-4043>), Kent Johnson [aut], Simon Urbanek [ctb], Sergi Padilla-Parra [ths] (<https://orcid.org/0000-0002-8010-9481>), Jeroen Ooms [rev, ctb] (<https://orcid.org/0000-0002-4035-0289>), Jon Clayden [rev] (<https://orcid.org/0000-0002-6608-0619>)
Maintainer: Rory Nolan <rorynoolan@gmail.com>

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Package fulltext updated to version 1.5.0 with previous version 1.4.0 dated 2019-12-13

Title: Full Text of 'Scholarly' Articles Across Many Data Sources
Description: Provides a single interface to many sources of full text 'scholarly' data, including 'Biomed Central', Public Library of Science, 'Pubmed Central', 'eLife', 'F1000Research', 'PeerJ', 'Pensoft', 'Hindawi', 'arXiv' 'preprints', and more. Functionality included for searching for articles, downloading full or partial text, downloading supplementary materials, converting to various data formats.
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>), rOpenSci [fnd] (https://ropensci.org/)
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

Diff between fulltext versions 1.4.0 dated 2019-12-13 and 1.5.0 dated 2020-07-02

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Package credentials updated to version 1.2.1 with previous version 1.2.0 dated 2020-06-25

Title: Tools for Managing SSH and Git Credentials
Description: Setup and retrieve HTTPS and SSH credentials for use with 'git' and other services. For HTTPS remotes the package interfaces the 'git-credential' utility which 'git' uses to store HTTP usernames and passwords. For SSH remotes we provide convenient functions to find or generate appropriate SSH keys. The package both helps the user to setup a local git installation, and also provides a back-end for git/ssh client libraries to authenticate with existing user credentials.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between credentials versions 1.2.0 dated 2020-06-25 and 1.2.1 dated 2020-07-02

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Package ballr (with last version 0.2.6) was removed from CRAN

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

2020-01-19 0.2.6
2020-01-10 0.2.5
2019-10-22 0.2.4
2018-11-01 0.2.3
2018-10-19 0.2.2
2018-07-16 0.2.1
2018-03-26 0.2.0
2017-11-27 0.1.1

Permanent link
Package SubTite updated to version 3.0.2 with previous version 3.0.1 dated 2020-02-12

Title: Subgroup Specific Optimal Dose Assignment
Description: Chooses subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulates clinical trials under the subgroup specific time to event continual reassessment method. Chapple, A.G., Thall, P.F. (2018) <doi:10.1002/pst.1891>.
Author: Andrew Chapple
Maintainer: Andrew Chapple <achapp@lsuhsc.edu>

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Package riverdist updated to version 0.15.3 with previous version 0.15.2 dated 2020-06-02

Title: River Network Distance Computation and Applications
Description: Reads river network shape files and computes network distances. Also included are a variety of computation and graphical tools designed for fisheries telemetry research, such as minimum home range, kernel density estimation, and clustering analysis using empirical k-functions with a bootstrap envelope. Tools are also provided for editing the river networks, meaning there is no reliance on external software.
Author: Matt Tyers [aut, cre]
Maintainer: Matt Tyers <matt.tyers@alaska.gov>

Diff between riverdist versions 0.15.2 dated 2020-06-02 and 0.15.3 dated 2020-07-02

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Package naijR updated to version 0.1.2 with previous version 0.1.0 dated 2020-05-21

Title: Operations to Ease Data Analyses Specific to Nigeria
Description: A set of convenience functions as well as geographical/political data about Nigeria, aimed at simplifying work with data and information that are specific to the country.
Author: Victor Ordu [aut, cre] (<https://orcid.org/0000-0003-3716-0668>)
Maintainer: Victor Ordu <victorordu@outlook.com>

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Package idiogramFISH updated to version 1.15.3 with previous version 1.15.1 dated 2020-06-03

Title: Idiograms with Marks and Karyotype Indices
Description: Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids (when not circular). Supports micrometers, cM and Mb or any unit. Two styles of centromeres are available: triangular and rounded; and seven styles of marks: square, dots, cM, cMLeft, cenStyle, upArrow, downArrow; its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x>, karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).
Author: Fernando Roa [aut, cre], Mariana PC Telles [ctb]
Maintainer: Fernando Roa <froao@unal.edu.co>

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