Sat, 16 Oct 2021

Package usethis updated to version 2.1.0 with previous version 2.0.1 dated 2021-02-10

Title: Automate Package and Project Setup
Description: Automate package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp', 'RStudio' projects, and more.
Author: Hadley Wickham [aut] (<https://orcid.org/0000-0003-4757-117X>), Jennifer Bryan [aut, cre] (<https://orcid.org/0000-0002-6983-2759>), Malcolm Barrett [aut] (<https://orcid.org/0000-0003-0299-5825>), RStudio [cph, fnd]
Maintainer: Jennifer Bryan <jenny@rstudio.com>

Diff between usethis versions 2.0.1 dated 2021-02-10 and 2.1.0 dated 2021-10-16

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More information about usethis at CRAN
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Package rvest updated to version 1.0.2 with previous version 1.0.1 dated 2021-07-26

Title: Easily Harvest (Scrape) Web Pages
Description: Wrappers around the 'xml2' and 'httr' packages to make it easy to download, then manipulate, HTML and XML.
Author: Hadley Wickham [aut, cre], RStudio [cph]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between rvest versions 1.0.1 dated 2021-07-26 and 1.0.2 dated 2021-10-16

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More information about rvest at CRAN
Permanent link

Package naturaList updated to version 0.4.2 with previous version 0.4.1 dated 2021-08-21

Title: Classify Occurrences by Confidence Levels in the Species ID
Description: Classify occurrence records based on confidence levels of species identification. In addition, implement tools to filter occurrence inside grid cells and to manually check for possibles errors with an interactive shiny application.
Author: Arthur Vinicius Rodrigues [cre], Gabriel Nakamura [aut], Leandro Duarte [aut]
Maintainer: Arthur Vinicius Rodrigues <rodrigues.arthur.v@gmail.com>

Diff between naturaList versions 0.4.1 dated 2021-08-21 and 0.4.2 dated 2021-10-16

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Package mvGPS updated to version 1.2.1 with previous version 1.1.1 dated 2021-04-28

Title: Causal Inference using Multivariate Generalized Propensity Score
Description: Methods for estimating and utilizing the multivariate generalized propensity score (mvGPS) for multiple continuous exposures described in Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow estimation of a dose-response surface relating the joint distribution of multiple continuous exposure variables to an outcome. Weights are constructed assuming a multivariate normal density for the marginal and conditional distribution of exposures given a set of confounders. Confounders can be different for different exposure variables. The weights are designed to achieve balance across all exposure dimensions and can be used to estimate dose-response surfaces.
Author: Justin Williams [aut, cre] (<https://orcid.org/0000-0002-5045-2764>)
Maintainer: Justin Williams <williazo@ucla.edu>

Diff between mvGPS versions 1.1.1 dated 2021-04-28 and 1.2.1 dated 2021-10-16

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More information about mvGPS at CRAN
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New package logib with initial version 0.1.1
Package: logib
Title: Salary Analysis by the Swiss Federal Office for Gender Equality
Version: 0.1.1
Description: Implementation of the Swiss Confederation's standard analysis model for salary analyses <https://www.ebg.admin.ch/dam/ebg/en/dokumente/lohngleichheit/infos-zu-analysen/standard-analysemodellzurueberpruefungderlohngleichheitzwischenf.pdf.download.pdf/methodological_approachformonitoringcompliancewithwageequalitybe.pdf> in R. The analysis is run at company-level and the model is intended for companies with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis. This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
Imports: lubridate (>= 1.7.9), readxl (>= 1.3.1)
Suggests: testthat
NeedsCompilation: no
Packaged: 2021-10-16 15:04:15 UTC; 09607938
Author: Jonathan Chassot [aut, cre], Jeremy Kolly [ctb], Federal Office for Gender Equality of Switzerland [cph, fnd]
Maintainer: Jonathan Chassot <jonathanlaurentdominique.chassot@ebg.admin.ch>
Repository: CRAN
Date/Publication: 2021-10-16 23:30:13 UTC

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Package decisionSupport updated to version 1.108 with previous version 1.107 dated 2021-10-16

Title: Quantitative Support of Decision Making under Uncertainty
Description: Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.
Author: Eike Luedeling [cre, aut] (University of Bonn), Lutz Goehring [aut] (ICRAF and Lutz Goehring Consulting), Katja Schiffers [aut] (University of Bonn), Cory Whitney [aut] (University of Bonn), Eduardo Fernandez [aut] (University of Bonn)
Maintainer: Eike Luedeling <eike@eikeluedeling.com>

Diff between decisionSupport versions 1.107 dated 2021-10-16 and 1.108 dated 2021-10-16

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Package csppData updated to version 0.1.4 with previous version 0.1.3 dated 2021-06-07

Title: Data Only: The Correlates of State Policy Project Dataset
Description: Contains the Correlates of State Policy Project dataset (+ codebook) assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy> used by the 'cspp' package. The Correlates data contains over 2000 variables across more than 100 years that pertain to state politics and policy in the United States.
Author: Caleb Lucas (https://caleblucas.com/) and Joshua McCrain (http://joshuamccrain.com/)
Maintainer: Caleb Lucas <calebjlucas@gmail.com>

Diff between csppData versions 0.1.3 dated 2021-06-07 and 0.1.4 dated 2021-10-16

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More information about csppData at CRAN
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Package cspp updated to version 0.3.2 with previous version 0.3.1 dated 2021-01-04

Title: A Tool for the Correlates of State Policy Project Data
Description: A tool that imports, subsets, visualizes, and exports the Correlates of State Policy Project dataset assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy>. The Correlates data contains over 2000 variables across more than 100 years that pertain to state politics and policy in the United States. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, create map visualizations, export the citations associated with their searches, and more.
Author: Caleb Lucas (https://caleblucas.com/) and Joshua McCrain (http://joshuamccrain.com/)
Maintainer: Caleb Lucas <calebjlucas@gmail.com>

Diff between cspp versions 0.3.1 dated 2021-01-04 and 0.3.2 dated 2021-10-16

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More information about cspp at CRAN
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New package basket with initial version 0.10.11
Package: basket
Title: Basket Trial Analysis
Version: 0.10.11
Description: Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring. The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability. References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) <doi:10.1056/NEJMoa1502309>; Hobbs, B.P. and Landin, R. (2018) <doi:10.1002/sim.7893>; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) <doi:10.1093/annonc/mdy457>; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) <doi:10.1093/biostatistics/kxx031>.
Depends: R (>= 3.5.0)
License: LGPL-2
Maintainer: Michael J. Kane <michael.kane@yale.edu>
Imports: GenSA, foreach, ggplot2, stats, tibble, tidyr, dplyr, igraph, gridExtra, itertools, crayon, cli, RColorBrewer, tidygraph, ggraph,
LazyData: true
Encoding: UTF-8
URL: https://github.com/kaneplusplus/basket
BugReports: https://github.com/kaneplusplus/basket/issues
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), doParallel
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-10-14 19:42:41 UTC; mike
Author: Nan Chen [aut], Ryan Hafen [ctb], Brian Hobbs [aut], Alex Kaizer [aut], Michael J. Kane [aut, cre] (<https://orcid.org/0000-0003-1899-6662>)
Repository: CRAN
Date/Publication: 2021-10-16 23:30:22 UTC

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Package disordR updated to version 0.0-8 with previous version 0.0-7 dated 2021-08-05

Title: Non-Ordered Vectors
Description: Functionality for manipulating values of associative maps. Ordinary R vectors are unsuitable for working with values of associative maps because elements of an R vector may be accessed by reference to their location in the vector, but associative maps are stored in arbitrary order. However, when associating keys with values one needs both parts to be in 1-1 correspondence, so one cannot dispense with the order entirely. The 'disordR' package includes a single S4 class, disord. This class allows one to perform only those operations appropriate for manipulating values of associative maps and prevents any other operation (such as accessing an element at a particular location). A useful heuristic is that one is only allowed to access or modify a disord object using a python list comprehension. The idea is to prevent ill-defined operations on values (or keys) of associative maps, whose order is undefined or at best implementation-specific, while allowing and facilitating sensible operations. The package is needed for development versions of 'mvp', 'hyper2', 'spray', 'clifford', and 'freealg'.
Author: Robin K. S. Hankin [aut, cre] (<https://orcid.org/0000-0001-5982-0415>)
Maintainer: Robin K. S. Hankin <hankin.robin@gmail.com>

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Package rts updated to version 1.1-3 with previous version 1.0-49 dated 2019-02-03

Title: Raster Time Series Analysis
Description: This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
Author: Babak Naimi
Maintainer: Babak Naimi <naimi.b@gmail.com>

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Package starvz updated to version 0.6.0 with previous version 0.5.0 dated 2021-03-25

Title: R-Based Visualization Techniques for Task-Based Applications
Description: Performance analysis workflow that combines the power of the R language (and the tidyverse realm) and many auxiliary tools to provide a consistent, flexible, extensible, fast, and versatile framework for the performance analysis of task-based applications that run on top of the StarPU runtime (with its MPI (Message Passing Interface) layer for multi-node support). Its goal is to provide a fruitful prototypical environment to conduct performance analysis hypothesis-checking for task-based applications that run on heterogeneous (multi-GPU, multi-core) multi-node HPC (High-performance computing) platforms.
Author: Lucas Mello Schnorr [aut, ths] (<https://orcid.org/0000-0003-4828-9942>), Vinicius Garcia Pinto [aut] (<https://orcid.org/0000-0002-6845-9358>), Lucas Leandro Nesi [aut, cre] (<https://orcid.org/0000-0001-8874-1839>), Marcelo Cogo Miletto [aut] (<https://orcid.org/0000-0002-1191-3863>), Guilherme Alles [ctb], Arnaud Legrand [ctb], Luka Stanisic [ctb], Rémy Drouilhet [ctb]
Maintainer: Lucas Leandro Nesi <lucas.nesi@inf.ufrgs.br>

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Package scMappR updated to version 1.0.7 with previous version 1.0.2 dated 2021-05-14

Title: Single Cell Mapper
Description: The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. <doi:10.1093/nargab/lqab011>.
Author: Dustin Sokolowski [aut, cre], Mariela Faykoo-Martinez [aut], Lauren Erdman [aut], Houyun Hou [aut], Cadia Chan [aut], Helen Zhu [aut], Melissa Holmes [aut], Anna Goldenberg [aut], Michael Wilson [aut]
Maintainer: Dustin Sokolowski <dustin.sokolowski@sickkids.ca>

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Package mixmeta updated to version 1.2.0 with previous version 1.1.3 dated 2021-06-12

Title: An Extended Mixed-Effects Framework for Meta-Analysis
Description: A collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models.
Author: Antonio Gasparrini [aut, cre], Francesco Sera [aut]
Maintainer: Antonio Gasparrini <antonio.gasparrini@lshtm.ac.uk>

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Package MBESS updated to version 4.8.1 with previous version 4.8.0 dated 2020-08-05

Title: The MBESS R Package
Description: Implements methods that are useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became more general and now contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from others, see <https://www3.nd.edu/~kkelley/site/MBESS.html> for a detailed list of those that have contributed and other details.
Author: Ken Kelley [aut, cre]
Maintainer: Ken Kelley <kkelley@nd.edu>

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Package legco updated to version 0.1.4 with previous version 0.1.3 dated 2020-03-31

Title: R Bindings for the Hong Kong Legislative Council API
Description: Fetching data from Hong Kong Legislative Council's open data API in R. Functions correspond to the data endpoints of the API. Documentations of supported API databases: <https://www.legco.gov.hk/odata/english/billsdb.html>, <https://www.legco.gov.hk/odata/english/hansard-db.html>, <https://www.legco.gov.hk/odata/english/attendance-db.html>, <https://www.legco.gov.hk/odata/english/schedule-db.html> and <https://www.legco.gov.hk/odata/english/vrdb.html>.
Author: Elgar Teo
Maintainer: Elgar Teo <elgarteo@connect.hku.hk>

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Package insight updated to version 0.14.5 with previous version 0.14.4 dated 2021-09-02

Title: Easy Access to Model Information for Various Model Objects
Description: A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.
Author: Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>, @strengejacke), Dominique Makowski [aut, ctb] (<https://orcid.org/0000-0001-5375-9967>, @Dom_Makowski), Indrajeet Patil [aut, ctb] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Philip Waggoner [aut, ctb] (<https://orcid.org/0000-0002-7825-7573>), Mattan S. Ben-Shachar [aut, ctb] (<https://orcid.org/0000-0002-4287-4801>), Brenton M. Wiernik [aut] (<https://orcid.org/0000-0001-9560-6336>, @bmwiernik), Vincent Arel-Bundock [ctb] (<https://orcid.org/0000-0003-2042-7063>), Alex Hayes [rev] (<https://orcid.org/0000-0002-4985-5160>)
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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Package ForestFit updated to version 1.2.3 with previous version 0.7.1 dated 2021-02-06

Title: Statistical Modelling for Plant Size Distributions
Description: Developed for the following tasks. 1 ) Computing the probability density function, cumulative distribution function, random generation, and estimating the parameters of the eleven mixture models. 2 ) Point estimation of the parameters of two - parameter Weibull distribution using twelve methods and three - parameter Weibull distribution using nine methods. 3 ) The Bayesian inference for the three - parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to grouped data using three methods including approximated maximum likelihood, expectation maximization, and maximum likelihood. 5 ) Estimating the parameters of the gamma, log-normal, and Weibull mixture models fitted to the grouped data through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve fitted to the height - diameter observation, 7 ) Estimating parameters, computing probability density function, cumulative distribution function, and generating realizations from gamma shape mixture model introduced by Venturini et al. (2008) <doi:10.1214/07-AOAS156> , 8 ) The Bayesian inference, computing probability density function, cumulative distribution function, and generating realizations from four-parameter Johnson SB distribution, 9 ) Robust multiple linear regression analysis when error term follows skewed t distribution, 10 ) Estimating parameters of a given distribution fitted to grouped data using method of maximum likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through the Bayesian, method of moment, conditional maximum likelihood, and two - percentile method.
Author: Mahdi Teimouri
Maintainer: Mahdi Teimouri <teimouri@aut.ac.ir>

Diff between ForestFit versions 0.7.1 dated 2021-02-06 and 1.2.3 dated 2021-10-16

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Package decisionSupport updated to version 1.107 with previous version 1.106 dated 2021-03-01

Title: Quantitative Support of Decision Making under Uncertainty
Description: Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.
Author: Eike Luedeling [cre, aut] (University of Bonn), Lutz Goehring [aut] (ICRAF and Lutz Goehring Consulting), Katja Schiffers [aut] (University of Bonn), Cory Whitney [aut] (University of Bonn), Eduardo Fernandez [aut] (University of Bonn)
Maintainer: Eike Luedeling <eike@eikeluedeling.com>

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Package CensSpatial updated to version 2.59 with previous version 2.58 dated 2020-05-04

Title: Censored Spatial Models
Description: It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) <doi:10.1016/j.spasta.2017.12.001>.
Author: Alejandro Ordonez, Christian E. Galarza, Victor H. Lachos
Maintainer: Alejandro Ordonez <ordonezjosealejandro@gmail.com>

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Package nfl4th updated to version 1.0.1 with previous version 1.0.0 dated 2021-03-17

Title: Functions to Calculate Optimal Fourth Down Decisions in the National Football League
Description: A set of functions to estimate outcomes of fourth down plays in the National Football League and obtain fourth down plays from <https://www.nfl.com/> and <https://www.espn.com/>.
Author: Ben Baldwin [aut, cre, cph], Sebastian Carl [ctb]
Maintainer: Ben Baldwin <bbaldwin206@gmail.com>

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Package gtsummary updated to version 1.5.0 with previous version 1.4.2 dated 2021-07-13

Title: Presentation-Ready Data Summary and Analytic Result Tables
Description: Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.
Author: Daniel D. Sjoberg [aut, cre] (<https://orcid.org/0000-0003-0862-2018>), Michael Curry [aut] (<https://orcid.org/0000-0002-0261-4044>), Joseph Larmarange [aut] (<https://orcid.org/0000-0001-7097-700X>), Jessica Lavery [aut] (<https://orcid.org/0000-0002-2746-5647>), Karissa Whiting [aut] (<https://orcid.org/0000-0002-4683-1868>), Emily C. Zabor [aut] (<https://orcid.org/0000-0002-1402-4498>), Xing Bai [ctb], Esther Drill [ctb] (<https://orcid.org/0000-0002-3315-4538>), Jessica Flynn [ctb] (<https://orcid.org/0000-0001-8310-6684>), Margie Hannum [ctb] (<https://orcid.org/0000-0002-2953-0449>), Stephanie Lobaugh [ctb], Gustavo Zapata Wainberg [ctb] (<https://orcid.org/0000-0002-2524-3637>)
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

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Package soundgen updated to version 2.4.0 with previous version 2.3.0 dated 2021-09-06

Title: Sound Synthesis and Acoustic Analysis
Description: Performs parametric synthesis of sounds with harmonic and noise components such as animal vocalizations or human voice. Also offers tools for audio manipulation and acoustic analysis, including pitch tracking, spectral analysis, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for synthesizing and annotating audio, manually correcting pitch contours, and measuring formant frequencies. Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.
Author: Andrey Anikin [aut, cre]
Maintainer: Andrey Anikin <rty.anik@rambler.ru>

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Package carat updated to version 2.0.2 with previous version 2.0.1 dated 2021-10-02

Title: Covariate-Adaptive Randomization for Clinical Trials
Description: Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria.
Author: Fuyi Tu [aut], Xiaoqing Ye [aut, cre], Wei Ma [aut, ths], Feifang Hu [aut, ths]
Maintainer: Xiaoqing Ye <ye_xiaoq@163.com>

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Package rEDM updated to version 1.9.3 with previous version 1.9.2 dated 2021-09-16

Title: Empirical Dynamic Modeling ('EDM')
Description: An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library. It implements the 'simplex' projection method from Sugihara & May (1990) <doi:10.1038/344734a0>, the 'S-map' algorithm from Sugihara (1994) <doi:10.1098/rsta.1994.0106>, convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, 'multiview embedding' described in Ye & Sugihara (2016) <doi:10.1126/science.aag0863>.
Author: Joseph Park [aut, cre] (<https://orcid.org/0000-0001-5411-1409>), Cameron Smith [aut] (<https://orcid.org/0000-0003-0020-5607>), George Sugihara [aut, ccp] (<https://orcid.org/0000-0002-2863-6946>), Ethan Deyle [aut] (<https://orcid.org/0000-0001-8704-8434>), Erik Saberski [ctb] (<https://orcid.org/0000-0002-6475-6187>), Hao Ye [ctb] (<https://orcid.org/0000-0002-8630-1458>), The Regents of the University of California [cph]
Maintainer: Joseph Park <JosephPark@IEEE.org>

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Package seriation updated to version 1.3.1 with previous version 1.3.0 dated 2021-06-30

Title: Infrastructure for Ordering Objects Using Seriation
Description: Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.
Author: Michael Hahsler [aut, cre, cph], Christian Buchta [aut, cph], Kurt Hornik [aut, cph], Fionn Murtagh [ctb, cph], Michael Brusco [ctb, cph], Stephanie Stahl [ctb, cph], Hans-Friedrich Koehn [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

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Package RaSEn updated to version 3.0.0 with previous version 2.2.0 dated 2021-08-19

Title: Random Subspace Ensemble Classification and Variable Screening
Description: We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems.
Author: Ye Tian [aut, cre] and Yang Feng [aut]
Maintainer: Ye Tian <ye.t@columbia.edu>

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Package r2dii.plot updated to version 0.2.0 with previous version 0.1.0 dated 2021-06-29

Title: Visualize the Climate Scenario Alignment of a Financial Portfolio
Description: Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the 'PACTA' methodology (Paris Agreement Capital Transition Assessment; <https://2degrees-investing.org/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals.
Author: Monika Furdyna [aut, ctr, cre] (<https://orcid.org/0000-0002-3728-0646>), Mauro Lepore [aut, ctr] (<https://orcid.org/0000-0002-1986-7988>), Alex Axthelm [aut, ctr] (<https://orcid.org/0000-0001-8579-8565>), 2 Degrees Investing Initiative [cph, fnd]
Maintainer: Monika Furdyna <monika.furdyna@gmail.com>

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Package fdth updated to version 1.2-6 with previous version 1.2-5 dated 2020-04-17

Title: Frequency Distribution Tables, Histograms and Polygons
Description: Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.
Author: Jos<e9> Cl<e1>udio Faria, Enio G. Jelihovschi and Ivan B. Allaman
Maintainer: Jos<e9> Cl<e1>udio Faria <joseclaudio.faria@gmail.com>

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Package evalITR updated to version 0.2.2 with previous version 0.2.1 dated 2021-07-07

Title: Evaluating Individualized Treatment Rules
Description: Provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019) <arXiv:1905.05389>.
Author: Michael Lingzhi Li [aut, cre], Kosuke Imai [aut]
Maintainer: Michael Lingzhi Li <mlli@mit.edu>

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