Sat, 23 Nov 2019

Package pCODE updated to version 0.9.3 with previous version 0.9.2 dated 2019-08-03

Title: Estimation of an Ordinary Differential Equation Model by Parameter Cascade Method
Description: An implementation of the parameter cascade method Ramsay, J. O., Hooker,G., Campbell, D., and Cao, J. (2007) <doi:10.1111/j.1467-9868.2007.00610.x> for estimating ordinary differential equation models with missing or complete observations. It combines smoothing method and profile estimation to estimate any non-linear dynamic system. The package also offers variance estimates for parameters of interest based on either bootstrap or Delta method.
Author: Haixu Wang [aut, cre], Jiguo Cao [aut]
Maintainer: Haixu Wang <haixuw@sfu.ca>

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Package bibliometrix updated to version 2.3.2 with previous version 2.3.1 dated 2019-10-30

Title: An R-Tool for Comprehensive Science Mapping Analysis
Description: Tool for quantitative research in scientometrics and bibliometrics. It provides various routines for importing bibliographic data from SCOPUS (<http://scopus.com>), Clarivate Analytics Web of Science (<http://www.webofknowledge.com/>), Dimensions (<https://www.dimensions.ai/>), Cochrane Library (<http://www.cochranelibrary.com/>) and PubMed (<https://www.ncbi.nlm.nih.gov/pubmed/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.
Author: Massimo Aria [cre, aut], Corrado Cuccurullo [aut]
Maintainer: Massimo Aria <aria@unina.it>

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Package nonlinearTseries updated to version 0.2.7 with previous version 0.2.6 dated 2019-02-21

Title: Nonlinear Time Series Analysis
Description: Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Author: Constantino A. Garcia [aut, cre], Gunther Sawitzki [ctb]
Maintainer: Constantino A. Garcia <constantino.garciama@ceu.es>

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Package rlang updated to version 0.4.2 with previous version 0.4.1 dated 2019-10-24

Title: Functions for Base Types and Core R and 'Tidyverse' Features
Description: A toolbox for working with base types, core R features like the condition system, and core 'Tidyverse' features like tidy evaluation.
Author: Lionel Henry [aut, cre], Hadley Wickham [aut], RStudio [cph]
Maintainer: Lionel Henry <lionel@rstudio.com>

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Package iheatmapr updated to version 0.4.12 with previous version 0.4.8 dated 2019-03-16

Title: Interactive, Complex Heatmaps
Description: Make complex, interactive heatmaps. 'iheatmapr' includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.
Author: Alicia Schep [aut, cre] (<https://orcid.org/0000-0002-3915-0618>), Sarah Kummerfeld [aut] (<https://orcid.org/0000-0002-0089-2358>), Genentech [cph], Andee Kaplan [rev] (Reviewer for rOpensci review process https://github.com/ropensci/onboarding/issues/107), Carl Ganz [rev] (Reviewer for rOpensci review process https://github.com/ropensci/onboarding/issues/107), Alex Johnson [ctb] (plotly.js library), Étienne Tétreault-Pinard [ctb] (plotly.js library), Mikola Lysenko [ctb] (plotly.js library), Ricky Reusser [ctb] (plotly.js library), Robert Monfera [ctb] (plotly.js library), Nicolas Riesco [ctb] (plotly.js library), Miklós Tusz [ctb] (plotly.js library), Chelsea Douglas [ctb] (plotly.js library), Ben Postlethwaite [ctb] (plotly.js library), Chris Parmer [ctb] (plotly.js library), Alex Vados [ctb] (plotly.js library), Plotly [cph] (plotly.js library), Frederick Boehm [ctb] (colorbars & main_heatmap)
Maintainer: Alicia Schep <aschep@gmail.com>

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Package fixest updated to version 0.2.1 with previous version 0.2.0 dated 2019-11-19

Title: Fast Fixed-Effects Estimations
Description: Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://wwwen.uni.lu/content/download/110162/1299525/file/2018_13>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.
Author: Laurent Berge [aut, cre]
Maintainer: Laurent Berge <laurent.berge@uni.lu>

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Package finalfit updated to version 0.9.7 with previous version 0.9.6 dated 2019-11-20

Title: Quickly Create Elegant Regression Results Tables and Plots when Modelling
Description: Generate regression results tables and plots in final format for publication. Explore models and export directly to PDF and 'Word' using 'RMarkdown'.
Author: Ewen Harrison [aut, cre], Tom Drake [aut], Riinu Ots [aut]
Maintainer: Ewen Harrison <ewen.harrison@ed.ac.uk>

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Package digest updated to version 0.6.23 with previous version 0.6.22 dated 2019-10-21

Title: Create Compact Hash Digests of R Objects
Description: Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash', 'murmurhash' and 'spookyhash' algorithms) permitting easy comparison of R language objects, as well as functions such as'hmac()' to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.
Author: Dirk Eddelbuettel <edd@debian.org> with contributions by Antoine Lucas, Jarek Tuszynski, Henrik Bengtsson, Simon Urbanek, Mario Frasca, Bryan Lewis, Murray Stokely, Hannes Muehleisen, Duncan Murdoch, Jim Hester, Wush Wu, Qiang Kou, Thierry Onkelinx, Michel Lang, Viliam Simko, Kurt Hornik, Radford Neal, Kendon Bell, Matthew de Queljoe, Ion Suruceanu, and Bill Denney.
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package Compositional updated to version 3.6 with previous version 3.5 dated 2019-06-18

Title: Compositional Data Analysis
Description: Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include a) Tsagris M.T., Preston S. and Wood A.T.A. (2011) A data-based power transformation for compositional data. Fourth International International Workshop on Compositional Data Analysis. b) Tsagris M. (2014). The k-NN algorithm for compositional data: a revised approach with and without zero values present. Journal of Data Science, 12(3):519--534. c) Tsagris M. (2015). A novel, divergence based, regression for compositional data. Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. d) Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2):47--57. e) Tsagris M., Preston S. and Wood A.T.A. (2016). Improved supervised classification for compositional data using the alpha-transformation. Journal of Classification, 33(2):243--261. <doi:10.1007/s00357-016-9207-5>. f) Tsagris M., Preston S. and Wood A.T.A. (2017). Nonparametric hypothesis testing for equality of means on the simplex. Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554> g) Tsagris M. and Stewart C. (2018). A Dirichlet regression model for compositional data with zeros. Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. h) Alenazi A. (2019). Regression for compositional data with compositional data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. Tsagris M. and Stewart C. (2019). A folded model for compositional data analysis. <arXiv:1802.07330>. Furher, we include functions for percentages (or proportions).
Author: Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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Package vosonSML updated to version 0.29.4 with previous version 0.27.2 dated 2019-07-18

Title: Collecting Social Media Data and Generating Networks for Analysis
Description: A suite of tools for collecting and constructing networks from social media data. Provides easy-to-use functions for collecting data across popular platforms (Twitter, YouTube and Reddit) and generating different types of networks for analysis.
Author: Timothy Graham, Robert Ackland, Chung-hong Chan, Bryan Gertzel
Maintainer: Bryan Gertzel <bryan.gertzel@anu.edu.au>

Diff between vosonSML versions 0.27.2 dated 2019-07-18 and 0.29.4 dated 2019-11-23

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Permanent link

Package disk.frame updated to version 0.2.1 with previous version 0.2.0 dated 2019-10-05

Title: Larger-than-RAM Disk-Based Data Manipulation Framework
Description: A disk-based data manipulation tool for working with large-than-RAM datasets. Aims to lower the barrier-to-entry for manipulating large datasets by adhering closely to popular and familiar data manipulation paradigms like dplyr verbs and data.table syntax.
Author: Dai ZJ [aut, cre], Jacky Poon [ctb]
Maintainer: Dai ZJ <zhuojia.dai@gmail.com>

Diff between disk.frame versions 0.2.0 dated 2019-10-05 and 0.2.1 dated 2019-11-23

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Permanent link

Package CALIBERrfimpute (with last version 1.0-1) was removed from CRAN

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

2018-06-11 1.0-1
2014-05-07 0.1-6
2014-03-24 0.1-5
2014-02-13 0.1-4
2013-11-11 0.1-2

Permanent link
Package Correlplot (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:

2013-10-13 1.0-2

Permanent link
Package tribe updated to version 0.1.8 with previous version 0.1.7 dated 2018-08-02

Title: Play with the Tribe of Attributes
Description: Functions to make manipulation of object attributes easier. It also contains a few functions that extend the 'dplyr' package for data manipulation, and it provides new pipe operators, including the pipe '%@>%' similar to the 'magrittr' '%>%', but with the additional functionality to enable attributes propagation.
Author: Paul Poncet [aut, cre], Stefan Milton Bache [aut] (for functions copied or modified from the 'magrittr' package), Hadley Wickham [aut] (for functions copied or modified from the 'magrittr' package)
Maintainer: Paul Poncet <paulponcet@yahoo.fr>

Diff between tribe versions 0.1.7 dated 2018-08-02 and 0.1.8 dated 2019-11-23

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Package stplanr updated to version 0.4.1 with previous version 0.4.0 dated 2019-10-13

Title: Sustainable Transport Planning
Description: Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. Enables common transport planning tasks including: downloading and cleaning transport datasets; creating geographic "desire lines" from origin-destination (OD) data; route assignment, locally and via interfaces to routing services such as <http://cyclestreets.net/>; calculation of route segment attributes such as bearing and aggregate flow; and 'travel watershed' analysis. See Lovelace and Ellison (2018) <doi:10.32614/RJ-2018-053>.
Author: Robin Lovelace [aut, cre] (<https://orcid.org/0000-0001-5679-6536>), Richard Ellison [aut], Malcolm Morgan [aut] (<https://orcid.org/0000-0002-9488-9183>), Barry Rowlingson [ctb] (Author of overline), Nick Bearman [ctb] (Co-author of gclip), Nikolai Berkoff [ctb] (Co-author of line2route), Scott Chamberlain [rev] (Scott reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/10), Mark Padgham [ctb], Andrea Gilardi [ctb] (<https://orcid.org/0000-0002-9424-7439>)
Maintainer: Robin Lovelace <rob00x@gmail.com>

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Package prettydoc updated to version 0.3.1 with previous version 0.3.0 dated 2019-07-18

Title: Creating Pretty Documents from R Markdown
Description: Creating tiny yet beautiful documents and vignettes from R Markdown. The package provides the 'html_pretty' output format as an alternative to the 'html_document' and 'html_vignette' engines that convert R Markdown into HTML pages. Various themes and syntax highlight styles are supported.
Author: Yixuan Qiu [aut, cre], Jason Long [ctb] (the Cayman, Tactile and Architect themes), Renyuan Zou [ctb] (the Leonids theme), Michael Rose [ctb] (the HPSTR theme), JJ Allaire [ctb] (pandoc template for rmarkdown), Hadley Wickham [ctb] (the html_vignette() function), Yihui Xie [ctb] (the html_vignette() function), Steve Matteson [ctb] (the Open Sans fonts), Emily Eisenberg [ctb] (the KaTeX library), Sophie Alpert [ctb] (the KaTeX library)
Maintainer: Yixuan Qiu <yixuan.qiu@cos.name>

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Package memisc updated to version 0.99.20.1 with previous version 0.99.17.2 dated 2019-04-24

Title: Management of Survey Data and Presentation of Analysis Results
Description: An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.
Author: Martin Elff (with contributions from Christopher N. Lawrence, Dave Atkins, Jason W. Morgan, Achim Zeileis)
Maintainer: Martin Elff <memisc@elff.eu>

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Package abn updated to version 2.2 with previous version 2.1 dated 2019-07-04

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>. 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@math.uzh.ch>

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Package VOSONDash updated to version 0.5.1 with previous version 0.4.4 dated 2019-08-09

Title: User Interface for Collecting and Analysing Social Networks
Description: A 'Shiny' application for the interactive visualisation and analysis of networks that also provides a web interface for collecting social media data using 'vosonSML'.
Author: Bryan Gertzel, Robert Ackland
Maintainer: Bryan Gertzel <bryan.gertzel@anu.edu.au>

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Package gradeR updated to version 1.0.5 with previous version 1.0.4 dated 2019-10-21

Title: Helps Grade Assignment Submissions that are R Scripts
Description: After being given the location of your students' submissions and a test file, the function runs each .R file, and evaluates the results from all the given tests. Results are neatly returned in a data frame that has a row for each student, and a column for each test.
Author: Taylor Brown [aut, cre]
Maintainer: Taylor Brown <trb5me@virginia.edu>

Diff between gradeR versions 1.0.4 dated 2019-10-21 and 1.0.5 dated 2019-11-23

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Package forecastML updated to version 0.6.0 with previous version 0.5.0 dated 2019-10-09

Title: Time Series Forecasting with Machine Learning Methods
Description: The purpose of 'forecastML' is to simplify the process of multi-step-ahead direct forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
Author: Nickalus Redell
Maintainer: Nickalus Redell <nickalusredell@gmail.com>

Diff between forecastML versions 0.5.0 dated 2019-10-09 and 0.6.0 dated 2019-11-23

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Package capitalR updated to version 1.3.0 with previous version 1.2.0 dated 2019-05-09

Title: Capital Budgeting Analysis, Annuity Loan Calculations and Amortization Schedules
Description: Provides Capital Budgeting Analysis functionality and the essential Annuity loan functions. Also computes Loan Amortization Schedules including schedules with irregular payments.
Author: John T. Buynak
Maintainer: John T. Buynak <jbuynak94@gmail.com>

Diff between capitalR versions 1.2.0 dated 2019-05-09 and 1.3.0 dated 2019-11-23

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