Mon, 04 Jan 2021

Package round updated to version 0.20-0 with previous version 0.12-2 dated 2020-07-06

Title: Rounding to Decimal Digits
Description: Decimal rounding is non-trivial in binary arithmetic. ISO standard round to even is more rare than typically assumed as most decimal fractions are not exactly representable in binary. Our roundX() versions explore differences between current and potential future versions of round() in R. Further, provides (some partly related) C99 math lib functions not in base R.
Author: Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), R-core [ctb] ("r1.C" only)
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

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Package janitor updated to version 2.1.0 with previous version 2.0.1 dated 2020-04-12

Title: Simple Tools for Examining and Cleaning Dirty Data
Description: The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and isolate duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel. This package follows the principles of the "tidyverse" and works well with the pipe function %>%. janitor was built with beginning-to-intermediate R users in mind and is optimized for user-friendliness. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff.
Author: Sam Firke [aut, cre], Bill Denney [ctb], Chris Haid [ctb], Ryan Knight [ctb], Malte Grosser [ctb], Jonathan Zadra [ctb]
Maintainer: Sam Firke <samuel.firke@gmail.com>

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Package cspp updated to version 0.3.1 with previous version 0.3.0 dated 2020-10-27

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: Josh McCrain <josh.mccrain@gmail.com>

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Package causalCmprsk updated to version 1.0.1 with previous version 1.0.0 dated 2020-11-03

Title: Nonparametric and Cox-Based Estimation of ATE in Competing Risks
Description: Estimation of average treatment effects (ATE) of two static treatment regimes on time-to-event outcomes with K competing events (K can be 1). The method uses propensity scores weighting for emulation of baseline randomization.
Author: Bella Vakulenko-Lagun, Colin Magdamo, Marie-Laure Charpignon, Bang Zheng, Mark Albers, Sudeshna Das
Maintainer: Bella Vakulenko-Lagun <blagun@stat.haifa.ac.il>

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Package uncmbb updated to version 0.2.2 with previous version 0.2.1 dated 2019-10-28

Title: UNC Men's Basketball Match Results Since 1949-1950 Season
Description: Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.
Author: Jay Lee [aut, cre]
Maintainer: Jay Lee <joongsup@gmail.com>

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Package JuliaCall updated to version 0.17.2 with previous version 0.17.1 dated 2019-11-27

Title: Seamless Integration Between R and 'Julia'
Description: Provides an R interface to 'Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see <https://julialang.org/> for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any 'Julia' function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like 'Julia'.
Author: Changcheng Li [aut, cre], Randy Lai [ctb], Dmitri Grominski [ctb], Nagi Teramo [ctb]
Maintainer: Changcheng Li <cxl508@psu.edu>

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Package IMIFA updated to version 2.1.5 with previous version 2.1.4 dated 2020-11-18

Title: Infinite Mixtures of Infinite Factor Analysers and Related Models
Description: Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Cinzia Viroli [ctb] (<https://orcid.org/0000-0002-3278-5266>), Isobel Claire Gormley [ctb] (<https://orcid.org/0000-0001-7713-681X>)
Maintainer: Keefe Murphy <keefe.murphy@mu.ie>

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Package dynparam updated to version 1.0.2 with previous version 1.0.1 dated 2020-06-24

Title: Creating Meta-Information for Parameters
Description: Provides tools for describing parameters of algorithms in an abstract way. Description can include an id, a description, a domain (range or list of values), and a default value. 'dynparam' can also convert parameter sets to a 'ParamHelpers' format, in order to be able to use 'dynparam' in conjunction with 'mlrMBO'.
Author: Robrecht Cannoodt [aut, cre] (<https://orcid.org/0000-0003-3641-729X>, rcannood), Wouter Saelens [aut] (<https://orcid.org/0000-0002-7114-6248>, zouter)
Maintainer: Robrecht Cannoodt <rcannood@gmail.com>

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Package CausalImpact updated to version 1.2.5 with previous version 1.2.4 dated 2020-01-08

Title: Inferring Causal Effects using Bayesian Structural Time-Series Models
Description: Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>. See the package documentation on GitHub <https://google.github.io/CausalImpact/> to get started.
Author: Kay H. Brodersen <kbrodersen@google.com>, Alain Hauser <alhauser@google.com>
Maintainer: Alain Hauser <alhauser@google.com>

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Package wordpressr updated to version 0.2.0 with previous version 0.1.1 dated 2020-09-29

Title: An API Wrapper for WordPress Site APIs
Description: An API wrapper that helps R developers retrieve data from a WordPress site's API in tidy format, in addition to publishing content on the WordPress site (WordPress API documentation: <https://developer.wordpress.org/rest-api/>).
Author: Simit Patel [aut, cre], SixJupiter [cph, fnd]
Maintainer: Simit Patel <simit@sixjupiter.com>

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Package yhat updated to version 2.0-3 with previous version 2.0-2 dated 2020-05-27

Title: Interpreting Regression Effects
Description: The purpose of this package is to provide methods to interpret multiple linear regression and canonical correlation results including beta weights,structure coefficients, validity coefficients, product measures, relative weights, all-possible-subsets regression, dominance analysis, commonality analysis, and adjusted effect sizes.
Author: Kim Nimon <kim.nimon@gmail.com>, Fred Oswald, and J. Kyle Roberts.
Maintainer: Kim Nimon <kim.nimon@gmail.com>

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Package RestRserve updated to version 0.4.1 with previous version 0.4.0 dated 2020-11-11

Title: A Framework for Building HTTP API
Description: Allows to easily create high-performance full featured HTTP APIs from R functions. Provides high-level classes such as 'Request', 'Response', 'Application', 'Middleware' in order to streamline server side application development. Out of the box allows to serve requests using 'Rserve' package, but flexible enough to integrate with other HTTP servers such as 'httpuv'.
Author: Dmitriy Selivanov [aut, cre] (<https://orcid.org/0000-0001-5413-1506>), Artem Klevtsov [aut] (<https://orcid.org/0000-0003-0492-6647>), rexy.ai [cph, fnd]
Maintainer: Dmitriy Selivanov <ds@rexy.ai>

Diff between RestRserve versions 0.4.0 dated 2020-11-11 and 0.4.1 dated 2021-01-04

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Package parallelly updated to version 1.23.0 with previous version 1.22.0 dated 2020-12-13

Title: Enhancing the 'parallel' Package
Description: Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package. For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package ggdist updated to version 2.4.0 with previous version 2.3.0 dated 2020-10-30

Title: Visualizations of Distributions and Uncertainty
Description: Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <doi:10.1111/1467-985X.00120>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.
Author: Matthew Kay [aut, cre]
Maintainer: Matthew Kay <mjskay@northwestern.edu>

Diff between ggdist versions 2.3.0 dated 2020-10-30 and 2.4.0 dated 2021-01-04

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Package future.batchtools updated to version 0.10.0 with previous version 0.9.0 dated 2020-04-14

Title: A Future API for Parallel and Distributed Processing using 'batchtools'
Description: Implementation of the Future API on top of the 'batchtools' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between future.batchtools versions 0.9.0 dated 2020-04-14 and 0.10.0 dated 2021-01-04

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Package future.apply updated to version 1.7.0 with previous version 1.6.0 dated 2020-07-01

Title: Apply Function to Elements in Parallel using Futures
Description: Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
Author: Henrik Bengtsson [aut, cre, cph], R Core Team [cph, ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between future.apply versions 1.6.0 dated 2020-07-01 and 1.7.0 dated 2021-01-04

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Package doFuture updated to version 0.12.0 with previous version 0.11.0 dated 2020-12-12

Title: A Universal Foreach Parallel Adapter using the Future API of the 'future' Package
Description: Provides a '%dopar%' adapter such that any type of futures can be used as backends for the 'foreach' framework.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between doFuture versions 0.11.0 dated 2020-12-12 and 0.12.0 dated 2021-01-04

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Package aweek updated to version 1.0.2 with previous version 1.0.1 dated 2020-04-29

Title: Convert Dates to Arbitrary Week Definitions
Description: Which day a week starts depends heavily on the either the local or professional context. This package is designed to be a lightweight solution to easily switching between week-based date definitions.
Author: Zhian N. Kamvar [aut, cre]
Maintainer: Zhian N. Kamvar <zkamvar@gmail.com>

Diff between aweek versions 1.0.1 dated 2020-04-29 and 1.0.2 dated 2021-01-04

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Package SCORNET updated to version 0.1.1 with previous version 0.1.0 dated 2020-11-12

Title: Semi-Supervised Calibration of Risk with Noisy Event Times
Description: A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) <doi:10.2307/2670119>.
Author: Yuri Ahuja [aut, cre]
Maintainer: Yuri Ahuja <Yuri_Ahuja@hms.harvard.edu>

Diff between SCORNET versions 0.1.0 dated 2020-11-12 and 0.1.1 dated 2021-01-04

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Package ppmSuite updated to version 0.1.3 with previous version 0.1.1 dated 2020-11-26

Title: A Collection of Models that Employ a Prior Distribution on Partitions
Description: Provides functions that fit hierarchical Gaussian and probit ordinal models. A (covariate dependent) product partition model is used as a prior. If a covariate dependent product partition model is selected, then all the options detailed in Page, G.L.; Quintana, F.A.; (2018) <doi:10.1007/s11222-017-9777-z> are available. If covariate values are missing, then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) <arXiv:1912.13119> is employed. Also included in the package is a function that fits a Gaussian likelihood spatial product partition model that is detailed in Page, G.L.; Quintana, F.A.; (2016) <doi:10.1214/15-BA971>.
Author: Garritt L. Page [aut, cre, cph], S. McKay Curtis [ctb, cph], Radford M. Neal [ctb, cph]
Maintainer: Garritt L. Page <page@stat.byu.edu>

Diff between ppmSuite versions 0.1.1 dated 2020-11-26 and 0.1.3 dated 2021-01-04

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Package xLLiM updated to version 2.2 with previous version 2.1 dated 2017-05-23

Title: High Dimensional Locally-Linear Mapping
Description: Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <https://hal.archives-ouvertes.fr/hal-01347455>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
Author: Emeline Perthame (emeline.perthame@inria.fr), Florence Forbes (florence.forbes@inria.fr), Antoine Deleforge (antoine.deleforge@inria.fr), Emilie Devijver (emilie.devijver@kuleuven.be), Melina Gallopin (melina.gallopin@u-psud.fr)
Maintainer: Emeline Perthame <emeline.perthame@pasteur.fr>

Diff between xLLiM versions 2.1 dated 2017-05-23 and 2.2 dated 2021-01-04

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Package RFCCA updated to version 1.0.3 with previous version 1.0.2 dated 2020-12-04

Title: Random Forest with Canonical Correlation Analysis
Description: Random Forest with Canonical Correlation Analysis (RFCCA) is a random forest method for estimating the canonical correlations between two sets of variables depending on the subject-related covariates. The trees are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. The method is described in Alakus et al. (2020) <arXiv:2011.11555>. RFCCA uses 'randomForestSRC' package (Ishwaran and Kogalur, 2020) by freezing at the version 2.9.3. The custom splitting rule feature is utilised to apply the proposed splitting rule.
Author: Cansu Alakus [aut, cre], Denis Larocque [aut], Aurelie Labbe [aut], Hemant Ishwaran [ctb] (Author of included randomForestSRC codes), Udaya B. Kogalur [ctb] (Author of included randomForestSRC codes)
Maintainer: Cansu Alakus <cansu.alakus@hec.ca>

Diff between RFCCA versions 1.0.2 dated 2020-12-04 and 1.0.3 dated 2021-01-04

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Package pathfindR updated to version 1.6.1 with previous version 1.6.0 dated 2020-11-21

Title: Enrichment Analysis Utilizing Active Subnetworks
Description: Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. pathfindR is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. pathfindR also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in pathfindR are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
Author: Ege Ulgen [cre, cph] (<https://orcid.org/0000-0003-2090-3621>), Ozan Ozisik [aut] (<https://orcid.org/0000-0001-5980-8002>)
Maintainer: Ege Ulgen <egeulgen@gmail.com>

Diff between pathfindR versions 1.6.0 dated 2020-11-21 and 1.6.1 dated 2021-01-04

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Package mrbin updated to version 1.5.0 with previous version 1.4.4 dated 2020-12-09

Title: Magnetic Resonance Binning, Integration and Normalization
Description: Nuclear Magnetic Resonance is widely used in Life Science research. The package (<http://www.kleinomicslab.com/software/>) converts 1D or 2D data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, are replaced by positive values. All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time.
Author: Matthias Klein [aut, cre] (<https://orcid.org/0000-0001-7455-5381>)
Maintainer: Matthias Klein <klein.663@osu.edu>

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Package MATA updated to version 0.5 with previous version 0.4 dated 2019-02-15

Title: Model-Averaged Tail Area Wald (MATA-Wald) Confidence Interval
Description: Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence intervals, which are constructed using single-model estimators and model weights. See Turek and Fletcher (2012) <doi:10.1016/j.csda.2012.03.002> for details.
Author: Daniel Turek [aut, cre]
Maintainer: Daniel Turek <danielturek@gmail.com>

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Package kmed updated to version 0.4.0 with previous version 0.3.0 dated 2019-06-14

Title: Distance-Based k-Medoids
Description: Algorithms of distance-based k-medoids clustering: simple and fast k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. Calculate distances for mixed variable data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and relative criteria. The internal criteria includes silhouette index and shadow values. The relative criterium applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages. The cluster result can be plotted in a marked barplot or pca biplot.
Author: Weksi Budiaji
Maintainer: Weksi Budiaji <budiaji@untirta.ac.id>

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Package PortfolioAnalysis updated to version 1.1.1 with previous version 1.1 dated 2020-11-08

Title: Portfolio Optimization Methods
Description: Collection of functions to optimize portfolio weights using quadratic programming. This package includes different functions to compute portfolio weights based on different constraints and methods. For more information see Markowitz, H.M. (1952), <doi:10.2307/2975974>. Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748].
Author: Anurag Agrawal [aut, cre] (<https://orcid.org/0000-0003-2272-8273>)
Maintainer: Anurag Agrawal <agrawalanurag1999@gmail.com>

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Package mlma updated to version 6.0-1 with previous version 6.0-0 dated 2020-12-11

Title: Multilevel Mediation Analysis
Description: Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.
Author: Qingzhao Yu, Bin Li
Maintainer: Qingzhao Yu <qyu@lsuhsc.edu>

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Package future.callr updated to version 0.6.0 with previous version 0.5.0 dated 2019-09-28

Title: A Future API for Parallel Processing using 'callr'
Description: Implementation of the Future API on top of the 'callr' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine. Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession'), the 'callr' backend provided here can run more than 125 parallel R processes.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package apaTables updated to version 2.0.8 with previous version 2.0.5 dated 2018-08-29

Title: Create American Psychological Association (APA) Style Tables
Description: A common task faced by researchers is the creation of APA style (i.e., American Psychological Association style) tables from statistical output. In R a large number of function calls are often needed to obtain all of the desired information for a single APA style table. As well, the process of manually creating APA style tables in a word processor is prone to transcription errors. This package creates Word files (.doc files) containing APA style tables for several types of analyses. Using this package minimizes transcription errors and reduces the number commands needed by the user.
Author: David Stanley [aut, cre]
Maintainer: David Stanley <dstanley@uoguelph.ca>

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Package TULIP updated to version 1.0.2 with previous version 1.0.1 dated 2020-06-29

Title: A Toolbox for Linear Discriminant Analysis with Penalties
Description: Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.
Author: Yuqing Pan <yuqing.pan@stat.fsu.edu>, Qing Mai <mai@stat.fsu.edu>, Xin Zhang <henry@stat.fsu.edu>
Maintainer: Yuqing Pan <yuqing.pan@stat.fsu.edu>

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Package terra updated to version 1.0-7 with previous version 0.9-11 dated 2020-12-01

Title: Spatial Data Analysis
Description: Methods for spatial data analysis, especially raster data. Methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/terra/> to get started. 'terra' is very similar to the 'raster' package; but 'terra' is simpler, better, and faster.
Author: Robert J. Hijmans [cre, aut] (<https://orcid.org/0000-0001-5872-2872>), Roger Bivand [ctb] (<https://orcid.org/0000-0003-2392-6140>), Karl Forner [ctb], Jeroen Ooms [ctb] (<https://orcid.org/0000-0002-4035-0289>), Edzer Pebesma [ctb] (<https://orcid.org/0000-0001-8049-7069>)
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

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 terra-1.0-7/terra/src/aggregate.cpp                 |   29 
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 terra-1.0-7/terra/src/buffer.cpp                    |    4 
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 261 files changed, 11702 insertions(+), 4224 deletions(-)

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Package popsom updated to version 5.1 with previous version 5.0 dated 2020-12-09

Title: Tools for Building, Visualizing, and Evaluating Self-Organizing Maps
Description: A self-organizing map package with three distinguishing features: (1) Automatic cluster centroid detection and visualization using starbursts. (2) Maintains two models of the data: (a) a self-organizing map model (b) a centroid based clustering model. (3) A very efficient stochastic training algorithm based on ideas from tensor algebra.
Author: Lutz Hamel [aut, cre], Benjamin Ott [aut], Gregory Breard [aut], Robert Tatoian [aut], Michael Eiger [aut], Vishakh Gopu [aut]
Maintainer: Lutz Hamel <lutzhamel@uri.edu>

Diff between popsom versions 5.0 dated 2020-12-09 and 5.1 dated 2021-01-04

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More information about popsom at CRAN
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Package officer updated to version 0.3.16 with previous version 0.3.15 dated 2020-11-01

Title: Manipulation of Microsoft Word and PowerPoint Documents
Description: Access and manipulate 'Microsoft Word' and 'Microsoft PowerPoint' documents from R. The package focuses on tabular and graphical reporting from R; it also provides two functions that let users get document content into data objects. A set of functions lets add and remove images, tables and paragraphs of text in new or existing documents. The package does not require any installation of Microsoft products to be able to write Microsoft files.
Author: David Gohel [aut, cre], Frank Hangler [ctb] (function body_replace_all_text), Liz Sander [ctb] (several documentation fixes), Anton Victorson [ctb] (fixes xml structures), Jon Calder [ctb] (update vignettes), John Harrold [ctb] (function annotate_base), John Muschelli [ctb] (google doc compatibility)
Maintainer: David Gohel <david.gohel@ardata.fr>

Diff between officer versions 0.3.15 dated 2020-11-01 and 0.3.16 dated 2021-01-04

 DESCRIPTION                           |   14 +--
 MD5                                   |  143 +++++++++++++++++-----------------
 NAMESPACE                             |    5 -
 NEWS.md                               |   15 +++
 R/docx_add.R                          |  115 +++++++++++++++------------
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 R/docx_img.R                          |   40 +++++++++
 R/docx_part.R                         |    1 
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 R/ooxml_block_objects.R               |   12 ++
 R/ooxml_run_objects.R                 |   78 +++++++++++++++++-
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 tests/testthat/test-docx-footnotes.R  |   18 +---
 tests/testthat/test-docx-insert.R     |   20 ++--
 tests/testthat/test-docx-misc.R       |   71 ++++++++--------
 tests/testthat/test-docx-replace.R    |   14 +--
 tests/testthat/test-fp_border.R       |   17 +---
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 tests/testthat/test-fp_par.R          |   16 +--
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 73 files changed, 851 insertions(+), 704 deletions(-)

More information about officer at CRAN
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Package HardyWeinberg updated to version 1.7.1 with previous version 1.7.0 dated 2020-12-14

Title: Statistical Tests and Graphics for Hardy-Weinberg Equilibrium
Description: Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908; Weinberg, 1908) <doi:10.1126/science.28.706.49> for bi and multi-allelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included (Graffelman & Weir, 2016) <doi: 10.1038/hdy.2016.20>, including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots.
Author: Jan Graffelman [aut, cre], Christopher Chang [ctb], Xavi Puig [ctb], Jan Wigginton [ctb], Leonardo Ortoleva [ctb], William R. Engels [ctb]
Maintainer: Jan Graffelman <jan.graffelman@upc.edu>

Diff between HardyWeinberg versions 1.7.0 dated 2020-12-14 and 1.7.1 dated 2021-01-04

 DESCRIPTION                  |    8 ++++----
 MD5                          |   35 +++++++++++++++++++----------------
 NAMESPACE                    |    5 +++--
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 src/HWxChromosomal.cpp       |   29 ++++++++++++++++-------------
 vignettes/HardyWeinberg.Rnw  |    2 +-
 20 files changed, 60 insertions(+), 54 deletions(-)

More information about HardyWeinberg at CRAN
Permanent link

Package ggspatial updated to version 1.1.5 with previous version 1.1.4 dated 2020-07-12

Title: Spatial Data Framework for ggplot2
Description: Spatial data plus the power of the ggplot2 framework means easier mapping when input data are already in the form of spatial objects.
Author: Dewey Dunnington [aut, cre] (<https://orcid.org/0000-0002-9415-4582>), Brent Thorne [ctb] (<https://orcid.org/0000-0002-1099-3857>)
Maintainer: Dewey Dunnington <dewey@fishandwhistle.net>

Diff between ggspatial versions 1.1.4 dated 2020-07-12 and 1.1.5 dated 2021-01-04

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 ggspatial-1.1.5/ggspatial/tests/testthat/test-annotation-map-tile.R      |   10 ++
 ggspatial-1.1.5/ggspatial/tests/testthat/test-annotation-north-arrow.R   |    9 +
 ggspatial-1.1.5/ggspatial/tests/testthat/test-annotation-scale.R         |    5 +
 ggspatial-1.1.5/ggspatial/tests/testthat/test-df-spatial-raster.R        |    2 
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 ggspatial-1.1.5/ggspatial/tests/testthat/test-geom-spatial-segment.R     |    3 
 ggspatial-1.1.5/ggspatial/tests/testthat/test-geom-spatial-xline.R       |    4 
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More information about ggspatial at CRAN
Permanent link

Package future.BatchJobs updated to version 0.17.0 with previous version 0.16.2 dated 2019-09-29

Title: A Future API for Parallel and Distributed Processing using BatchJobs
Description: Implementation of the Future API on top of the 'BatchJobs' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'. NOTE: The 'BatchJobs' package is deprecated in favor of the 'batchtools' package. Because of this, it is recommended to use the 'future.batchtools' package instead of this package.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between future.BatchJobs versions 0.16.2 dated 2019-09-29 and 0.17.0 dated 2021-01-04

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Package EValue updated to version 4.1.1 with previous version 4.1.0 dated 2020-11-18

Title: Sensitivity Analyses for Unmeasured Confounding and Other Biases in Observational Studies and Meta-Analyses
Description: Conducts sensitivity analyses for unmeasured confounding,selection bias, and measurement error (individually or in combination) in individual studies. Also conducts sensitivity analyses for unmeasured confounding in meta-analyses.
Author: Maya B. Mathur [cre, aut], Louisa H. Smith [aut], Peng Ding [aut], Tyler J. VanderWeele [aut]
Maintainer: Maya B. Mathur <mmathur@stanford.edu>

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Package catch updated to version 1.0.1 with previous version 1.0 dated 2018-05-14

Title: Covariate-Adjusted Tensor Classification in High-Dimensions
Description: Performs classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in Pan, Mai and Zhang (2018) <arXiv:1805.04421>. The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates.
Author: Yuqing Pan <yuqing.pan@stat.fsu.edu>, Qing Mai <mai@stat.fsu.edu>, Xin Zhang <henry@stat.fsu.edu>
Maintainer: Yuqing Pan <yuqing.pan@stat.fsu.edu>

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Package rEDM updated to version 1.7.3 with previous version 1.7.1 dated 2020-12-03

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], 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 its.analysis updated to version 1.6.0 with previous version 1.4.1 dated 2019-07-22

Title: Running Interrupted Time Series Analysis
Description: Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.
Author: Patrick English
Maintainer: Patrick English <p.english@exeter.ac.uk>

Diff between its.analysis versions 1.4.1 dated 2019-07-22 and 1.6.0 dated 2021-01-04

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Package drake updated to version 7.13.0 with previous version 7.12.7 dated 2020-10-27

Title: A Pipeline Toolkit for Reproducible Computation at Scale
Description: A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://docs.ropensci.org/drake/> and the online manual <https://books.ropensci.org/drake/>.
Author: William Michael Landau [aut, cre] (<https://orcid.org/0000-0003-1878-3253>), Alex Axthelm [ctb], Jasper Clarkberg [ctb], Kirill Müller [ctb], Ben Bond-Lamberty [ctb] (<https://orcid.org/0000-0001-9525-4633>), Tristan Mahr [ctb] (<https://orcid.org/0000-0002-8890-5116>), Miles McBain [ctb] (<https://orcid.org/0000-0003-2865-2548>), Noam Ross [ctb] (<https://orcid.org/0000-0002-2136-0000>), Ellis Hughes [ctb], Matthew Mark Strasiotto [ctb], Ben Marwick [rev], Peter Slaughter [rev], Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau@gmail.com>

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New package collapse with initial version 1.5.0
Package: collapse
Title: Advanced and Fast Data Transformation
Version: 1.5.0
Date: 2021-01-04
Authors@R: c( person("Sebastian", "Krantz", role = c("aut", "cre"), email = "sebastian.krantz@graduateinstitute.ch"), person("Matt", "Dowle", role = "ctb"), person("Arun", "Srinivasan", role = "ctb"), person("Laurent", "Berge", role = "ctb"), person("Dirk", "Eddelbuettel", role = "ctb"), person("Josh", "Pasek", role = "ctb"), person("Kevin", "Tappe", role = "ctb"), person("R Core Team and contributors worldwide", role = "ctb"), person("Martyn", "Plummer", role = "cph"), person("1999-2016 The R Core Team", role = "cph") )
Description: A C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, flexible and parsimonious to code with, class-agnostic and programmer friendly. It is well integrated with base R, 'dplyr' / (grouped) 'tibble', 'data.table' and 'plm' (panel-series and data frames), and non- destructively handles other matrix or data frame based classes (such as 'ts', 'xts' / 'zoo', 'timeSeries', 'tsibble', 'sf' data frames etc.) --- Key Features: --- (1) Advanced statistical programming: A full set of fast statistical functions supporting grouped and weighted computations on vectors, matrices and data frames. Fast and programmable grouping, ordering, unique values / rows, factor generation and interactions. Fast and flexible functions for data manipulation and data object conversions. (2) Advanced aggregation: Fast and easy multi-data-type, multi-function, weighted, parallelized and fully customized data aggregation. (3) Advanced transformations: Fast row / column arithmetic, (grouped) replacing and sweeping out of statistics, (grouped, weighted) scaling / standardizing, between (averaging) and (quasi-)within (centering / demeaning) transformations, higher-dimensional centering (i.e. multiple fixed effects transformations), linear prediction / partialling-out, linear model fitting and testing. (4) Advanced time-computations: Fast (sequences of) lags / leads, and (lagged / leaded, iterated, quasi-, log-) differences and (compounded) growth rates on (unordered, irregular) time series and panel data. Multivariate auto-, partial- and cross-correlation functions for panel data. Panel data to (ts-)array conversions. (5) List processing: (Recursive) list search / identification, splitting, extraction / subsetting, data-apply, and generalized recursive row-binding / unlisting in 2D. (6) Advanced data exploration: Fast (grouped, weighted, panel-decomposed) summary statistics for complex multilevel / panel data.
URL: https://sebkrantz.github.io/collapse/, https://github.com/SebKrantz/collapse, https://twitter.com/collapse_R
BugReports: https://github.com/SebKrantz/collapse/issues
License: GPL (>= 2) | file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
Imports: Rcpp (>= 1.0.1)
LinkingTo: Rcpp
Suggests: dplyr, plm, data.table, matrixStats, magrittr, fixest, vars, weights, RcppArmadillo, RcppEigen, ggplot2, scales, microbenchmark, testthat, covr
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2021-01-04 13:00:48 UTC; Sebastian Krantz
Author: Sebastian Krantz [aut, cre], Matt Dowle [ctb], Arun Srinivasan [ctb], Laurent Berge [ctb], Dirk Eddelbuettel [ctb], Josh Pasek [ctb], Kevin Tappe [ctb], R Core Team and contributors worldwide [ctb], Martyn Plummer [cph], 1999-2016 The R Core Team [cph]
Maintainer: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Repository: CRAN
Date/Publication: 2021-01-04 17:00:03 UTC

More information about collapse at CRAN
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Package pleiades (with last version 0.3.0) was removed from CRAN

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

2020-10-07 0.3.0
2017-06-15 0.2.0
2016-12-19 0.1.0

Permanent link
Package geoops (with last version 0.3.0) was removed from CRAN

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

2020-05-16 0.3.0
2018-06-18 0.2.0
2018-03-19 0.1.8
2017-10-17 0.1.2
2017-10-14 0.1.0

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Package robustbase updated to version 0.93-7 with previous version 0.93-6 dated 2020-03-23

Title: Basic Robust Statistics
Description: "Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Author: Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Peter Rousseeuw [ctb] (Qn and Sn), Christophe Croux [ctb] (Qn and Sn), Valentin Todorov [aut] (most robust Cov), Andreas Ruckstuhl [aut] (nlrob, anova, glmrob), Matias Salibian-Barrera [aut] (lmrob orig.), Tobias Verbeke [ctb, fnd] (mc, adjbox), Manuel Koller [aut] (mc, lmrob, psi-func.), Eduardo L. T. Conceicao [aut] (MM-, tau-, CM-, and MTL- nlrob), Maria Anna di Palma [ctb] (initial version of Comedian)
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

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Package rfacts updated to version 0.1.0 with previous version 0.0.2 dated 2020-10-14

Title: R Interface to 'FACTS' on Unix-Like Systems
Description: The 'rfacts' package is an R interface to the Fixed and Adaptive Clinical Trial Simulator ('FACTS') on Unix-like systems. It programmatically invokes 'FACTS' to run clinical trial simulations, and it aggregates simulation output data into tidy data frames. These capabilities provide end-to-end automation for large-scale simulation pipelines, and they enhance computational reproducibility. For more information on 'FACTS' itself, please visit <https://www.berryconsultants.com/software/>.
Author: William Michael Landau [aut, cre] (<https://orcid.org/0000-0003-1878-3253>), Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau@gmail.com>

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Package rfacebookstat updated to version 2.2.2 with previous version 2.1.9 dated 2020-10-01

Title: Load Data from Facebook API Marketing
Description: Load data by campaigns, ads, ad sets and insights, ad account and business manager from Facebook Marketing API into R. For more details see official documents by Facebook Marketing API <https://developers.facebook.com/docs/marketing-apis/>.
Author: Alexey Seleznev [aut, cre] (<https://orcid.org/0000-0003-0410-7385>)
Maintainer: Alexey Seleznev <selesnow@gmail.com>

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Package r2dii.match updated to version 0.0.8 with previous version 0.0.7 dated 2020-12-03

Title: Tools to Match Financial Portfolios with Climate Data
Description: These tools implement in R a fundamental part of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (<https://2degrees-investing.org/>). Financial institutions use 'PACTA' to study how their capital allocation impacts the climate. This package matches data from financial portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals.
Author: Mauro Lepore [aut, cre, ctr] (<https://orcid.org/0000-0002-1986-7988>), Jackson Hoffart [aut] (<https://orcid.org/0000-0002-8600-5042>), Klaus Hagedorn [aut], Florence Palandri [aut], Evgeny Petrovsky [aut], 2 Degrees Investing Initiative [cph, fnd]
Maintainer: Mauro Lepore <maurolepore@gmail.com>

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Package proffer updated to version 0.1.1 with previous version 0.1.0 dated 2020-03-22

Title: Profile R Code and Visualize with 'Pprof'
Description: Like similar profiling tools, the 'proffer' package automatically detects sources of slowness in R code. The distinguishing feature of 'proffer' is its utilization of 'pprof', which supplies interactive visualizations that are efficient and easy to interpret. Behind the scenes, the 'profile' package converts native Rprof() data to a protocol buffer that 'pprof' understands. For the documentation of 'proffer', visit <https://r-prof.github.io/proffer>. To learn about the implementations and methodologies of 'pprof', 'profile', and protocol buffers, visit <https://github.com/google/pprof>. <https://developers.google.com/protocol-buffers>, and <https://github.com/r-prof/profile>, respectively.
Author: William Michael Landau [aut, cre] (<https://orcid.org/0000-0003-1878-3253>), Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau@gmail.com>

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Package otpr updated to version 0.5.0 with previous version 0.4.2 dated 2020-10-01

Title: An R Wrapper for the 'OpenTripPlanner' REST API
Description: A wrapper for the 'OpenTripPlanner' <http://www.opentripplanner.org/> REST API. Queries are submitted to the relevant 'OpenTripPlanner' API resource, the response is parsed and useful R objects are returned.
Author: Marcus Young [aut, cre] (<https://orcid.org/0000-0003-4627-1116>)
Maintainer: Marcus Young <M.A.Young@soton.ac.uk>

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Package jfa updated to version 0.5.0 with previous version 0.4.0 dated 2020-10-30

Title: Bayesian and Classical Audit Sampling
Description: Implements the audit sampling workflow as discussed in Derks et al. (2019) <doi:10.31234/osf.io/9f6ub>. The package makes it easy for an auditor to plan an audit sample, sample from the population, and evaluating that sample using various confidence bounds according to the International Standards on Auditing. Furthermore, the package implements Bayesian equivalents of these methods.
Author: Koen Derks [aut, cre] (<https://orcid.org/0000-0002-5533-9349>)
Maintainer: Koen Derks <k.derks@nyenrode.nl>

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Package geos updated to version 0.0.2 with previous version 0.0.1 dated 2020-08-13

Title: Open Source Geometry Engine ('GEOS') R API
Description: Provides an R API to the Open Source Geometry Engine ('GEOS') library (<https://trac.osgeo.org/geos/>) and a vector format with which to efficiently store 'GEOS' geometries. High-performance functions to extract information from, calculate relationships between, and transform geometries are provided. Finally, facilities to import and export geometry vectors to other spatial formats are provided.
Author: Dewey Dunnington [aut, cre] (<https://orcid.org/0000-0002-9415-4582>), Edzer Pebesma [aut] (<https://orcid.org/0000-0001-8049-7069>)
Maintainer: Dewey Dunnington <dewey@fishandwhistle.net>

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Package restatapi updated to version 0.9.8 with previous version 0.8.10 dated 2020-09-18

Title: Search and Retrieve Data from Eurostat Database
Description: Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX <https://sdmx.org>) Web Services (<https://ec.europa.eu/eurostat/web/sdmx-web-services/about-this-service>) to search and download data from the Eurostat database using the SDMX standard.
Author: Mátyás Mészáros [aut, cre]
Maintainer: Mátyás Mészáros <matyas.meszaros@ec.europa.eu>

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Package gfilinreg updated to version 2.0.0 with previous version 1.0.0 dated 2020-12-04

Title: Generalized Fiducial Inference for Low-Dimensional Robust Linear Regression
Description: Fiducial framework for linear regression models allowing normal, Student, Cauchy, or logistic error terms. Only low-dimensional models are possible, such as the simple linear regression model, or the one-way ANOVA model with two factor levels. Reference: Hannig, Lai & Lee (2014) <doi:10.1016/j.csda.2013.03.003>.
Author: Stéphane Laurent
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>

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New package Crossover with initial version 0.1-19
Package: Crossover
Type: Package
Title: Analysis and Search of Crossover Designs
Version: 0.1-19
Author: Kornelius Rohmeyer
Maintainer: Kornelius Rohmeyer <rohmeyer@small-projects.de>
Description: Package Crossover provides different crossover designs from combinatorial or search algorithms as well as from literature and a GUI to access them.
Depends: R (>= 3.0.2), ggplot2
Imports: MASS, crossdes (>= 1.1-1), xtable, methods, Matrix, rJava (>= 0.8-3), CommonJavaJars (>= 1.0.5), Rcpp (>= 0.10.3), RcppArmadillo (>= 0.2.0), JavaGD, multcomp, stats4, digest
Suggests: knitr, testthat, nlme
LinkingTo: Rcpp (>= 0.10.3), RcppArmadillo (>= 0.2.0)
SystemRequirements: Java (>= 5.0)
License: GPL-2
VignetteBuilder: knitr
URL: https://github.com/kornl/Crossover/wiki
BugReports: https://github.com/kornl/Crossover/issues
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2020-12-30 10:47:01 UTC; kornel
Repository: CRAN
Date/Publication: 2021-01-04 14:40:05 UTC

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Package acled.api updated to version 1.0.9 with previous version 1.0.8 dated 2020-10-27

Title: Automated Retrieval of ACLED Conflict Event Data
Description: Access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED) at <https://acleddata.com/>. The package makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow. See the package's README file for a note on replicability when drawing on ACLED data. When using this package, you acknowledge that you have read ACLED's terms and conditions of use, and that you agree with their attribution requirements.
Author: Christoph Dworschak [aut, cre] (<https://orcid.org/0000-0003-0196-9545>)
Maintainer: Christoph Dworschak <c.dworschak@essex.ac.uk>

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Package radsafer updated to version 2.2.4 with previous version 2.2.3 dated 2020-09-09

Title: Radiation Safety
Description: Provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) <doi:10.2172/1419730>).
Author: Mark Hogue <mark.hogue.chp@gmail.com>
Maintainer: Mark Hogue <mark.hogue.chp@gmail.com>

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Package StratigrapheR updated to version 1.1.2 with previous version 1.1.1 dated 2020-11-02

Title: Integrated Stratigraphy
Description: Includes bases for litholog generation: graphical functions based on R base graphics, interval management functions and svg importation functions among others. Also include stereographic projection functions, and other functions made to deal with large datasets while keeping options to get into the details of the data. When using for publication please cite Wouters, S., Da Silva, A.C. Crucifix, M., Sinnesael, M., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2019, Litholog generation with the StratigrapheR package and signal decomposition for cyclostratigraphic purposes. Geophysical Research Abstracts Vol. 21, EGU2019-5520, 2019, EGU General Assembly 2019. <http://hdl.handle.net/2268/234402> The palaeomagnetism functions are based on: Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press. <https://earthref.org/MagIC/books/Tauxe/Essentials/>; Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge University Press, 289 pp.; Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205, <doi: 10.1016/j.cageo.2012.07.021>.
Author: Sebastien Wouters [aut, cre], Adam D. Smith [ctb]
Maintainer: Sebastien Wouters <sebastien.wouters@doct.uliege.be>

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Package PlackettLuce updated to version 0.3.2 with previous version 0.3.1 dated 2020-10-13

Title: Plackett-Luce Models for Rankings
Description: Functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings may be handled by adding pseudo-rankings with a hypothetical item. Optionally, a multivariate normal prior may be set on the log-worth parameters and ranker reliabilities may be incorporated as proposed by Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a posteriori estimation is used when priors are set. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for further details.
Author: Heather Turner [aut, cre] (<https://orcid.org/0000-0002-1256-3375>), Ioannis Kosmidis [aut] (<https://orcid.org/0000-0003-1556-0302>), David Firth [aut] (<https://orcid.org/0000-0003-0302-2312>), Jacob van Etten [ctb] (<https://orcid.org/0000-0001-7554-2558>)
Maintainer: Heather Turner <ht@heatherturner.net>

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Package groundhog updated to version 1.1.0 with previous version 1.0.0 dated 2020-10-30

Title: Reproducible Scripts via Version-Specific Package Loading
Description: Make R scripts that rely on packages reproducible, by ensuring that every time a given script is run, the same version of the used packages are loaded (instead of whichever version the user running the script happens to have installed). This is achieved by using the new command groundhog.library() instead of the base command library(), and including a date in the call. The date is used to call on the same version of the package every time (the most recent version available on CRAN at that date).
Author: Uri Simonsohn [aut, cre] (<https://orcid.org/0000-0002-8601-7211>), Hugo Gruson [ctb, aut] (<https://orcid.org/0000-0002-4094-1476>)
Maintainer: Uri Simonsohn <urisohn@gmail.com>

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Package extRatum updated to version 1.0.1 with previous version 1.0.0 dated 2020-12-18

Title: Summary Statistics for Geospatial Features
Description: Provides summary statistics of local geospatial features within a given geographic area. It does so by calculating the area covered by a target geospatial feature (i.e. buildings, parks, lakes, etc.). The geospatial features can be of any type of geospatial data, including point, polygon or line data.
Author: Nikos Patias [aut, cre] (<https://orcid.org/0000-0002-6542-2330>), Francisco Rowe [aut]
Maintainer: Nikos Patias <n.patias@liverpool.ac.uk>

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Package tapkee updated to version 1.2 with previous version 1.1 dated 2019-09-18

Title: Wrapper for 'tapkee' Dimension Reduction Library
Description: Wrapper for using 'tapkee' command line utility, it allows to run it from inside R and catch the results for further analysis and plotting. 'Tapkee' is a program for fast dimension reduction, see 'package?tapkee' and <http://tapkee.lisitsyn.me/> for installation and other details.
Author: Alexey Shipunov
Maintainer: Alexey Shipunov <dactylorhiza@gmail.com>

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Package shipunov updated to version 1.13 with previous version 1.12 dated 2020-10-19

Title: Miscellaneous Functions from Alexey Shipunov
Description: A collection of functions for data manipulation, plotting and statistical computing, to use separately or with the book "Visual Statistics. Use R!": Shipunov (2020) <http://ashipunov.info/shipunov/software/r/r-en.htm>. Most useful functions: Bclust(), Jclust() and BootA() which bootstrap hierarchical clustering; Recode() which does multiple recoding in a fast, simple and flexible way; Misclass() which outputs confusion matrix even if classes are not concerted; Overlap() which measures group separation on any projection; Biarrows() which converts any scatterplot into biplot; and Pleiad() which is fast and flexible correlogram.
Author: Alexey Shipunov [aut, cre], Paul Murrell [ctb], Marcello D'Orazio [ctb], Stephen Turner [ctb], Eugeny Altshuler [ctb], Roland Rau [ctb], Marcus W Beck [ctb], Sebastian Gibb [ctb], Weiliang Qiu [ctb], Emmanuel Paradis [ctb], Roger Koenker [ctb], R Core Team [ctb]
Maintainer: Alexey Shipunov <dactylorhiza@gmail.com>

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Package RCzechia updated to version 1.6.2 with previous version 1.6.1 dated 2020-10-30

Title: Spatial Objects of the Czech Republic
Description: Administrative regions and other spatial objects of the Czech Republic.
Author: Jindra Lacko
Maintainer: Jindra Lacko <jindra.lacko@gmail.com>

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Package mixAR updated to version 0.22.5 with previous version 0.22.4 dated 2020-06-29

Title: Mixture Autoregressive Models
Description: Model time series using mixture autoregressive (MAR) models. Implemented are frequentist (EM) and Bayesian methods for estimation, prediction and model evaluation. See Wong and Li (2002) <doi:10.1111/1467-9868.00222>, Boshnakov (2009) <doi:10.1016/j.spl.2009.04.009>), and the extensive references in the documentation.
Author: Georgi N. Boshnakov [aut, cre], Davide Ravagli [aut]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package hereR updated to version 0.6.0 with previous version 0.5.2 dated 2020-11-26

Title: 'sf'-Based Interface to the 'HERE' REST APIs
Description: Interface to the 'HERE' REST APIs <https://developer.here.com/develop/rest-apis>: (1) geocode and autosuggest addresses or reverse geocode POIs using the 'Geocoder' API; (2) route directions, travel distance or time matrices and isolines using the 'Routing', 'Matrix Routing' and 'Isoline Routing' APIs; (3) request real-time traffic flow and incident information from the 'Traffic' API; (4) find request public transport connections and nearby stations from the 'Public Transit' API; (5) request intermodal routes using the 'Intermodal Routing' API; (6) get weather forecasts, reports on current weather conditions, astronomical information and alerts at a specific location from the 'Destination Weather' API. Locations, routes and isolines are returned as 'sf' objects.
Author: Merlin Unterfinger [aut, cre] (<https://orcid.org/0000-0003-2020-2366>), Daniel Possenriede [ctb] (<https://orcid.org/0000-0002-6738-9845>)
Maintainer: Merlin Unterfinger <info@munterfinger.ch>

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Package flexpolyline updated to version 0.2.1 with previous version 0.2.0 dated 2020-12-17

Title: Flexible Polyline Encoding
Description: Binding to the C++ implementation of the flexible polyline encoding by HERE <https://github.com/heremaps/flexible-polyline>. The flexible polyline encoding is a lossy compressed representation of a list of coordinate pairs or coordinate triples. The encoding is achieved by: (1) Reducing the decimal digits of each value; (2) encoding only the offset from the previous point; (3) using variable length for each coordinate delta; and (4) using 64 URL-safe characters to display the result.
Author: Merlin Unterfinger [aut, cre] (<https://orcid.org/0000-0003-2020-2366>), HERE Europe B.V. [aut, cph] (Flexible polyline encoding C++ implementation)
Maintainer: Merlin Unterfinger <info@munterfinger.ch>

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New package fdm2id with initial version 0.9.5
Package: fdm2id
Title: Data Mining and R Programming for Beginners
Version: 0.9.5
Authors@R: person("Alexandre", "Blansché", email = "alexandre.blansche@univ-lorraine.fr", role = c("aut", "cre"))
Description: Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".
Depends: R (>= 3.5.0), arules, mclust, nnet, pls
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: car, caret, class, cluster, e1071, FactoMineR, flexclust, fpc, glmnet, graphics, grDevices, ibr, irr, kohonen, leaps, MASS, mda, meanShiftR, methods, questionr, randomForest, ROCR, rpart, Rtsne, SnowballC, stats, text2vec, stopwords, utils, wordcloud, xgboost
Suggests: datasets, fds
Enhances: NMF
NeedsCompilation: no
Packaged: 2020-12-29 14:57:37 UTC; blansche
Author: Alexandre Blansché [aut, cre]
Maintainer: Alexandre Blansché <alexandre.blansche@univ-lorraine.fr>
Repository: CRAN
Date/Publication: 2021-01-04 11:30:06 UTC

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Package diffcor updated to version 0.6.3 with previous version 0.6.2 dated 2020-10-16

Title: Fisher's z-Tests Concerning Difference of Correlations
Description: Computations of Fisher's z-tests concerning differences between correlations. diffcor.one() could be used to test for differences regarding an expected value, e.g., in construct validation. diffcor.two() may be useful in replication studies, to test if the original study and the replication study differed in terms of effects. diffcor.dep() can be applied to check if the correlation between one construct with another one (r12) is significantly different/higher/smaller than the correlation of one of the constructs with a third construct (r13), given the correlation of the constructs that are compared (r23). The outputs for all the three functions provide the test statistic in z-units, p-values, and alpha levels that were corrected in terms of multiple testing according to Bonferroni. To help interpret the output, the procedure prompts if a single p value is smaller than the corrected alpha. For diffcor.one() and diffcor.two(), the effect size Cohens q is additionally provided. It is a descriptive index to evaluate differences of independent correlations. According to Cohen (1988), q = |.10|, |.30| and |.50| are considered small, moderate, and large differences, respectively.
Author: Christian Blötner
Maintainer: Christian Blötner <c.bloetner@gmail.com>

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Package aRbs updated to version 0.1.0 with previous version 0.0.1 dated 2020-11-24

Title: Find Arbitrage Opportunities for Sports Matches
Description: Money doesn't grow on trees. Arbitrage opportunities do. Find the best arbitrage opportunities (arbs) in sports matches through <https://www.oddschecker.com/>. This package allows the user to input the URLs of <https://www.oddschecker.com/> subdomains in order to extract any opportunities for arbitrage. The majority of the functionality is implemented using the function `get_arbs_shiny()`, which simply provides an easy-to-use interface wrapping the functionality of the `get_arbs()` function. This function first finds all subdomains of the URL entered, then filters to only include those that look like event betting pages. Next, odds are scraped from the various bookmakers listed for that event, before returning combinations of bookmakers, odds or outcomes that could be used for arbitrage. Bets placed subsequently are done so at bettor's risk. Please see package README for full details.
Author: Andrew Little [aut, cre]
Maintainer: Andrew Little <andrewlittlebristol@gmail.com>

Diff between aRbs versions 0.0.1 dated 2020-11-24 and 0.1.0 dated 2021-01-04

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 aRbs-0.0.1/aRbs/man/get_arbs.Rd         |only
 aRbs-0.0.1/aRbs/man/remove_form.Rd      |only
 aRbs-0.1.0/aRbs/DESCRIPTION             |   27 ++---
 aRbs-0.1.0/aRbs/MD5                     |   34 +++----
 aRbs-0.1.0/aRbs/NAMESPACE               |    3 
 aRbs-0.1.0/aRbs/NEWS.md                 |   14 ++
 aRbs-0.1.0/aRbs/R/get_arb_single.R      |  109 +++++++---------------
 aRbs-0.1.0/aRbs/R/get_arbs.R            |  155 ++++++++++++++------------------
 aRbs-0.1.0/aRbs/R/get_arbs_shiny.R      |only
 aRbs-0.1.0/aRbs/R/implied_probability.R |   21 +++-
 aRbs-0.1.0/aRbs/R/oddschecker2.R        |  106 ++++++++++++++-------
 aRbs-0.1.0/aRbs/R/print.arb.R           |    8 -
 aRbs-0.1.0/aRbs/R/scrape_links.R        |    2 
 aRbs-0.1.0/aRbs/R/test_get_arbs.R       |only
 aRbs-0.1.0/aRbs/README.md               |only
 aRbs-0.1.0/aRbs/inst                    |only
 aRbs-0.1.0/aRbs/man/get_arb_single.Rd   |   68 +++++++-------
 aRbs-0.1.0/aRbs/man/get_arbs_shiny.Rd   |only
 aRbs-0.1.0/aRbs/man/oddschecker2.Rd     |   37 ++++---
 aRbs-0.1.0/aRbs/man/print.arb.Rd        |   42 ++++----
 aRbs-0.1.0/aRbs/man/scrape_links.Rd     |   48 ++++-----
 22 files changed, 350 insertions(+), 324 deletions(-)

More information about aRbs at CRAN
Permanent link

Package alfred updated to version 0.1.9 with previous version 0.1.8 dated 2020-09-11

Title: Downloading Time Series from ALFRED Database for Various Vintages
Description: Provides direct access to the ALFRED (<https://alfred.stlouisfed.org>) and FRED (<https://fred.stlouisfed.org>) databases. Its functions return tidy data frames for different releases of the specified time series. Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.
Author: Onno Kleen [aut, cre] (<https://orcid.org/0000-0003-4731-4640>)
Maintainer: Onno Kleen <r@onnokleen.de>

Diff between alfred versions 0.1.8 dated 2020-09-11 and 0.1.9 dated 2021-01-04

 DESCRIPTION           |    9 +++++----
 MD5                   |    8 ++++----
 NEWS.md               |    1 +
 R/get_alfred_series.R |   18 +++++++++---------
 inst/doc/alfred.html  |    6 +++---
 5 files changed, 22 insertions(+), 20 deletions(-)

More information about alfred at CRAN
Permanent link


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