Fri, 08 Jan 2021

Package vizdraws updated to version 1.1 with previous version 1.0.0 dated 2020-04-28

Title: Visualize Draws from the Prior and Posterior Distributions
Description: Interactive visualization for Bayesian prior and posterior distributions. When both distributions are provided the animation shows a transition from prior to posterior. Finally, the animation splits the distribution using the provided 'breaks' into bars that show the probability for each region. If no 'breaks' are providers it will use zero by default.
Author: Ignacio Martinez [aut, cre], Linus Marco [aut], Dan Thal [aut]
Maintainer: Ignacio Martinez <ignacio@protonmail.com>

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Package vimp updated to version 2.1.6 with previous version 2.1.0 dated 2020-06-18

Title: Perform Inference on Algorithm-Agnostic Variable Importance
Description: Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (arXiv, 2020+) <arXiv:2004.03683>, and Williamson and Feng (ICML, 2020).
Author: Brian D. Williamson [aut, cre] (<https://orcid.org/0000-0002-7024-548X>), Jean Feng [ctb], Noah Simon [ths] (<https://orcid.org/0000-0002-8985-2474>), Marco Carone [ths] (<https://orcid.org/0000-0003-2106-0953>)
Maintainer: Brian D. Williamson <bwillia2@fredhutch.org>

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Package tsvr updated to version 1.0.2 with previous version 1.0.1 dated 2019-09-27

Title: Timescale-Specific Variance Ratio for Use in Community Ecology
Description: Tools for timescale decomposition of the classic variance ratio of community ecology. Tools are as described in Zhao et al (in prep), extending commonly used methods introduced by Peterson et al (1975) <doi: 10.2307/1936306>.
Author: Daniel C. Reuman [aut, cre], Lei Zhao [aut], Shaopeng Wang [aut]
Maintainer: Daniel C. Reuman <reuman@ku.edu>

Diff between tsvr versions 1.0.1 dated 2019-09-27 and 1.0.2 dated 2021-01-08

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Package MXM updated to version 1.5.0 with previous version 1.4.9 dated 2020-09-03

Title: Feature Selection (Including Multiple Solutions) and Bayesian Networks
Description: Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets. Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>. b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. <doi:10.1186/s12859-018-2023-7>. c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1): 19-30. <doi:10.1007/s41060-018-0097-y>. d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. <doi:10.1101/431734>. e) Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>. f) Tsagris, M. and Tsamardinos, I. (2019). Feature selection with the R package MXM. F1000Research 7: 1505. <doi:10.12688/f1000research.16216.2>. g) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39. h) The gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics (Accepted for publication).
Author: Michail Tsagris [aut, cre], Ioannis Tsamardinos [aut, cph], Vincenzo Lagani [aut, cph], Giorgos Athineou [aut], Giorgos Borboudakis [ctb], Anna Roumpelaki [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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Package MSEtool updated to version 3.0.0 with previous version 2.0.1 dated 2020-09-29

Title: Management Strategy Evaluation Toolkit
Description: Development, simulation testing, and implementation of management procedures for fisheries (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).
Author: Adrian Hordyk [aut, cre], Quang Huynh [aut], Tom Carruthers [aut], Chris Grandin [ctb] (iSCAM functions)
Maintainer: Adrian Hordyk <adrian@bluematterscience.com>

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Package ipmisc updated to version 5.0.2 with previous version 5.0.1 dated 2020-12-08

Title: Miscellaneous Functions for Data Cleaning and Analysis
Description: Provides functions needed for data cleaning and formatting and forms data cleaning and wrangling backend for the following packages: 'broomExtra', 'ggstatsplot', 'groupedstats', 'pairwiseComparisons', 'statsExpressions', and 'tidyBF'.
Author: Indrajeet Patil [aut, cre, cph] (<https://orcid.org/0000-0003-1995-6531>)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package SampleSize4ClinicalTrials updated to version 0.2.3 with previous version 0.2.2 dated 2020-03-25

Title: Sample Size Calculation for the Comparison of Means or Proportions in Phase III Clinical Trials
Description: There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals.
Author: Hongchao Qi, Fang Zhu
Maintainer: Hongchao Qi <hcqi1992@gmail.com>

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Package moderndive updated to version 0.5.1 with previous version 0.5.0 dated 2020-07-19

Title: Tidyverse-Friendly Introductory Linear Regression
Description: Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.
Author: Albert Y. Kim [aut, cre] (<https://orcid.org/0000-0001-7824-306X>), Chester Ismay [aut] (<https://orcid.org/0000-0003-2820-2547>), Andrew Bray [ctb] (<https://orcid.org/0000-0002-4037-7414>), Delaney Moran [ctb], Evgeni Chasnovski [ctb] (<https://orcid.org/0000-0002-1617-4019>), Will Hopper [ctb] (<https://orcid.org/0000-0002-7848-1946>), Marium Tapal [ctb] (<https://orcid.org/0000-0001-5093-6462>)
Maintainer: Albert Y. Kim <albert.ys.kim@gmail.com>

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Package BayesPostEst updated to version 0.3.1 with previous version 0.2.1 dated 2020-05-28

Title: Generate Postestimation Quantities for Bayesian MCMC Estimation
Description: An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'.
Author: Johannes Karreth [aut] (<https://orcid.org/0000-0003-4586-7153>), Shana Scogin [aut, cre] (<https://orcid.org/0000-0002-7801-853X>), Rob Williams [aut] (<https://orcid.org/0000-0001-9259-3883>), Andreas Beger [aut] (<https://orcid.org/0000-0003-1883-3169>), Myunghee Lee [ctb], Neil Williams [ctb]
Maintainer: Shana Scogin <shanarscogin@gmail.com>

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Package BayesSPsurv updated to version 0.1.2 with previous version 0.1.1 dated 2020-11-25

Title: Bayesian Spatial Split Population Survival Model
Description: Parametric spatial split-population (SP) survival models for clustered event processes. The models account for structural and spatial heterogeneity among “at risk” and “immune” populations, and incorporate time-varying covariates. This package currently implements Weibull, Exponential and Log-logistic forms for the duration component. It also includes functions for a series of diagnostic tests and plots to easily visualize spatial autocorrelation, convergence, and spatial effects. Users can create their own spatial weights matrix based on their units and adjacencies of interest, making the use of these models flexible and broadly applicable to a variety of research areas. Joo et al. (2020) <https://github.com/Nicolas-Schmidt/BayesSPsurv/blob/master/man/figures/SPcure.pdf> describe the estimators included in this package.
Author: Brandon L. Bolte [aut], Nicolas Schmidt [aut, cre], Sergio Bejar [aut], Bumba Mukherjee [aut], Minnie M. Joo [ctb], Nguyen K. Huynh [ctb]
Maintainer: Nicolas Schmidt <nschmidt@cienciassociales.edu.uy>

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Package poismf updated to version 0.2.7 with previous version 0.2.6 dated 2020-11-13

Title: Factorization of Sparse Counts Matrices Through Poisson Likelihood
Description: Creates a low-rank factorization of a sparse counts matrix by maximizing Poisson likelihood with l1/l2 regularization with all non-negative latent factors (e.g. for recommender systems or topic modeling) (Cortes, (2018) <arXiv:1811.01908>). Similar to hierarchical Poisson factorization, but follows an optimization-based approach with regularization instead of a hierarchical structure, and is fit through gradient-based methods instead of variational inference.
Author: David Cortes [aut, cre, cph], Jean-Sebastien Roy [cph], Stephen Nash [cph]
Maintainer: David Cortes <david.cortes.rivera@gmail.com>

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Package MarketMatching updated to version 1.2.0 with previous version 1.1.2 dated 2019-07-03

Title: Market Matching and Causal Impact Inference
Description: For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the 'dtw' package) to do the matching and the 'CausalImpact' package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).
Author: Larsen Kim [aut, cre]
Maintainer: Larsen Kim <kblarsen4@gmail.com>

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Package gtsummary updated to version 1.3.6 with previous version 1.3.5 dated 2020-09-29

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>), Margie Hannum [aut] (<https://orcid.org/0000-0002-2953-0449>), Karissa Whiting [aut] (<https://orcid.org/0000-0002-4683-1868>), Emily C. Zabor [aut] (<https://orcid.org/0000-0002-1402-4498>), Esther Drill [ctb] (<https://orcid.org/0000-0002-3315-4538>), Jessica Flynn [ctb] (<https://orcid.org/0000-0001-8310-6684>), Joseph Larmarange [ctb] (<https://orcid.org/0000-0001-7097-700X>), Jessica Lavery [ctb] (<https://orcid.org/0000-0002-2746-5647>), 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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Permanent link

Package elevatr updated to version 0.3.3 with previous version 0.3.1 dated 2020-11-23

Title: Access Elevation Data from Various APIs
Description: Several web services are available that provide access to elevation data. This package provides access to several of those services and returns elevation data either as a SpatialPointsDataFrame from point elevation services or as a raster object from raster elevation services. Currently, the package supports access to the Amazon Web Services Terrain Tiles <https://registry.opendata.aws/terrain-tiles/>, the Open Topography Global Datasets API <https://opentopography.org/developers/>, and the USGS Elevation Point Query Service <https://nationalmap.gov/epqs/>.
Author: Jeffrey Hollister [aut, cre] (<https://orcid.org/0000-0002-9254-9740>), Tarak Shah [ctb], Alec L. Robitaille [ctb] (<https://orcid.org/0000-0002-4706-1762>), Marcus W. Beck [rev] (<https://orcid.org/0000-0002-4996-0059>), Mike Johnson [ctb] (<https://orcid.org/0000-0002-5288-8350>)
Maintainer: Jeffrey Hollister <hollister.jeff@epa.gov>

Diff between elevatr versions 0.3.1 dated 2020-11-23 and 0.3.3 dated 2021-01-08

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 tests/testthat/test-get_elev_point.R  |   11 ++--
 tests/testthat/test-get_elev_raster.R |   13 +++--
 tests/testthat/test-internal.R        |   21 ++++-----
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More information about elevatr at CRAN
Permanent link

Package valaddin updated to version 1.0.1 with previous version 1.0.0 dated 2019-02-10

Title: Functional Input Validation
Description: A set of basic tools to transform functions into functions with input validation checks, in a manner suitable for both programmatic and interactive use.
Author: Eugene Ha [aut, cre]
Maintainer: Eugene Ha <eha@posteo.de>

Diff between valaddin versions 1.0.0 dated 2019-02-10 and 1.0.1 dated 2021-01-08

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Package stratamatch updated to version 0.1.6 with previous version 0.1.5 dated 2020-04-09

Title: Stratification and Matching for Large Observational Data Sets
Description: A pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata.
Author: Rachael C. Aikens [aut, cre], Joseph Rigdon [aut], Justin Lee [aut], Michael Baiocchi [aut], Jonathan Chen [aut]
Maintainer: Rachael C. Aikens <rockyaikens@gmail.com>

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Package ProjectTemplate updated to version 0.10.0 with previous version 0.9.3 dated 2020-12-04

Title: Automates the Creation of New Statistical Analysis Projects
Description: Provides functions to automatically build a directory structure for a new R project. Using this structure, 'ProjectTemplate' automates data loading, preprocessing, library importing and unit testing.
Author: Aleksandar Blagotic [ctb], Diego Valle-Jones [ctb], Jeffrey Breen [ctb], Joakim Lundborg [ctb], John Myles White [aut, cph], Josh Bode [ctb], Kenton White [ctb, cre], Kirill Mueller [ctb], Matteo Redaelli [ctb], Noah Lorang [ctb], Patrick Schalk [ctb], Dominik Schneider [ctb], Gerold Hepp [ctb]
Maintainer: Kenton White <jkentonwhite@gmail.com>

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Package profileModel updated to version 0.6.1 with previous version 0.6.0 dated 2019-04-03

Title: Profiling Inference Functions for Various Model Classes
Description: Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors. More information on the methods that are implemented can be found in Kosmidis (2008) <https://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf>.
Author: Ioannis Kosmidis [aut, cre] (<https://orcid.org/0000-0003-1556-0302>)
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>

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Package nofrills updated to version 0.3.1 with previous version 0.3.0 dated 2018-01-21

Title: Low-Cost Anonymous Functions
Description: Provides a compact variation of the usual syntax of function declaration, in order to support tidyverse-style quasiquotation of a function's arguments and body.
Author: Eugene Ha [aut, cre]
Maintainer: Eugene Ha <eha@posteo.de>

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Package momentuHMM updated to version 1.5.2 with previous version 1.5.1 dated 2020-03-06

Title: Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models
Description: Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
Author: Brett McClintock, Theo Michelot
Maintainer: Brett McClintock <brett.mcclintock@noaa.gov>

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Package fflr updated to version 0.3.15 with previous version 0.3.13 dated 2020-11-25

Title: Collect ESPN Fantasy Football Data
Description: Format the raw data from the ESPN fantasy football API <https://fantasy.espn.com/apis/v3/games/ffl/> into tidy tables. Scrape data on public leagues, rosters, athletes, and matches.
Author: Kiernan Nicholls [aut, cre]
Maintainer: Kiernan Nicholls <kiernann@protonmail.com>

Diff between fflr versions 0.3.13 dated 2020-11-25 and 0.3.15 dated 2021-01-08

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Package ufs updated to version 0.4.1 with previous version 0.4.0 dated 2020-11-10

Title: Quantitative Analysis Made Accessible
Description: This is a new version of the 'userfriendlyscience' package, which has grown a bit unwieldy. Therefore, distinct functionalities are being 'consciously uncoupled' into different packages. This package contains the general-purpose tools and utilities (see the 'behaviorchange' package, the 'rosetta' package, and the soon-to-be-released 'scd' package for other functionality), and is the most direct 'successor' of the original 'userfriendlyscience' package. For example, this package contains a number of basic functions to create higher level plots, such as diamond plots, to easily plot sampling distributions, to generate confidence intervals, to plan study sample sizes for confidence intervals, and to do some basic operations such as (dis)attenuate effect size estimates.
Author: Gjalt-Jorn Peters [aut, cre] (<https://orcid.org/0000-0002-0336-9589>), Stefan Gruijters [ctb] (<https://orcid.org/0000-0003-0141-0071>)
Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>

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Package Tplyr updated to version 0.3.1 with previous version 0.2.2 dated 2020-11-30

Title: A Grammar of Clinical Data Summary
Description: A tool created to simplify the data manipulation necessary to create clinical reports.
Author: Eli Miller [aut] (<https://orcid.org/0000-0002-2127-9456>), Mike Stackhouse [aut, cre] (<https://orcid.org/0000-0001-6030-723X>), Ashley Tarasiewicz [aut], Nathan Kosiba [aut] (<https://orcid.org/0000-0001-5359-4234>), Atorus Research LLC [cph]
Maintainer: Mike Stackhouse <mike.stackhouse@atorusresearch.com>

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Package splithalf updated to version 0.7.2 with previous version 0.7.1 dated 2020-03-18

Title: Calculate Task Split Half Reliability Estimates
Description: Estimate the internal consistency of your tasks with a permutation based split-half reliability approach. Unofficial release name: "Kitten Mittens".
Author: Sam Parsons [aut, cre]
Maintainer: Sam Parsons <sam.parsons@psy.ox.ac.uk>

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Package compositions updated to version 2.0-1 with previous version 2.0-0 dated 2020-07-14

Title: Compositional Data Analysis
Description: Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
Author: K. Gerald van den Boogaart <boogaart@hzdr.de>, Raimon Tolosana-Delgado, Matevz Bren
Maintainer: K. Gerald van den Boogaart <support@boogaart.de>

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Package W2CWM2C updated to version 2.2 with previous version 2.0 dated 2015-08-05

Title: A Graphical Tool for Wavelet (Cross) Correlation and Wavelet Multiple (Cross) Correlation Analysis
Description: Set of functions that improves the graphical presentations of the functions: wave.correlation and spin.correlation (waveslim package, Whitcher 2012) and the wave.multiple.correlation and wave.multiple.cross.correlation (wavemulcor package, Fernandez-Macho 2012b). The plot outputs (heatmaps) can be displayed in the screen or can be saved as PNG or JPG images or as PDF or EPS formats. The W2CWM2C package also helps to handle the (input data) multivariate time series easily as a list of N elements (times series) and provides a multivariate data set (dataexample) to exemplify its use. A description of the package was published in a scientific paper: Polanco-Martinez and Fernandez-Macho (2014), <doi:10.1109/MCSE.2014.96>.
Author: Josue M. Polanco-Martinez [aut, cre] (<https://orcid.org/0000-0001-7164-0185>)
Maintainer: Josue M. Polanco-Martinez <josue.m.polanco@gmail.com>

Diff between W2CWM2C versions 2.0 dated 2015-08-05 and 2.2 dated 2021-01-08

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Package RSQLite updated to version 2.2.2 with previous version 2.2.1 dated 2020-09-30

Title: 'SQLite' Interface for R
Description: Embeds the 'SQLite' database engine in R and provides an interface compliant with the 'DBI' package. The source for the 'SQLite' engine is included.
Author: Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), Hadley Wickham [aut], David A. James [aut], Seth Falcon [aut], SQLite Authors [ctb] (for the included SQLite sources), Liam Healy [ctb] (for the included SQLite sources), R Consortium [fnd], RStudio [cph]
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

Diff between RSQLite versions 2.2.1 dated 2020-09-30 and 2.2.2 dated 2021-01-08

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

Title: Factor Big Integers with the Parallel Quadratic Sieve
Description: Features the multiple polynomial quadratic sieve (MPQS) algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. The MPQS is based off of the seminal work of Carl Pomerance (1984) <doi:10.1007/3-540-39757-4_17> along with the modification of multiple polynomials introduced by Peter Montgomery and J. Davis as outlined by Robert D. Silverman (1987) <doi:10.1090/S0025-5718-1987-0866119-8>. Utilizes the C library GMP (GNU Multiple Precision Arithmetic) and 'RcppThread' for factoring integers in parallel. For smaller integers, a simple Elliptic Curve algorithm is attempted followed by a constrained version of Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm used by the factorize function in the 'gmp' package.
Author: Joseph Wood [aut, cre], Free Software Foundation, Inc. [cph], Mike Tryczak [ctb]
Maintainer: Joseph Wood <jwood000@gmail.com>

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Package r5r updated to version 0.3-2 with previous version 0.3-1 dated 2021-01-07

Title: Rapid Realistic Routing with 'R5'
Description: Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using 'R5', the Rapid Realistic Routing on Real-world and Reimagined networks <https://github.com/conveyal/r5>. The package allows users to generate detailed routing analysis or calculate travel time matrices using seamless parallel computing on top of the R5 Java machine.
Author: Marcus Saraiva [aut] (<https://orcid.org/0000-0001-6218-2338>), Rafael H. M. Pereira [aut, cre] (<https://orcid.org/0000-0003-2125-7465>), Daniel Herszenhut [aut] (<https://orcid.org/0000-0001-8066-1105>), Carlos Kaue Vieira Braga [aut] (<https://orcid.org/0000-0002-6104-7297>), Matthew Wigginton Conway [ctb], Ipea - Institue for Applied Economic Research [cph, fnd]
Maintainer: Rafael H. M. Pereira <rafa.pereira.br@gmail.com>

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Package corr2D updated to version 1.0.2 with previous version 1.0.0 dated 2019-07-29

Title: Implementation of 2D Correlation Analysis in R
Description: Implementation of two-dimensional (2D) correlation analysis based on the Fourier-transformation approach described by Isao Noda (I. Noda (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot functions for the resulting correlation matrix: The first one creates colored 2D plots, while the second one generates 3D plots.
Author: Robert Geitner [cre, aut], Robby Fritzsch [aut], Thomas Bocklitz [aut], Juergen Popp [ctb, cph]
Maintainer: Robert Geitner <robert.geitner@uni-jena.de>

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Package copCAR updated to version 2.0-4 with previous version 2.0-3 dated 2020-05-08

Title: Fitting the copCAR Regression Model for Discrete Areal Data
Description: Provides tools for fitting the copCAR (Hughes, 2015) <DOI:10.1080/10618600.2014.948178> regression model for discrete areal data. Three types of estimation are supported (continuous extension, composite marginal likelihood, and distributional transform), for three types of outcomes (Bernoulli, negative binomial, and Poisson).
Author: Emily Goren <emily.goren@gmail.com> and John Hughes <drjphughesjr@gmail.com>
Maintainer: John Hughes <drjphughesjr@gmail.com>

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Package act updated to version 1.0 with previous version 0.94 dated 2020-11-04

Title: Aligned Corpus Toolkit
Description: The Aligned Corpus Toolkit (act) is designed for linguists that work with time aligned transcription data. It offers functions to import and export various annotation file formats ('ELAN' .eaf, 'EXMARaLDA .exb and 'Praat' .TextGrid files), create print transcripts in the style of conversation analysis, search transcripts (span searches across multiple annotations, search in normalized annotations, make concordances etc.), export and re-import search results (.csv and 'Excel' .xlsx format), create cuts for the search results (print transcripts, audio/video cuts using 'FFmpeg' and video sub titles in 'Subrib title' .srt format), modify the data in a corpus (search/replace, delete, filter etc.), interact with 'Praat' using 'Praat'-scripts, and exchange data with the 'rPraat' package. The package is itself written in R and may be expanded by other users.
Author: Oliver Ehmer [aut, cre]
Maintainer: Oliver Ehmer <oliver.ehmer@romanistik.uni-freiburg.de>

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Package SoilHyP updated to version 0.1.5 with previous version 0.1.4 dated 2020-05-19

Title: Soil Hydraulic Properties
Description: Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7]. Main references: [1] van Genuchten (1980) <doi:10.2136/sssaj1980.03615995004400050002x>, [2] Mualem (1976) <doi:10.1029/WR012i003p00513>, [3] Peters (2013) <doi:10.1002/wrcr.20548>, [4] Iden and Durner (2013) <doi:10.1002/2014WR015937>, [5] Peters (2014) <doi:10.1002/2014WR015937>, [6] Wind G. P. (1966), [7] Peters and Durner (2008) <doi:10.1016/j.jhydrol.2008.04.016> and [8] Brooks and Corey (1964).
Author: Ullrich Dettmann [aut, cre], Felix Andrews [ctb] (For the code copied from the hydromad::SCEoptim function (Version: 0.9-15) which is not on r-cran (https://github.com/floybix/hydromad). The SCEoptim function is adapted, and substantially revised from Brecht Donckels MATLAB code which is in turn adopted from Qingyun Duans MATLAB code), Brecht Donckels [ctb] (For the Matlab code which was adapted and reviesed in the hydromad::SCEoptim function.), Qingyun Duan [ctb] (For the MATLAB code adapted from Brecht Donkels.)
Maintainer: Ullrich Dettmann <ullrich.dettmann@thuenen.de>

Diff between SoilHyP versions 0.1.4 dated 2020-05-19 and 0.1.5 dated 2021-01-08

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Package readsdr updated to version 0.2.0 with previous version 0.1.0 dated 2020-06-12

Title: Translate Models from System Dynamics Software into 'R'
Description: The goal of 'readsdr' is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by 'R' libraries. The package accomplishes this goal by parsing 'XMILE' files ('Vensim' and 'Stella') models into 'R' objects to construct networks (graph theory); 'ODE' functions for 'Stan'; and inputs to simulate via 'deSolve' as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.
Author: Jair Andrade [aut, cre] (<https://orcid.org/0000-0002-1412-7868>)
Maintainer: Jair Andrade <jair.albert.andrade@gmail.com>

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More information about readsdr at CRAN
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Package ggridges updated to version 0.5.3 with previous version 0.5.2 dated 2020-01-12

Title: Ridgeline Plots in 'ggplot2'
Description: Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
Author: Claus O. Wilke [aut, cre] (<https://orcid.org/0000-0002-7470-9261>)
Maintainer: Claus O. Wilke <wilke@austin.utexas.edu>

Diff between ggridges versions 0.5.2 dated 2020-01-12 and 0.5.3 dated 2021-01-08

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 man/Aus_athletes.Rd                                                                        |    4 
 man/Catalan_elections.Rd                                                                   |    6 
 man/figures/README-diamonds-1.png                                                          |binary
 man/lincoln_weather.Rd                                                                     |    6 
 man/stat_binline.Rd                                                                        |   16 
 tests/figs/deps.txt                                                                        |    2 
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More information about ggridges at CRAN
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Package circlize updated to version 0.4.12 with previous version 0.4.11 dated 2020-10-31

Title: Circular Visualization
Description: Circular layout is an efficient way for the visualization of huge amounts of information. Here this package provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of the package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multiple dimensional data. The package is described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>

Diff between circlize versions 0.4.11 dated 2020-10-31 and 0.4.12 dated 2021-01-08

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More information about circlize at CRAN
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Package segregation updated to version 0.4.0 with previous version 0.3.0 dated 2019-09-20

Title: Entropy-Based Segregation Indices
Description: Computes entropy-based segregation indices, as developed by Theil (1971) <isbn:978-0471858454>, with a focus on the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>, is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. Includes standard error estimation by bootstrapping.
Author: Benjamin Elbers [aut, cre] (<https://orcid.org/0000-0001-5392-3448>)
Maintainer: Benjamin Elbers <be2239@columbia.edu>

Diff between segregation versions 0.3.0 dated 2019-09-20 and 0.4.0 dated 2021-01-08

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More information about segregation at CRAN
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New package tabulator with initial version 1.0.0
Package: tabulator
Title: Efficient Tabulation with Stata-Like Output
Version: 1.0.0
Authors@R: person("Sean", "Higgins", email = "sean.higgins@kellogg.northwestern.edu", role = c("aut", "cre"))
Description: Efficient tabulation with Stata-like output. For each unique value of the variable, it shows the number of observations with that value, proportion of observations with that value, and cumulative proportion, in descending order of frequency. Accepts data.table, tibble, or data.frame as input. Efficient with big data: if you give it a data.table, tab() uses data.table syntax.
Imports: assertthat, dplyr, data.table, magrittr, purrr, rlang, stats, stringr, tibble, tidyr
Depends: R (>= 3.4.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-06 22:03:57 UTC; cesarlandin
Author: Sean Higgins [aut, cre]
Maintainer: Sean Higgins <sean.higgins@kellogg.northwestern.edu>
Repository: CRAN
Date/Publication: 2021-01-08 13:20:02 UTC

More information about tabulator at CRAN
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New package tablet with initial version 0.2.0
Package: tablet
Type: Package
Title: Tabulate Descriptive Statistics in Multiple Formats
Version: 0.2.0
Author: Tim Bergsma
Maintainer: Tim Bergsma <bergsmat@gmail.com>
Description: Creates a table of descriptive statistics for factor and numeric columns in a data frame. Displays these by groups, if any. Highly customizable, with support for 'html' and 'pdf' provided by 'kableExtra'. Respects original column order, column labels, and factor level order. See ?tablet.data.frame and vignettes.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: dplyr (>= 1.0.2), rlang, tidyr, kableExtra (>= 0.9.0), tidyselect
RoxygenNote: 7.1.1
VignetteBuilder: knitr
Suggests: knitr, magrittr, rmarkdown, yamlet, boot, testthat
NeedsCompilation: no
Packaged: 2021-01-06 23:10:06 UTC; tim.bergsma
Repository: CRAN
Date/Publication: 2021-01-08 13:30:03 UTC

More information about tablet at CRAN
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Package support.CEs updated to version 0.5-0 with previous version 0.4-1 dated 2015-08-27

Title: Basic Functions for Supporting an Implementation of Choice Experiments
Description: Provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831> for details on CEs, and Aizaki (2012) <doi:10.18637/jss.v050.c02> for the package.
Author: Hideo Aizaki
Maintainer: Hideo Aizaki <azk-r@spa.nifty.com>

Diff between support.CEs versions 0.4-1 dated 2015-08-27 and 0.5-0 dated 2021-01-08

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Package speedglm updated to version 0.3-3 with previous version 0.3-2 dated 2017-01-09

Title: Fitting Linear and Generalized Linear Models to Large Data Sets
Description: Fitting linear models and generalized linear models to large data sets by updating algorithms.
Author: Marco Enea [aut, cre], Ronen Meiri [ctb] (on behalf of DMWay Analytics LTD), Tomer Kalimi [ctb] (on behalf of DMWay Analytics LTD)
Maintainer: Marco Enea <emarco76@libero.it>

Diff between speedglm versions 0.3-2 dated 2017-01-09 and 0.3-3 dated 2021-01-08

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New package read.gt3x with initial version 1.0.1
Package: read.gt3x
Type: Package
Title: Parse 'Actigraph' 'GT3X'/'GT3X+' 'Accelerometer' Data
Version: 1.0.1
Authors@R: c(person(given = "Tuomo", family = "Nieminen", role = c("aut", "cre"), email = "tuomo.a.nieminen@gmail.com"), person(given = "John", family = "Muschelli", role = "aut", email = "muschellij2@gmail.com", comment = c(ORCID = "0000-0001-6469-1750")))
Description: Implements a high performance C++ parser for 'ActiGraph' 'GT3X'/'GT3X+' data format (with extension '.gt3x') for 'accelerometer' samples. Activity samples can be easily read into a matrix or data.frame. This allows for storing the raw 'accelerometer' samples in the original binary format to reserve space.
License: EUPL
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp
Imports: Rcpp, utils, R.utils, tools
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), data.table, zoo, readr, lubridate, zip
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2021-01-06 22:23:54 UTC; tuomo
Author: Tuomo Nieminen [aut, cre], John Muschelli [aut] (<https://orcid.org/0000-0001-6469-1750>)
Maintainer: Tuomo Nieminen <tuomo.a.nieminen@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-08 13:20:05 UTC

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Package powerSurvEpi updated to version 0.1.1 with previous version 0.1.0 dated 2018-02-07

Title: Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies
Description: Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials.
Author: Weiliang Qiu, Jorge Chavarro, Ross Lazarus, Bernard Rosner, Jing Ma.
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>

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New package matrixdist with initial version 1.0
Package: matrixdist
Type: Package
Title: Statistics for Matrix Distributions
Version: 1.0
Date: 2020-12-12
Authors@R: c(person("Martin", "Bladt", email = "martinbladt@gmail.com", role = c("aut", "cre")), person("Jorge", "Yslas", email = "jorge.yslas1@gmail.com", role = "aut") )
Maintainer: Martin Bladt <martinbladt@gmail.com>
Description: Tools for homogeneous and in-homogeneous phase-type distributions. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996) <https://www.jstor.org/stable/4616418>, Olsson, M. (1996) <https://www.jstor.org/stable/4616419>. Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60> Albrecher, H., Bladt, M., & Yslas, J. (2020) <doi:10.1111/sjos.12505> Bladt, M., & Yslas, J. (2020) <arXiv:abs/2011.03219>.
Depends: R (>= 3.1.0)
License: GPL-3
Imports: Rcpp, methods
LinkingTo: Rcpp
SystemRequirements: C++11
Encoding: UTF-8
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-01-07 01:26:20 UTC; martinbladt
Author: Martin Bladt [aut, cre], Jorge Yslas [aut]
Repository: CRAN
Date/Publication: 2021-01-08 13:40:02 UTC

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Package greybox updated to version 0.6.6 with previous version 0.6.4 dated 2020-12-01

Title: Toolbox for Model Building and Forecasting
Description: Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK), Yves R. Sagaert [ctb] (Visiting Research at Centre for Marketing Analytics and Forecasting, Lancaster University, UK)
Maintainer: Ivan Svetunkov <ivan@svetunkov.ru>

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New package mactivate with initial version 0.6.4
Package: mactivate
Type: Package
Title: Multiplicative Activation
Version: 0.6.4
Date: 2021-01-06
Author: Dave Zes
Maintainer: Dave Zes <zesdave@gmail.com>
Description: Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models. M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model. M-activation can detect high-order interactions -- a traditionally non-trivial challenge. Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."
License: GPL (>= 3)
Depends: R (>= 3.5.0)
NeedsCompilation: yes
Packaged: 2021-01-06 20:31:31 UTC; davezes
Repository: CRAN
Date/Publication: 2021-01-08 12:50:02 UTC

More information about mactivate at CRAN
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Package metaBMA updated to version 0.6.6 with previous version 0.6.5 dated 2020-10-29

Title: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis
Description: Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2020, <doi:10.31234/osf.io/97qup>).
Author: Daniel W. Heck [aut, cre] (<https://orcid.org/0000-0002-6302-9252>), Quentin F. Gronau [ctb], Eric-Jan Wagenmakers [ctb]
Maintainer: Daniel W. Heck <dheck@uni-marburg.de>

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Package flightplanning (with last version 0.8.3) was removed from CRAN

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

2020-10-29 0.8.3
2020-03-13 0.8.0
2019-08-06 0.7.2

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Package geozoning (with last version 1.0.0) was removed from CRAN

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

2018-02-08 1.0.0

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Package kuniezu (with last version 0.1.1) was removed from CRAN

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

2020-05-23 0.1.1
2020-05-08 0.1.0

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Package VancouvR (with last version 0.1.1) was removed from CRAN

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

2019-11-15 0.1.1

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Package ctsemOMX (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:

2020-07-07 1.0.2

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Package TreeBUGS updated to version 1.4.7 with previous version 1.4.5 dated 2020-05-31

Title: Hierarchical Multinomial Processing Tree Modeling
Description: User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7>.
Author: Daniel W. Heck [aut, cre] (<https://orcid.org/0000-0002-6302-9252>), Nina R. Arnold [aut, dtc], Denis Arnold [aut], Alexander Ly [ctb], Marius Barth [ctb] (<https://orcid.org/0000-0002-3421-6665>)
Maintainer: Daniel W. Heck <dheck@uni-marburg.de>

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New package RandomForestsGLS with initial version 0.1.0
Package: RandomForestsGLS
Type: Package
Title: Random Forests for Dependent Data
Version: 0.1.0
Authors@R: c(person("Arkajyoti", "Saha", role=c("aut", "cre"), email="arkajyotisaha93@gmail.com"), person("Sumanta", "Basu", role="aut", email="sumbose@cornell.edu"), person("Abhirup", "Datta", role="aut", email="abhidatta@jhu.edu"))
Maintainer: Arkajyoti Saha <arkajyotisaha93@gmail.com>
Author: Arkajyoti Saha [aut, cre], Sumanta Basu [aut], Abhirup Datta [aut]
Depends: R (>= 3.3.0)
Imports: BRISC, parallel, stats, matrixStats, randomForest, pbapply
Suggests: knitr, rmarkdown, ggplot2, testthat (>= 2.1.0)
Description: Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2020) <arXiv:2007.15421>.
License: GPL (>= 2)
URL: https://github.com/ArkajyotiSaha/RandomForestsGLS
BugReports: https://github.com/ArkajyotiSaha/RandomForestsGLS/issues
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2021-01-06 18:00:38 UTC; arkajyotisaha
Repository: CRAN
Date/Publication: 2021-01-08 10:50:02 UTC

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Package pkgndep updated to version 1.0.2 with previous version 1.0.1 dated 2020-11-12

Title: Check the Heaviness of Package Dependencies
Description: It checks the heaviness of the packages that user's package depends on. For each package listed in the "Depends", "Imports" and "Suggests" fields in the DESCRIPTION file, it opens a new R session, loads the package and counts the number of namespaces that are loaded. The summary of the dependencies is visualized by a customized heatmap. Examples of dependency analysis can be found at <https://jokergoo.github.io/pkgndep/stat/>.
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>

Diff between pkgndep versions 1.0.1 dated 2020-11-12 and 1.0.2 dated 2021-01-08

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Package moonBook updated to version 0.2.4 with previous version 0.2.3 dated 2018-07-23

Title: Functions and Datasets for the Book by Keon-Woong Moon
Description: Several analysis-related functions for the book entitled "R statistics and graph for medical articles" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions.
Author: Keon-Woong Moon [aut, cre]
Maintainer: Keon-Woong Moon <cardiomoon@gmail.com>

Diff between moonBook versions 0.2.3 dated 2018-07-23 and 0.2.4 dated 2021-01-08

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New package KPC with initial version 0.1.0
Package: KPC
Type: Package
Title: Kernel Partial Correlation Coefficient
Version: 0.1.0
Authors@R: c(person("Zhen", "Huang", email = "zh2395@columbia.edu", role = c("aut", "cre")), person("Nabarun", "Deb", email = "nd2560@columbia.edu", role = "ctb"), person("Bodhisattva", "Sen", email = "bodhi@stat.columbia.edu", role = "ctb"))
Maintainer: Zhen Huang <zh2395@columbia.edu>
Description: Implementations of two empirical versions the kernel partial correlation (KPC) coefficient and the associated variable selection algorithms. KPC is a measure of the strength of conditional association between Y and Z given X, with X, Y, Z being random variables taking values in general topological spaces. As the name suggests, KPC is defined in terms of kernels on reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is consistent under very weak conditions. The other empirical estimator, defined using conditional mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm based on the RKHS based estimator. For more details on KPC, its empirical estimators and its application on variable selection, see Huang, Z., N. Deb, and B. Sen (2020). “Kernel partial correlation coefficient – a measure of conditional dependence” <arXiv:2012.14804>. When X is empty, KPC measures the unconditional dependence between Y and Z, which has been described in Deb, N., P. Ghosal, and B. Sen (2020), “Measuring association on topological spaces using kernels and geometric graphs” <arXiv:2010.01768>, and it is implemented in the functions KMAc() and Klin() in this package. The latter can be computed in near linear time.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 3.6.0), data.table, kernlab
Imports: RANN, proxy, parallel, gmp, emstreeR
NeedsCompilation: no
Packaged: 2021-01-06 18:49:32 UTC; huangzhen
Author: Zhen Huang [aut, cre], Nabarun Deb [ctb], Bodhisattva Sen [ctb]
Repository: CRAN
Date/Publication: 2021-01-08 10:50:05 UTC

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Package genieclust updated to version 0.9.8 with previous version 0.9.7 dated 2021-01-07

Title: The Genie++ Hierarchical Clustering Algorithm with Noise Points Detection
Description: A retake on the Genie algorithm - a robust hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). Now faster and more memory efficient; determining the whole hierarchy for datasets of 10M points in low dimensional Euclidean spaces or 100K points in high-dimensional ones takes only 1-2 minutes. Allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of 'HDBSCAN*' (that is able to detect a predefined number of clusters and hence it does not dependent on the somewhat fragile 'eps' parameter). The package also features an implementation of economic inequity indices (the Gini, Bonferroni index) and external cluster validity measures (partition similarity scores; e.g., the adjusted Rand, Fowlkes-Mallows, adjusted mutual information, pair sets index). See also the 'Python' version of 'genieclust' available on 'PyPI', which supports sparse data, more metrics, and even larger datasets.
Author: Marek Gagolewski [aut, cre, cph] (<https://orcid.org/0000-0003-0637-6028>), Maciej Bartoszuk [ctb], Anna Cena [ctb], Peter M. Larsen [ctb]
Maintainer: Marek Gagolewski <marek@gagolewski.com>

Diff between genieclust versions 0.9.7 dated 2021-01-07 and 0.9.8 dated 2021-01-08

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New package GBcurves with initial version 0.1.0
Package: GBcurves
Type: Package
Title: Yield Curves of Brazil, China, and Russia
Version: 0.1.0
Date: 2021-01-06
Authors@R: person(given = "Werley", family="Cordeiro", email = "werleycordeiro@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8394-0417"))
Maintainer: Werley Cordeiro <werleycordeiro@gmail.com>
Description: Downloads and interpolates the Brazilian, Chinese, and Russian yield curves directly from <http://www.b3.com.br/>, <http://yield.chinabond.com.cn>, and <https://www.cbr.ru>, respectively.
Depends: R (>= 3.3.0)
License: GPL-2
Encoding: UTF-8
BugReports: https://github.com/werleycordeiro/GBcurves/issues
URL: https://github.com/werleycordeiro/GBcurves/
LazyData: true
Imports: curl, functional, httr, magrittr, readxl, rvest, stats, utils, xml2, xts
Suggests: knitr,rmarkdown,testthat
RoxygenNote: 6.1.1.9000
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-01-06 16:57:45 UTC; Maria Izabel
Author: Werley Cordeiro [aut, cre] (<https://orcid.org/0000-0001-8394-0417>)
Repository: CRAN
Date/Publication: 2021-01-08 10:20:02 UTC

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Package forestinventory updated to version 1.0.0 with previous version 0.3.1 dated 2017-10-16

Title: Design-Based Global and Small-Area Estimations for Multiphase Forest Inventories
Description: Extensive global and small-area estimation procedures for multiphase forest inventories under the design-based Monte-Carlo approach are provided. The implementation has been published in the Journal of Statistical Software (<doi:10.18637/jss.v097.i04>) and includes estimators for simple and cluster sampling published by Daniel Mandallaz in 2007 (<doi:10.1201/9781584889779>), 2013 (<doi:10.1139/cjfr-2012-0381>, <doi:10.1139/cjfr-2013-0181>, <doi:10.1139/cjfr-2013-0449>, <doi:10.3929/ethz-a-009990020>) and 2016 (<doi:10.3929/ethz-a-010579388>). It provides point estimates, their external- and design-based variances and confidence intervals, as well as a set of functions to analyze and visualize the produced estimates. The procedures have also been optimized for the use of remote sensing data as auxiliary information, as demonstrated in 2018 by Hill et al. (<doi:10.3390/rs10071052>).
Author: Andreas Hill [aut, cre], Alexander Massey [aut], Daniel Mandallaz [ctb]
Maintainer: Andreas Hill <andreas.hill@wald-rlp.de>

Diff between forestinventory versions 0.3.1 dated 2017-10-16 and 1.0.0 dated 2021-01-08

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New package eatRep with initial version 0.13.4
Package: eatRep
Type: Package
Title: Educational Assessment Tools for Replication Methods
Version: 0.13.4
Depends: R (>= 3.5.0), survey, BIFIEsurvey, progress, lavaan (>= 0.6-7)
Imports: Hmisc, fmsb, mice (>= 2.46), boot, car, reshape2, plyr, combinat, miceadds, tidyr, EffectLiteR, estimatr, eatTools (>= 0.3.0), eatGADS, janitor, msm
Date: 2021-01-04
Authors@R: c( person("Sebastian", "Weirich", email = "sebastian.weirich@iqb.hu-berlin.de", role = c("aut", "cre")), person("Martin", "Hecht", role = c("aut")), person("Benjamin", "Becker", role = c("aut")))
Description: Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles and generalized linear models) in complex survey designs comprising multiple imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. See the package documentation for a more detailed description along with references.
License: GPL (>= 2)
LazyLoad: yes
LazyData: yes
NeedsCompilation: no
Packaged: 2021-01-06 15:27:44 UTC; Sebastian Weirich
Suggests: testthat
Author: Sebastian Weirich [aut, cre], Martin Hecht [aut], Benjamin Becker [aut]
Maintainer: Sebastian Weirich <sebastian.weirich@iqb.hu-berlin.de>
Repository: CRAN
Date/Publication: 2021-01-08 10:30:09 UTC

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New package CauchyCP with initial version 0.1.0
Package: CauchyCP
Type: Package
Title: Powerful Test for Survival Data under Non-Proportional Hazards
Version: 0.1.0
Author: Hong Zhang
Maintainer: Hong Zhang <hzhang@wpi.edu>
Description: An omnibus test of change-point Cox regression models to improve the statistical power of detecting signals of non-proportional hazards patterns. The technical details can be found in Hong Zhang, Qing Li, Devan Mehrotra and Judong Shen (2021) <arXiv:2101.00059>. Extensive simulation studies demonstrate that, compared to existing tests under non-proportional hazards, the proposed CauchyCP test 1) controls the type I error better at small alpha levels; 2) increases the power of detecting time-varying effects; and 3) is more computationally efficient.
License: GPL-2
Imports: stats, survival
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2021-01-06 18:53:37 UTC; consi
Repository: CRAN
Date/Publication: 2021-01-08 11:00:09 UTC

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New package vimpclust with initial version 0.1.0
Package: vimpclust
Type: Package
Title: Variable Importance in Clustering
Version: 0.1.0
Authors@R: c(person(given = "Alex", family = "Mourer", role = "aut", email = "mourer.alex@gmail.com"), person(given = "Marie", family = "Chavent", role = c("aut", "ths"), email = "marie.chavent@u-bordeaux.fr"), person(given = "Madalina", family = "Olteanu", role = c("aut", "ths", "cre"), email = "madalina.olteanu@dauphine.psl.eu"))
Description: An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: PCAmixdata, ggplot2, Polychrome, mclust, rlang
Suggests: knitr, rmarkdown
VignetteBuilder: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2020-12-18 13:36:56 UTC; administrateur
Author: Alex Mourer [aut], Marie Chavent [aut, ths], Madalina Olteanu [aut, ths, cre]
Maintainer: Madalina Olteanu <madalina.olteanu@dauphine.psl.eu>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2021-01-08 09:30:03 UTC

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Package treemapify updated to version 2.5.5 with previous version 2.5.4 dated 2020-11-19

Title: Draw Treemaps in 'ggplot2'
Description: Provides 'ggplot2' geoms for drawing treemaps.
Author: David Wilkins [aut, cre], Bob Rudis [ctb] (<https://orcid.org/0000-0001-5670-2640>)
Maintainer: David Wilkins <david@wilkox.org>

Diff between treemapify versions 2.5.4 dated 2020-11-19 and 2.5.5 dated 2021-01-08

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Package sigminer updated to version 1.2.1 with previous version 1.1.0 dated 2020-11-11

Title: Extract, Analyze and Visualize Mutational Signatures for Genomic Variations
Description: Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called 'signature' (Wang, Shixiang, et al. (2020) <DOI:10.1101/2020.04.27.20082404> & Alexandrov, Ludmil B., et al. (2020) <DOI:10.1038/s41586-020-1943-3> & Macintyre, Geoff, et al. (2018) <DOI:10.1038/s41588-018-0179-8>). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study.
Author: Shixiang Wang [aut, cre] (<https://orcid.org/0000-0001-9855-7357>), Ziyu Tao [aut] (<https://orcid.org/0000-0003-3272-1227>), Huimin Li [aut] (<https://orcid.org/0000-0003-1683-9057>), Tao Wu [aut] (<https://orcid.org/0000-0002-8999-9628>), Xue-Song Liu [aut, ctb] (<https://orcid.org/0000-0002-7736-0077>), Geoffrey Macintyre [ctb], Anand Mayakonda [ctb]
Maintainer: Shixiang Wang <w_shixiang@163.com>

Diff between sigminer versions 1.1.0 dated 2020-11-11 and 1.2.1 dated 2021-01-08

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New package sfcr with initial version 0.1.1
Package: sfcr
Title: Simulate Stock-Flow Consistent Models
Version: 0.1.1
Authors@R: person(given = "Joao", family = "Macalos", role = c("aut", "cre"), email = "joaomacalos@gmail.com", comment = c(ORCID = "https://orcid.org/0000-0001-6050-6394"))
Description: Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988, ISBN:0-387-96614-5).
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
URL: https://github.com/joaomacalos/sfcr
BugReports: https://github.com/joaomacalos/sfcr/issues
Imports: dplyr (>= 1.0.2), expm (>= 0.999.5), forcats (>= 0.5.0), igraph (>= 1.2.6), kableExtra (>= 1.3.1), magrittr (>= 1.5), purrr (>= 0.3.4), Rdpack (>= 2.1), rootSolve (>= 1.8.2.1), rlang (>= 0.4.7), tibble (>= 3.0.3), tidyr (>= 1.1.2), tidyselect (>= 1.1.0), stringr (>= 1.4.0), utils, vctrs (>= 0.3.5), Rcpp (>= 1.0.5)
Suggests: ggraph, ggplot2, grDevices, knitr, pkgdown, rmarkdown, RColorBrewer, testthat (>= 2.3.2), tidygraph, tidyverse, networkD3
VignetteBuilder: knitr
RdMacros: Rdpack
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2021-01-06 09:13:23 UTC; JouJo
Author: Joao Macalos [aut, cre] (<https://orcid.org/0000-0001-6050-6394>)
Maintainer: Joao Macalos <joaomacalos@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-08 09:30:06 UTC

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Package RIA updated to version 1.4.3 with previous version 1.4.2 dated 2019-04-09

Title: Radiomics Image Analysis Toolbox for Medial Images
Description: Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI and nrrd file formats. RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and geometry-based statistics. Almost all calculations are done using vectorized formulas to optimize run speeds. Calculation of several thousands of parameters only takes minutes on a single core of a conventional PC.
Author: Marton Kolossvary [aut, cre]
Maintainer: Marton Kolossvary <marton.kolossvary@gmail.com>

Diff between RIA versions 1.4.2 dated 2019-04-09 and 1.4.3 dated 2021-01-08

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More information about RIA at CRAN
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New package mgss with initial version 1.0
Package: mgss
Type: Package
Title: A Matrix-Free Multigrid Preconditioner for Spline Smoothing
Version: 1.0
Authors@R: c( person("Martin", "Siebenborn", role = c("aut", "cre", "cph"), email = "martin.siebenborn@uni-hamburg.de"), person("Julian", "Wagner", role = c("aut", "cph"), email = "juwagn89@gmail.com") )
Description: Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2019) <arXiv:1901.00654>.
URL: https://arxiv.org/abs/1901.00654
License: MIT + file LICENSE
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.5), combinat (>= 0.0-8), statmod (>= 1.1)
LinkingTo: Rcpp
RoxygenNote: 7.1.1
Encoding: UTF-8
Repository: CRAN
Suggests: testthat
BugReports: https://github.com/SplineSmoothing/MGSS
NeedsCompilation: yes
Packaged: 2021-01-06 06:19:16 UTC; martin
Author: Martin Siebenborn [aut, cre, cph], Julian Wagner [aut, cph]
Maintainer: Martin Siebenborn <martin.siebenborn@uni-hamburg.de>
Date/Publication: 2021-01-08 09:30:09 UTC

More information about mgss at CRAN
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Package intsurv updated to version 0.2.2 with previous version 0.2.1 dated 2019-12-17

Title: Integrative Survival Modeling
Description: Contains implementations of integrative survival analysis routines, including regular Cox cure rate model proposed by Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531> via an EM algorithm proposed by Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>, regularized Cox cure rate model with elastic net penalty following Masud et al. (2018) <doi:10.1177/0962280216677748>, and Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, and weighted concordance index for cure models proposed by Asano and Hirakawa (2017) <doi:10.1080/10543406.2017.1293082>.
Author: Wenjie Wang [aut, cre] (<https://orcid.org/0000-0003-0363-3180>), Kun Chen [ctb] (<https://orcid.org/0000-0003-3579-5467>), Jun Yan [ctb] (<https://orcid.org/0000-0003-4401-7296>)
Maintainer: Wenjie Wang <wang@wwenjie.org>

Diff between intsurv versions 0.2.1 dated 2019-12-17 and 0.2.2 dated 2021-01-08

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Package hexbin updated to version 1.28.2 with previous version 1.28.1 dated 2020-02-03

Title: Hexagonal Binning Routines
Description: Binning and plotting functions for hexagonal bins.
Author: Dan Carr <dcarr@voxel.galaxy.gmu.edu>, ported by Nicholas Lewin-Koh and Martin Maechler <maechler@stat.math.ethz.ch>, contains copies of lattice functions written by Deepayan Sarkar <deepayan.sarkar@r-project.org>
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

Diff between hexbin versions 1.28.1 dated 2020-02-03 and 1.28.2 dated 2021-01-08

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Package climate updated to version 0.9.9 with previous version 0.9.8 dated 2020-08-03

Title: Interface to Download Meteorological (and Hydrological) Datasets
Description: Automatize downloading of meteorological and hydrological data from publicly available repositories: OGIMET (<http://ogimet.com/index.phtml.en>), University of Wyoming - atmospheric vertical profiling data (<http://weather.uwyo.edu/upperair>), Polish Institute of Meterology and Water Management - National Research Institute (<https://dane.imgw.pl>), and National Oceanic & Atmospheric Administration (NOAA). This package also allows for adding geographical coordinates for each observation.
Author: Bartosz Czernecki [aut, cre] (<https://orcid.org/0000-0001-6496-1386>), Arkadiusz Głogowski [aut] (<https://orcid.org/0000-0002-7587-8892>), Jakub Nowosad [aut] (<https://orcid.org/0000-0002-1057-3721>), IMGW-PIB [ctb] (source of the data)
Maintainer: Bartosz Czernecki <nwp@amu.edu.pl>

Diff between climate versions 0.9.8 dated 2020-08-03 and 0.9.9 dated 2021-01-08

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Package BayesMallows updated to version 1.0.0 with previous version 0.5.0 dated 2020-08-28

Title: Bayesian Preference Learning with the Mallows Rank Model
Description: An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>). Both Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
Author: Oystein Sorensen [aut, cre] (<https://orcid.org/0000-0003-0724-3542>), Valeria Vitelli [aut] (<https://orcid.org/0000-0002-6746-0453>), Marta Crispino [aut], Qinghua Liu [aut], Cristina Mollica [aut], Luca Tardella [aut]
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>

Diff between BayesMallows versions 0.5.0 dated 2020-08-28 and 1.0.0 dated 2021-01-08

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New package cragg with initial version 0.0.1
Package: cragg
Title: Tests for Weak Instruments in R
Version: 0.0.1
Authors@R: person(given = "Beniamino", family = "Green", role = c("aut", "cre"), email = "ben@greendalba.com", )
Description: Implements Cragg-Donald (1993) <doi:10.1017/S0266466600007519> and Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006> tests for weak instruments in R.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: expm (>= 0.999)
Suggests: testthat (>= 3.0.0), knitr, MASS, rmarkdown
Config/testthat/edition: 3
Depends: R (>= 2.10)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-01-05 20:31:53 UTC; beniamino
Author: Beniamino Green [aut, cre]
Maintainer: Beniamino Green <ben@greendalba.com>
Repository: CRAN
Date/Publication: 2021-01-08 08:40:03 UTC

More information about cragg at CRAN
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Package future.BatchJobs (with last version 0.17.0) was removed from CRAN

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

2021-01-04 0.17.0
2019-09-29 0.16.2
2019-01-04 0.16.1
2018-07-10 0.16.0
2017-09-11 0.15.0
2017-05-31 0.14.1
2017-03-18 0.14.0
2016-10-21 0.13.1
2016-08-03 0.13.0
2016-06-27 0.12.1
2016-06-26 0.12.0

Permanent link
Package lawn (with last version 0.6.0) was removed from CRAN

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

2021-01-06 0.6.0
2019-02-01 0.5.0
2017-10-13 0.4.2
2017-09-15 0.4.0
2016-10-25 0.3.0
2016-05-03 0.1.7
2016-03-04 0.1.6
2015-08-24 0.1.4
2015-05-16 0.1.0

Permanent link
Package magickGUI updated to version 1.2.2 with previous version 1.2.0 dated 2020-06-23

Title: GUI Tools for Interactive Image Processing with 'magick'
Description: Enables us to use the functions of the package 'magick' interactively.
Author: Shota Ochi [aut, cre]
Maintainer: Shota Ochi <shotaochi1990@gmail.com>

Diff between magickGUI versions 1.2.0 dated 2020-06-23 and 1.2.2 dated 2021-01-08

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Package diffeqr updated to version 1.1.0 with previous version 1.0.0 dated 2020-08-26

Title: Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)
Description: An interface to 'DifferentialEquations.jl' <https://diffeq.sciml.ai/dev/> from the R programming language. It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. 'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users.
Author: Christopher Rackauckas [aut, cre]
Maintainer: Christopher Rackauckas <me@chrisrackauckas.com>

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Package spatstat.sparse updated to version 1.2-0 with previous version 1.1-0 dated 2020-06-04

Title: Sparse Three-Dimensional Arrays and Linear Algebra Utilities
Description: Defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms.
Author: Adrian Baddeley [aut, cre, cph] (<https://orcid.org/0000-0001-9499-8382>), Rolf Turner [aut, cph] (<https://orcid.org/0000-0001-5521-5218>), Ege Rubak [aut, cph] (<https://orcid.org/0000-0002-6675-533X>)
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

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Package NST updated to version 3.0.6 with previous version 3.0.3 dated 2020-09-16

Title: Normalized Stochasticity Ratio
Description: To estimate ecological stochasticity in community assembly. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) <doi:10.1073/pnas.1324044111>. NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 (Ning et al 2019) <doi:10.1073/pnas.1904623116>. A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) <doi:10.1016/j.soilbio.2020.108023>. SES is calculated as described in Kraft et al (2011) <doi:10.1126/science.1208584>. RC is calculated as reported by Chase et al (2011) <doi:10.1890/es10-00117.1> and Stegen et al (2013) <doi:10.1038/ismej.2013.93>. Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) <doi:10.1038/s41467-020-18560-z>.
Author: Daliang Ning
Maintainer: Daliang Ning <ningdaliang@ou.edu>

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Package brinton updated to version 0.2.4 with previous version 0.2.1 dated 2020-08-31

Title: A Graphical EDA Tool
Description: An automated graphical exploratory data analysis (EDA) tool that introduces: a.) wideplot() graphics for exploring the structure of a dataset through a grid of variables and graphic types. b.) longplot() graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. c.) plotup() function, which presents a particular graphic for a specific variable of a dataset. The plotup() function also makes it possible to obtain the code used to generate the graphic, meaning that the user can adjust its properties as needed.
Author: Pere Millán-Martínez [aut, cre], Ramon Oller Piqué [ctb]
Maintainer: Pere Millán-Martínez <info@sciencegraph.org>

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