Tue, 18 Jun 2024

New package wordmap with initial version 0.8.0
Package: wordmap
Title: Feature Extraction and Document Classification with Noisy Labels
Version: 0.8.0
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>
Description: Extract features and classify documents with noisy labels given by document-meta data or keyword matching Watanabe & Zhou (2020) <doi:10.1177/0894439320907027>.
License: MIT + file LICENSE
URL: https://github.com/koheiw/wordmap
BugReports: https://github.com/koheiw/wordmap/issues
LazyData: TRUE
Encoding: UTF-8
Depends: R (>= 3.5), methods
Imports: utils, Matrix, quanteda (>= 2.1), stringi
Suggests: spelling, testthat, newsmap
Language: en-US
NeedsCompilation: no
Packaged: 2024-06-18 04:42:30 UTC; watan
Author: Kohei Watanabe [aut, cre, cph]
Repository: CRAN
Date/Publication: 2024-06-18 15:20:02 UTC

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New package RSAtools with initial version 0.1.0
Package: RSAtools
Title: Advanced Response Surface Analysis
Version: 0.1.0
Author: Fernando Nunez-Regueiro [aut, cre] , Jacques Juhel [aut] , Felix Schoenbrodt [ctb] , Sarah Humberg [ctb]
Maintainer: Fernando Nunez-Regueiro <fernando.nr.france@gmail.com>
Description: Provides tools for response surface analysis, using a comparative framework that identifies best-fitting solutions across 37 families of polynomials. Many of these tools are based upon and extend the 'RSA' package, by testing a larger scope of polynomials (+27 families), more diverse response surface probing techniques (+acceleration points), more plots (+line of congruence, +line of incongruence, both with extrema), and other useful functions for exporting results.
Suggests: fields, rgl, qgraph, tcltk, tkrplot, testthat, covr, psych, ggpubr
Depends: R (>= 2.15.0), lavaan (>= 0.5.20), semTools (>= 0.5-5), RSA (>= 0.10.4), ggplot2, lattice
Imports: plyr, RColorBrewer, aplpack, methods
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2024-06-18 09:47:54 UTC; FerNR
Repository: CRAN
Date/Publication: 2024-06-18 15:30:05 UTC

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New package redistverse with initial version 0.1.0
Package: redistverse
Title: Easily Install and Load Redistricting Software
Version: 0.1.0
Description: Easy installation, loading, and control of packages for redistricting data downloading, spatial data processing, simulation, analysis, and visualization. This package makes it easy to install and load multiple 'redistverse' packages at once. The 'redistverse' is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see <https://alarm-redist.org>.
Depends: R (>= 4.0.0)
Imports: redist, redistmetrics, geomander, ggredist, sf, censable, tinytiger, easycensus, PL94171, alarmdata, cli, birdie
Suggests: wacolors, testthat (>= 3.0.0), spelling
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/alarm-redist/redistverse, https://alarm-redist.org/redistverse/
BugReports: https://github.com/alarm-redist/redistverse/issues
Language: en-US
NeedsCompilation: no
Packaged: 2024-06-18 00:25:02 UTC; chris
Author: Christopher T. Kenny [aut, cre] , Cory McCartan [aut] , tidyverse contributors [ctb]
Maintainer: Christopher T. Kenny <christopherkenny@fas.harvard.edu>
Repository: CRAN
Date/Publication: 2024-06-18 15:10:02 UTC

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New package ciuupi with initial version 1.2.3
Package: ciuupi
Title: Confidence Intervals Utilizing Uncertain Prior Information
Version: 1.2.3
Author: Paul Kabaila [aut, cre], Rheanna Mainzer [aut], Ayesha Perera [ctb]
Maintainer: Paul Kabaila <P.Kabaila@latrobe.edu.au>
Depends: R (>= 2.10)
Description: Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with known variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a given value. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval has the following three practical applications. Firstly, if the error variance has been accurately estimated from previous data then it may be treated as being effectively known. Secondly, for sufficiently large (dimension of the response vector) minus (dimension of regression parameter vector), greater than or equal to 30 (say), if we replace the assumed known value of the error variance by its usual estimator in the formula for the confidence interval then [...truncated...]
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: nloptr, statmod, functional, pracma, stats, graphics
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-06-18 06:40:21 UTC; Kabaila
Repository: CRAN
Date/Publication: 2024-06-18 15:30:02 UTC

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New package speakeasyR with initial version 0.1.0
Package: speakeasyR
Title: Fast and Robust Multi-Scale Graph Clustering
Version: 0.1.0
Description: A graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) <doi:10.1186/s13059-023-03062-0>. The core algorithm is written in 'C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base 'R' matrices, the 'Matrix' library, 'igraph' graphs, or any data that can coerced into a matrix.
License: GPL (>= 3)
Encoding: UTF-8
Imports: Matrix, methods
Suggests: igraph, scRNAseq, SummarizedExperiment, knitr, rmarkdown, testthat (>= 3.0.0)
URL: https://github.com/SpeakEasy-2/speakeasyR
BugReports: https://github.com/SpeakEasy-2/speakeasyR/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-06-17 22:01:41 UTC; voidee
Author: David Connell [aut, cre, cph] , Chris Gaiteri [cph] , Gabor Csardi [cph] , Tamas Nepusz [cph] , Szabolcs Horvat [cph] , Vincent Traag [cph] , Fabio Zanini [cph] , Daniel Noom [cph]
Maintainer: David Connell <david32@dcon.addy.io>
Repository: CRAN
Date/Publication: 2024-06-18 14:40:02 UTC

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New package gmfamm with initial version 0.1.0
Package: gmfamm
Title: Generalized Multivariate Functional Additive Models
Version: 0.1.0
Description: Supply implementation to model generalized multivariate functional data using Bayesian additive mixed models of R package 'bamlss' via a latent Gaussian process (see Umlauf, Klein, Zeileis (2018) <doi:10.1080/10618600.2017.1407325>).
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5), bamlss
Imports: mgcv, stats, MASS, splines, Matrix
Suggests: testthat (>= 3.0.0), tidyverse, JMbayes2, registr, funData, MFPCA, MJMbamlss, refund
NeedsCompilation: no
Packaged: 2024-06-17 20:10:54 UTC; alex
Author: Nikolaus Umlauf [aut] , Alexander Volkmann [aut, cre]
Maintainer: Alexander Volkmann <alexandervolkmann8@gmail.com>
Repository: CRAN
Date/Publication: 2024-06-18 14:30:05 UTC

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New package easyORtables with initial version 0.0.1
Package: easyORtables
Title: Easy Odds Ratio Tables
Version: 0.0.1
Description: Creates text, 'LaTeX', Markdown, or Bootstrap-styled HTML-formatted odds ratio tables with confidence intervals for multiple logistic regression models.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: stats, knitr, kableExtra, MASS
NeedsCompilation: no
Packaged: 2024-06-16 19:56:55 UTC; neilmehta
Author: Neil Mehta [aut, cre]
Maintainer: Neil Mehta <neilmhta@gmail.com>
Repository: CRAN
Date/Publication: 2024-06-18 14:20:02 UTC

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New package DeepLearningCausal with initial version 0.0.102
Package: DeepLearningCausal
Title: Causal Inference with Super Learner and Deep Neural Networks
Version: 0.0.102
Maintainer: Nguyen K. Huynh <khoinguyen.huynh@r.hit-u.ac.jp>
Description: Functions to estimate Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT) from experimental or observational data using the Super Learner (SL) ensemble method and Deep neural networks. The package first provides functions to implement meta-learners such as the Single-learner (S-learner) and Two-learner (T-learner) described in Künzel et al. (2019) <doi:10.1073/pnas.1804597116> for estimating the CATE. The S- and T-learner are each estimated using the SL ensemble method and deep neural networks. It then provides functions to implement the Ottoboni and Poulos (2020) <doi:10.1515/jci-2018-0035> PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: ROCR, caret, neuralnet, SuperLearner, class, xgboost, randomForest, glmnet, gam, e1071, gbm, Hmisc, weights
Suggests: testthat, ggplot2, tidyr
Depends: R (>= 4.1.0)
URL: https://github.com/hknd23/DeepLearningCausal
BugReports: https://github.com/hknd23/DeepLearningCausal/issues
NeedsCompilation: no
Packaged: 2024-06-18 00:18:22 UTC; nguye
Author: Nguyen K. Huynh [aut, cre] , Bumba Mukherjee [aut] , Irvin Lee [aut]
Repository: CRAN
Date/Publication: 2024-06-18 14:40:06 UTC

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New package plotor with initial version 0.4.1
Package: plotor
Title: Produces an Odds Ratio Plot from a Logistic Regression Model
Version: 0.4.1
Maintainer: Craig Parylo <craig.parylo2@nhs.net>
Description: Produces an Odds Ratio (OR) Plot to visualise the result of a logistic regression analysis. Provide it with a binomial regression model produced by 'glm()' and it will convert the estimates to odds ratios with a 95% confidence interval and plot the results using 'ggplot2'.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: broom, dplyr, ggplot2, glue, purrr, scales, stats, tidyselect
Suggests: datasets, forcats, knitr, labelled, rmarkdown, testthat (>= 3.0.0), tidyr
VignetteBuilder: knitr
URL: https://github.com/craig-parylo/plotor, https://craig-parylo.github.io/plotor/
BugReports: https://github.com/craig-parylo/plotor/issues
NeedsCompilation: no
Packaged: 2024-06-17 11:07:25 UTC; Craig.Parylo
Author: Craig Parylo [aut, cre, cph]
Repository: CRAN
Date/Publication: 2024-06-18 13:40:02 UTC

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New package microdatasus with initial version 2.3.1
Package: microdatasus
Title: Download and Process 'DataSUS' Files
Version: 2.3.1
URL: https://github.com/rfsaldanha/microdatasus, https://rfsaldanha.github.io/microdatasus/
BugReports: https://github.com/rfsaldanha/microdatasus/issues
Description: Downloads data files from 'DataSUS' health information systems from <ftp.datasus.gov.br> and process the data, including labeling categorical variables.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
Imports: checkmate, cli, curl, data.table, dplyr, dtplyr, foreign, lubridate, magrittr, RCurl, read.dbc, rlang, stringi, tibble, utils, zip
Suggests: testthat (>= 3.0.0)
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-06-17 09:30:02 UTC; raphael
Author: Raphael Saldanha [aut, cre]
Maintainer: Raphael Saldanha <raphael.saldanha@fiocruz.br>
Repository: CRAN
Date/Publication: 2024-06-18 13:30:02 UTC

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New package DIDHAD with initial version 1.0.0
Package: DIDHAD
Title: Difference-in-Differences in Heterogeneous Adoption Designs with Quasi Stayers
Version: 1.0.0
Maintainer: Diego Ciccia <diego.ciccia@sciencespo.fr>
Description: Estimation of Difference-in-Differences (DiD) estimators from de Chaisemartin and D'Haultfoeuille (2024) <doi:10.2139/ssrn.4284811> in Heterogeneous Adoption Designs with no stayers but with quasi stayers.
License: MIT + file LICENSE
Imports: MASS, YatchewTest (>= 1.1.0), nprobust, ggplot2, plm, rnames, stats, dplyr, haven, rlang
Author: Diego Ciccia [aut, cre], Felix Knau [aut], Doulo Sow [aut], Clement de Chaisemartin [aut], Xavier D'Haultfoeuille [aut]
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-06-17 07:48:25 UTC; 39380
Repository: CRAN
Date/Publication: 2024-06-18 13:20:02 UTC

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New package beeca with initial version 0.1.3
Package: beeca
Title: Binary Endpoint Estimation with Covariate Adjustment
Version: 0.1.3
Description: Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.
Maintainer: Alex Przybylski <alexander.przybylski@novartis.com>
License: LGPL (>= 3)
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), tidyr, marginaleffects, margins, RobinCar (== 0.3.0)
Depends: R (>= 2.10)
LazyData: true
Imports: dplyr, lifecycle, sandwich, stats
VignetteBuilder: knitr
URL: https://openpharma.github.io/beeca/
NeedsCompilation: no
Packaged: 2024-06-17 11:38:53 UTC; przybal2
Author: Alex Przybylski [cre, aut], Mark Baillie [aut] , Craig Wang [aut] , Dominic Magirr [aut]
Repository: CRAN
Date/Publication: 2024-06-18 13:30:06 UTC

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Package spiralize updated to version 1.1.0 with previous version 1.0.6 dated 2022-08-09

Title: Visualize Data on Spirals
Description: It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.
Author: Zuguang Gu [aut, cre]
Maintainer: Zuguang Gu <z.gu@dkfz.de>

Diff between spiralize versions 1.0.6 dated 2022-08-09 and 1.1.0 dated 2024-06-18

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Package ulrb updated to version 0.1.5 with previous version 0.1.3 dated 2023-11-17

Title: Unsupervised Learning Based Definition of Microbial Rare Biosphere
Description: A tool to define rare biosphere. 'ulrb' solves the problem of the definition of rarity by replacing arbitrary thresholds with an unsupervised machine learning algorithm (partitioning around medoids, or k-medoids). This algorithm works for any type of microbiome data, provided there is a species abundance table. For validation of this method to different species abundance tables see Pascoal et al, 2024 (in peer-review). This method also works for non-microbiome data.
Author: Francisco Pascoal [aut, cre] , Paula Branco [aut] , Luis Torgo [aut] , Rodrigo Costa [aut] , Catarina Magalhaes [aut]
Maintainer: Francisco Pascoal <fpascoal1996@gmail.com>

Diff between ulrb versions 0.1.3 dated 2023-11-17 and 0.1.5 dated 2024-06-18

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New package robusTest with initial version 1.1.0
Package: robusTest
Title: Calibrated Correlation and Two-Sample Tests
Version: 1.1.0
Description: Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented. The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is asymptotically equal to the level of the test. See <doi:10.48550/arXiv.2211.08784> for more details on the statistical tests.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
LinkingTo: Rcpp
Imports: Rcpp, stats
Suggests: testthat (>= 3.0.0)
NeedsCompilation: yes
Packaged: 2024-06-18 10:13:59 UTC; obouaziz
Author: Olivier Bouaziz [aut, cre], Jerome Dedecker [aut], Anatole Dedecker [aut]
Maintainer: Olivier Bouaziz <olivier.bouaziz@parisdescartes.fr>
Repository: CRAN
Date/Publication: 2024-06-18 11:00:02 UTC

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Package BGmisc updated to version 1.3.2 with previous version 1.3.1 dated 2024-06-16

Title: An R Package for Extended Behavior Genetics Analysis
Description: The BGmisc R package offers a comprehensive suite of functions tailored for extended behavior genetics analysis, including model identification, calculating relatedness, pedigree conversion, pedigree simulation, and more.
Author: S. Mason Garrison [aut, cre] , Michael D. Hunter [aut] , Xuanyu Lyu [aut] , Rachel N. Good [ctb], Jonathan D. Trattner [aut] , S. Alexandra Burt [aut]
Maintainer: S. Mason Garrison <garrissm@wfu.edu>

Diff between BGmisc versions 1.3.1 dated 2024-06-16 and 1.3.2 dated 2024-06-18

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Package iNEXT.4steps updated to version 1.0.1 with previous version 1.0.0 dated 2024-04-10

Title: Four-Step Biodiversity Analysis Based on 'iNEXT'
Description: Expands 'iNEXT' to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis: STEP 1: Assessment of sample completeness profiles. STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to determine whether the asymptotic diversity can be accurately estimated. STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves. STEP 4: Assessment of evenness profiles. The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) <doi:10.1111/1440-1703.12102> for statistical background.
Author: Anne Chao [aut, cre], KaiHsiang Hu [ctb]
Maintainer: Anne Chao <chao@stat.nthu.edu.tw>

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Package clinDataReview updated to version 1.6.1 with previous version 1.6.0 dated 2024-06-13

Title: Clinical Data Review Tool
Description: Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template 'R Markdown' reports contained in the package.
Author: Laure Cougnaud [aut, cre], Michela Pasetto [aut], Lennart Tuijnder [aut], Adriaan Blommaert [aut], Arne De Roeck [ctb, rev] , Open Analytics [cph]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>

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Package patientProfilesVis updated to version 2.0.9 with previous version 2.0.8 dated 2024-06-13

Title: Visualization of Patient Profiles
Description: Creation of patient profile visualizations for exploration, diagnostic or monitoring purposes during a clinical trial. These static visualizations display a patient-specific overview of the evolution during the trial time frame of parameters of interest (as laboratory, ECG, vital signs), presence of adverse events, exposure to a treatment; associated with metadata patient information, as demography, concomitant medication. The visualizations can be tailored for specific domain(s) or endpoint(s) of interest. Visualizations are exported into patient profile report(s) or can be embedded in custom report(s).
Author: Laure Cougnaud [aut, cre], Margaux Faes [rev] , Open Analytics [cph]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>

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Package mokken updated to version 3.1.2 with previous version 3.1.1 dated 2024-05-29

Title: Conducts Mokken Scale Analysis
Description: Contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions.
Author: L. Andries van der Ark [aut, cre], Letty Koopman [aut], J. Hendrik Straat [ctb], Don van den Bergh [ctb]
Maintainer: L. Andries van der Ark <L.A.vanderArk@uva.nl>

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Package CrossCarry updated to version 0.2.0 with previous version 0.1.0 dated 2023-04-11

Title: Analysis of Data from a Crossover Design with GEE
Description: Analyze data from a crossover design using generalized estimation equations (GEE), including carryover effects and various correlation structures based on the Kronecker product. It contains functions for semiparametric estimates of carry-over effects in repeated measures and allows estimation of complex carry-over effects. Related work includes: a) Cruz N.A., Melo O.O., Martinez C.A. (2023). "CrossCarry: An R package for the analysis of data from a crossover design with GEE". <doi:10.48550/arXiv.2304.02440>. b) Cruz N.A., Melo O.O., Martinez C.A. (2023). "A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures". <doi:10.1007/s00362-022-01391-z> and c) Cruz N.A., Melo O.O., Martinez C.A. (2023). "Semiparametric generalized estimating equations for repeated measurements in cross-over designs". <doi:10.1177/09622802231158736>.
Author: Nelson Alirio Cruz Gutierrez [aut, cre, cph], Oscar Orlando Melo [aut], Carlos Alberto Martinez [aut]
Maintainer: Nelson Alirio Cruz Gutierrez <neacruzgu@unal.edu.co>

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Package ClusterR updated to version 1.3.3 with previous version 1.3.2 dated 2023-12-04

Title: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering
Description: Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
Author: Lampros Mouselimis [aut, cre] , Conrad Sanderson [cph] , Ryan Curtin [cph] , Siddharth Agrawal [cph] ), Brendan Frey [cph] ), Delbert Dueck [cph] , Vitalie Spinu [ctb]
Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>

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