Fri, 09 May 2025

Package nasaweather updated to version 0.1.1 with previous version 0.1 dated 2014-07-22

Title: Collection of Datasets from the ASA 2006 Data Expo
Description: Tidied data from the ASA 2006 data expo, as well as a number of useful other related data sets.
Author: Hadley Wickham [aut, cre]
Maintainer: Hadley Wickham <hadley@posit.co>

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Package ggstatsplot updated to version 0.13.1 with previous version 0.13.0 dated 2024-12-04

Title: 'ggplot2' Based Plots with Statistical Details
Description: Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.
Author: Indrajeet Patil [cre, aut, cph] , Chuck Powell [ctb]
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

Diff between ggstatsplot versions 0.13.0 dated 2024-12-04 and 0.13.1 dated 2025-05-09

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Package generics updated to version 0.1.4 with previous version 0.1.3 dated 2022-07-05

Title: Common S3 Generics not Provided by Base R Methods Related to Model Fitting
Description: In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics.
Author: Hadley Wickham [aut, cre] , Max Kuhn [aut], Davis Vaughan [aut], Posit Software, PBC [cph, fnd]
Maintainer: Hadley Wickham <hadley@posit.co>

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Package statsExpressions updated to version 1.7.0 with previous version 1.6.2 dated 2024-12-02

Title: Tidy Dataframes and Expressions with Statistical Details
Description: Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.
Author: Indrajeet Patil [cre, aut, cph]
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package nixmass updated to version 1.3.1 with previous version 1.0.2 dated 2021-03-05

Title: Snow Water Equivalent Modeling with the 'Delta.snow' and 'HS2SWE' Models and Empirical Regression Models
Description: Snow water equivalent is modeled with the process based models 'delta.snow' and 'HS2SWE' and empirical regression, which use relationships between density and diverse at-site parameters. The methods are described in Winkler et al. (2021) <doi:10.5194/hess-25-1165-2021>, Magnusson et al. (2025) <doi:10.1016/j.coldregions.2025.104435>, Guyennon et al. (2019) <doi:10.1016/j.coldregions.2019.102859>, Pistocchi (2016) <doi:10.1016/j.ejrh.2016.03.004>, Jonas et al. (2009) <doi:10.1016/j.jhydrol.2009.09.021> and Sturm et al. (2010) <doi:10.1175/2010JHM1202.1>.
Author: Harald Schellander [aut, cre, cph], Michael Winkler [ctb, cph]
Maintainer: Harald Schellander <harald.schellander@geosphere.at>

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Package ggsurveillance updated to version 0.4.0 with previous version 0.3.0 dated 2025-04-11

Title: Tools for Outbreak Investigation/Infectious Disease Surveillance
Description: Create epicurves or epigantt charts in 'ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation. Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the 'ggplot2'. An introduction on how to use epicurves can be found on the US CDC website (2012, <https://www.cdc.gov/training/quicklearns/epimode/index.html>).
Author: Alexander Bartel [aut, cre]
Maintainer: Alexander Bartel <alexander.bartel@fu-berlin.de>

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New package gglinedensity with initial version 0.1.0
Package: gglinedensity
Title: Make DenseLines Heatmaps with 'ggplot2'
Version: 0.1.0
Description: Create a raster plot of line density using a 'ggplot2' statistic implementing the DenseLines algorithm, which "normalizes time series by the arc length to compute accurate densities" (Moritz and Fisher, 2018) <doi:10.48550/arXiv.1808.06019>.
License: GPL (>= 3)
Encoding: UTF-8
SystemRequirements: Cargo (Rust's package manager), rustc >= 1.65.0
Imports: cli, ggplot2, lifecycle, rlang, scales, vctrs
URL: https://github.com/hrryt/gglinedensity
BugReports: https://github.com/hrryt/gglinedensity/issues
NeedsCompilation: yes
Packaged: 2025-05-08 12:16:50 UTC; harry
Author: Harry Thompson [cre, aut, cph], Dominik Moritz [aut] , The authors of the dependency Rust crates [ctb] , Hiroaki Yutani [ctb]
Maintainer: Harry Thompson <harry@mayesfield.uk>
Repository: CRAN
Date/Publication: 2025-05-09 23:00:02 UTC

More information about gglinedensity at CRAN
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Package dnafractal updated to version 0.0.2 with previous version 0.0.1 dated 2025-05-07

Title: Generates a Fractal Image of a DNA Sequence
Description: The function takes a DNA sequence, a start point, an end point in the sequence, dot size and dot color and draws a fractal image of the sequence. The fractal starts in the center of the canvas. The image is drawn by moving base by base along the sequence and dropping a midpoint between the actual point and the corner designated by the actual base. For more details see Jeffrey (1990) <doi:10.1093/nar/18.8.2163>, Hill, Schisler, and Singh (1992) <doi:10.1007/BF00178602>, and Löchel and Heider (2021) <doi:10.1016/j.csbj.2021.11.008>.
Author: Matthew Cserhati [aut, cre]
Maintainer: Matthew Cserhati <matthew.cserhati@cui.edu>

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Package interprocess updated to version 1.1.0 with previous version 1.0.0 dated 2025-05-02

Title: Mutexes, Semaphores, and Message Queues
Description: Provides access to low-level operating system mechanisms for performing atomic operations on shared data structures. Mutexes provide shared and exclusive locks. Semaphores act as counters. Message queues move text strings from one process to another. All these interprocess communication (IPC) tools can optionally block with or without a timeout. Implemented using the cross-platform 'boost' 'C++' library <https://www.boost.org/doc/libs/release/libs/interprocess/>.
Author: Daniel P. Smith [aut, cre] , Alkek Center for Metagenomics and Microbiome Research [cph, fnd]
Maintainer: Daniel P. Smith <dansmith01@gmail.com>

Diff between interprocess versions 1.0.0 dated 2025-05-02 and 1.1.0 dated 2025-05-09

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Package RadEro updated to version 1.0.8 with previous version 1.0.5 dated 2024-11-18

Title: Cs-137 Conversion Model
Description: A straightforward model to estimate soil migration rates across various soil contexts. Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) <doi:10.1016/j.jaridenv.2004.02.003> and Soto and Navas (2008) <doi:10.1016/j.radmeas.2008.02.024>. 'RadEro' provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: <https://gi [...truncated...]
Author: Arturo Catala [aut, cre], Borja Latorre [aut, ctb, cph], Leticia Gaspar [aut], Ana Navas [aut], Dave Gamble [ctb]
Maintainer: Arturo Catala <radero@eead.csic.es>

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Package spider updated to version 1.5.1 with previous version 1.5.0 dated 2018-02-16

Title: Species Identity and Evolution in R
Description: Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.
Author: Samuel Brown [aut], Stephane Boyer [aut], Marie-Caroline Lefort [aut], Jagoba Malumbres-Olarte [aut], Cor Vink [aut], Rob Cruickshank [aut], Rupert A. Collins [aut, cre, cph]
Maintainer: Rupert A. Collins <rupertcollins@gmail.com>

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Package sphereML updated to version 0.1.1 with previous version 0.1.0 dated 2025-02-06

Title: Analyzing Students' Performance Dataset in Physics Education Research (SPHERE) using Machine Learning (ML)
Description: A simple package facilitating ML based analysis for physics education research (PER) purposes. The implemented machine learning technique is random forest optimized by item response theory (IRT) for feature selection and genetic algorithm (GA) for hyperparameter tuning. The data analyzed here has been made available in the CRAN repository through the 'spheredata' package. The SPHERE stands for Students' Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at the high school curriculum. We follow the stream of multidimensional students' assessment as probed by some research based assessments in PER. The goal is to predict the students' performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables predicting students' performance on physics are also demonstrated.
Author: Purwoko Haryadi Santoso [aut, cre] , Edi Istiyono [ctb], Haryanto Haryanto [ctb]
Maintainer: Purwoko Haryadi Santoso <purwokoharyadisantoso@unsulbar.ac.id>

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Package spheredata updated to version 0.1.3 with previous version 0.1.2 dated 2024-11-18

Title: Students' Performance Dataset in Physics Education Research (SPHERE)
Description: A multidimensional dataset of students' performance assessment in high school physics. The SPHERE dataset was collected from 497 students in four public high schools specifically measuring their conceptual understanding, scientific ability, and attitude toward physics [see Santoso et al. (2024) <doi:10.17632/88d7m2fv7p.1>]. The data collection was conducted using some research based assessments established by the physics education research community. They include the Force Concept Inventory, the Force and Motion Conceptual Evaluation, the Rotational and Rolling Motion Conceptual Survey, the Fluid Mechanics Concept Inventory, the Mechanical Waves Conceptual Survey, the Thermal Concept Evaluation, the Survey of Thermodynamic Processes and First and Second Laws, the Scientific Abilities Assessment Rubrics, and the Colorado Learning Attitudes about Science Survey. Students' attributes related to gender, age, socioeconomic status, domicile, literacy, physics identity, and test results [...truncated...]
Author: Purwoko Haryadi Santoso [aut, cre] , Edi Istiyono [ctb], Haryanto Haryanto [ctb]
Maintainer: Purwoko Haryadi Santoso <purwokoharyadisantoso@unsulbar.ac.id>

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Package poobly updated to version 0.1.2 with previous version 0.1.1 dated 2025-01-15

Title: Poolability Tests in Panel Data
Description: Homogeneity tests of the coefficients in panel data. Currently, only the Hsiao test for determining coefficient homogeneity between the panel data individuals is implemented, as described in Hsiao (2022), "Analysis of Panel Data" (<doi:10.1017/9781009057745>).
Author: Christos Adam [aut, cre]
Maintainer: Christos Adam <econp266@econ.soc.uoc.gr>

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Package datawizard updated to version 1.1.0 with previous version 1.0.2 dated 2025-03-24

Title: Easy Data Wrangling and Statistical Transformations
Description: A lightweight package to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. It is also the data wrangling backend for packages in 'easystats' ecosystem. References: Patil et al. (2022) <doi:10.21105/joss.04684>.
Author: Indrajeet Patil [aut] , Etienne Bacher [aut, cre] , Dominique Makowski [aut] , Daniel Luedecke [aut] , Mattan S. Ben-Shachar [aut] , Brenton M. Wiernik [aut] , Remi Theriault [ctb] , Thomas J. Faulkenberry [rev], Robert Garrett [rev]
Maintainer: Etienne Bacher <etienne.bacher@protonmail.com>

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More information about datawizard at CRAN
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Package ROCket updated to version 1.0.3 with previous version 1.0.1 dated 2021-02-17

Title: Simple and Fast ROC Curves
Description: A set of functions for receiver operating characteristic (ROC) curve estimation and area under the curve (AUC) calculation. All functions are designed to work with aggregated data; nevertheless, they can also handle raw samples. In 'ROCket', we distinguish two types of ROC curve representations: 1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) are functions of a parameter (the score), 2) functions - TPR is a function of FPR. There are several ROC curve estimation methods available. An introduction to the mathematical background of the implemented methods (and much more) can be found in de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004).
Author: Daniel Lazar [aut, cre]
Maintainer: Daniel Lazar <da-zar@gmx.net>

Diff between ROCket versions 1.0.1 dated 2021-02-17 and 1.0.3 dated 2025-05-09

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Package mrbin updated to version 1.9.1 with previous version 1.9.0 dated 2025-03-07

Title: Metabolomics Data Analysis Functions
Description: A collection of functions for processing and analyzing metabolite data. The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models.
Author: Matthias Klein [aut, cre]
Maintainer: Matthias Klein <matthias.s.klein@gmx.net>

Diff between mrbin versions 1.9.0 dated 2025-03-07 and 1.9.1 dated 2025-05-09

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Package ipanema updated to version 1.2.0 with previous version 1.1.0 dated 2024-03-01

Title: Read Data from 'LimeSurvey'
Description: Read data from 'LimeSurvey' (<https://www.limesurvey.org/>) in a comfortable way. Heavily inspired by 'limer' (<https://github.com/cloudyr/limer/>), which lacked a few comfort features for me.
Author: Maximilian Hagspiel [aut, cre, cph]
Maintainer: Maximilian Hagspiel <maxhag@mailbox.org>

Diff between ipanema versions 1.1.0 dated 2024-03-01 and 1.2.0 dated 2025-05-09

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Package tram updated to version 1.2-2 with previous version 1.2-1 dated 2025-02-25

Title: Transformation Models
Description: Formula-based user-interfaces to specific transformation models implemented in package 'mlt' (<DOI:10.32614/CRAN.package.mlt>, <DOI:10.32614/CRAN.package.mlt.docreg>). Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, <DOI:10.1093/biostatistics/kxac048>). Multivariate conditional transformation models (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) can be fitted as well. The package contains an implementation of a doubly robust score test, [...truncated...]
Author: Torsten Hothorn [aut, cre] , Luisa Barbanti [aut] , Sandra Siegfried [aut] , Lucas Kook [aut] , Brian Ripley [ctb], Bill Venables [ctb], Douglas M. Bates [ctb], Nadja Klein [ctb]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between tram versions 1.2-1 dated 2025-02-25 and 1.2-2 dated 2025-05-09

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Package SDModels updated to version 1.0.10 with previous version 1.0.7 dated 2025-04-09

Title: Spectrally Deconfounded Models
Description: Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ćevid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.48550/arXiv.2502.03969>).
Author: Markus Ulmer [aut, cre, cph] , Cyrill Scheidegger [aut]
Maintainer: Markus Ulmer <markus.ulmer@stat.math.ethz.ch>

Diff between SDModels versions 1.0.7 dated 2025-04-09 and 1.0.10 dated 2025-05-09

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Package regress updated to version 1.3-22 with previous version 1.3-21 dated 2020-06-18

Title: Gaussian Linear Models with Linear Covariance Structure
Description: Functions to fit Gaussian linear model by maximising the residual log likelihood where the covariance structure can be written as a linear combination of known matrices. Can be used for multivariate models and random effects models. Easy straight forward manner to specify random effects models, including random interactions. Code now optimised to use Sherman Morrison Woodbury identities for matrix inversion in random effects models. We've added the ability to fit models using any kernel as well as a function to return the mean and covariance of random effects conditional on the data (best linear unbiased predictors, BLUPs). Clifford and McCullagh (2006) <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.
Author: David Clifford [aut], Peter McCullagh [aut], HJ Auinger [ctb], Karl W Broman [ctb, cre]
Maintainer: Karl W Broman <broman@wisc.edu>

Diff between regress versions 1.3-21 dated 2020-06-18 and 1.3-22 dated 2025-05-09

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Package RcppSpdlog updated to version 0.0.22 with previous version 0.0.21 dated 2025-03-30

Title: R and C++ Interfaces to 'spdlog' C++ Header Library for Logging
Description: The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides many desirable features. This package bundles these header files for easy use by R packages from both their R and C or C++ code. Explicit use via 'LinkingTo:' is also supported. Also see the 'spdl' package which enhanced this package with a consistent R and C++ interface.
Author: Dirk Eddelbuettel [aut, cre] , Gabi Melman [aut] , Victor Zverovic [aut]
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RcppSpdlog versions 0.0.21 dated 2025-03-30 and 0.0.22 dated 2025-05-09

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Package mlt updated to version 1.6-5 with previous version 1.6-4 dated 2025-04-29

Title: Most Likely Transformations
Description: Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>. Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.
Author: Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between mlt versions 1.6-4 dated 2025-04-29 and 1.6-5 dated 2025-05-09

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New package dockViewR with initial version 0.1.0
Package: dockViewR
Title: Layout Manager Widget for R and 'shiny' Apps
Version: 0.1.0
Description: Provides R bindings to the 'dockview' 'JavaScript' library <https://dockview.dev/>. Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or 'Quarto' or in 'shiny' apps <https://shiny.posit.co/>. In 'shiny' mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: htmlwidgets, htmltools, shiny
Suggests: knitr, rmarkdown, roxy.shinylive, shinytest2, testthat (>= 3.0.0), visNetwork, quarto
URL: https://github.com/cynkra/dockViewR, https://cynkra.github.io/dockViewR/
BugReports: https://github.com/cynkra/dockViewR/issues
Depends: R (>= 2.10)
VignetteBuilder: quarto
NeedsCompilation: no
Packaged: 2025-05-08 13:33:04 UTC; davidgranjon
Author: David Granjon [aut, cre], Nelson Stevens [aut], Nicolas Bennett [aut], mathuo [cph], cynkra GmbH [fnd]
Maintainer: David Granjon <dgranjon@ymail.com>
Repository: CRAN
Date/Publication: 2025-05-09 15:20:05 UTC

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New package cutpoint with initial version 1.0.0
Package: cutpoint
Title: Estimate Cutpoints of Metric Variables in the Context of Cox Regression
Version: 1.0.0
Description: Estimate one or two cutpoints of a metric or ordinal-scaled variable in the multivariable context of survival data or time-to-event data. Visualise the cutpoint estimation process using contour plots, index plots, and spline plots. It is also possible to estimate cutpoints based on the assumption of a U-shaped or inverted U-shaped relationship between the predictor and the hazard ratio. Govindarajulu, U., and Tarpey, T. (2022) <doi:10.1080/02664763.2020.1846690>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-GB
LazyData: true
Imports: graphics, magrittr, plotly, RcppAlgos, stats, survival, utils
URL: https://github.com/jan-por/cutpoint
BugReports: https://github.com/jan-por/cutpoint/issues
Depends: R (>= 3.5)
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2025-05-08 16:09:54 UTC; janpo
Author: Jan Porthun [aut, cre, cph]
Maintainer: Jan Porthun <jan.porthun@ntnu.no>
Repository: CRAN
Date/Publication: 2025-05-09 15:20:09 UTC

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Package basefun updated to version 1.2-3 with previous version 1.2-2 dated 2025-01-29

Title: Infrastructure for Computing with Basis Functions
Description: Some very simple infrastructure for basis functions.
Author: Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between basefun versions 1.2-2 dated 2025-01-29 and 1.2-3 dated 2025-05-09

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Package variables updated to version 1.1-2 with previous version 1.1-1 dated 2021-06-17

Title: Variable Descriptions
Description: Abstract descriptions of (yet) unobserved variables.
Author: Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between variables versions 1.1-1 dated 2021-06-17 and 1.1-2 dated 2025-05-09

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New package trustmebro with initial version 1.0.0
Package: trustmebro
Title: Inspect and Clean Subject-Generated ID Codes and Related Data
Version: 1.0.0
Maintainer: Annemarie Plaeschke <anneplaeschke@gmail.com>
Description: Makes data wrangling with ID-related aspects more comfortable. Provides functions that make it easy to inspect various subject-generated ID codes (SGIC) for plausibility. Also helps with inspecting other common identifiers, ensuring that your data stays clean and reliable.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: knitr, rmarkdown, spelling, testthat (>= 3.0.0)
Depends: R (>= 2.10)
Imports: dplyr, tibble, rlang
VignetteBuilder: knitr
URL: https://kuuuwe.github.io/trustmebro/, https://github.com/kuuuwe/trustmebro
Language: en-US
BugReports: https://github.com/kuuuwe/trustmebro/issues
NeedsCompilation: no
Packaged: 2025-05-07 20:50:26 UTC; Anwender
Author: Annemarie Plaeschke [aut, cre, cph] , Tobias Braendle [aut]
Repository: CRAN
Date/Publication: 2025-05-09 14:10:02 UTC

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Package TELP updated to version 1.0.3 with previous version 1.0.2 dated 2023-08-31

Title: Social Representation Theory Application: The Free Evocation of Words Technique
Description: Using The Free Evocation of Words Technique method with some functions, this package will make a social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.
Author: Gabriel Henrique Oliveira Assuncao [aut, cre]
Maintainer: Gabriel Henrique Oliveira Assuncao <gabrielhenryoliver@hotmail.com>

Diff between TELP versions 1.0.2 dated 2023-08-31 and 1.0.3 dated 2025-05-09

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New package seliNDRIx with initial version 0.1.2
Package: seliNDRIx
Title: Construction of Selection Index
Version: 0.1.2
Description: Selection index is one of the efficient and acurrate method for selection of animals. This package is useful for construction of selection indices. It uses mixed and random model least squares analysis to estimate the heritability of traits and genetic correlation between traits. The package uses the sire model as it is considered as random effect. The genetic and phenotypic (co)variances along with the relative economic values are used to construct the selection index for any number of traits. It also estimates the accuracy of the index and the genetic gain expected for different traits. Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, psych, stats, utils
Suggests: testthat (>= 3.0.0)
Depends: R (>= 3.5)
LazyData: true
URL: https://github.com/venkatesanraja/seliNDRIx
BugReports: https://github.com/venkatesanraja/seliNDRIx/issues
NeedsCompilation: no
Packaged: 2025-05-08 05:54:42 UTC; hp
Author: Raja TV [aut, cre], Alex Rani [aut], Gowane Gopal [aut], Vohra Vikas [aut]
Maintainer: Raja TV <venkatesanraja09@gmail.com>
Repository: CRAN
Date/Publication: 2025-05-09 14:30:02 UTC

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Package pomp updated to version 6.3 with previous version 6.2 dated 2025-04-16

Title: Statistical Inference for Partially Observed Markov Processes
Description: Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Author: Aaron A. King [aut, cre] , Edward L. Ionides [aut] , Carles Breto [aut] , Stephen P. Ellner [ctb] , Matthew J. Ferrari [ctb] , Sebastian Funk [ctb] , Steven G. Johnson [ctb], Bruce E. Kendall [ctb] , Michael Lavine [ctb], Dao Nguyen [ctb] , Eamon B. [...truncated...]
Maintainer: Aaron A. King <kingaa@umich.edu>

Diff between pomp versions 6.2 dated 2025-04-16 and 6.3 dated 2025-05-09

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New package pkgdown.offline with initial version 0.1.0
Package: pkgdown.offline
Title: Build 'pkgdown' Websites Offline
Version: 0.1.0
Description: Provides support for building 'pkgdown' websites without an internet connection. Works by bundling cached dependencies and implementing drop-in replacements for key 'pkgdown' functions. Enables package documentation websites to be built in environments where internet access is unavailable or restricted. For more details on generating 'pkgdown' websites, see Wickham et al. (2025) <doi:10.32614/CRAN.package.pkgdown>.
License: MIT + file LICENSE
URL: https://nanx.me/pkgdown.offline/, https://github.com/nanxstats/pkgdown.offline
BugReports: https://github.com/nanxstats/pkgdown.offline/issues
Encoding: UTF-8
Imports: pkgdown
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-05-08 03:09:12 UTC; nanx
Author: Nan Xiao [aut, cre, cph] , John Blischak [aut] , Algolia, Inc. and other contributors [ctb, cph] , Aidan Feldman [ctb, cph] , Zeno Rocha [ctb, cph] , Nick Williams [ctb, cph] , Julian Kuehnel [ctb, cph] , Kiro Risk [ctb, cph] , Khan Academy and other [...truncated...]
Maintainer: Nan Xiao <me@nanx.me>
Repository: CRAN
Date/Publication: 2025-05-09 14:10:05 UTC

More information about pkgdown.offline at CRAN
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Package panelPomp updated to version 1.7.0.0 with previous version 1.6.0.0 dated 2025-04-03

Title: Inference for Panel Partially Observed Markov Processes
Description: Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.
Author: Carles Breto [aut] , Edward L. Ionides [aut] , Aaron A. King [aut] , Jesse Wheeler [aut, cre] , Aaron Abkemeier [ctb]
Maintainer: Jesse Wheeler <jeswheel@umich.edu>

Diff between panelPomp versions 1.6.0.0 dated 2025-04-03 and 1.7.0.0 dated 2025-05-09

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Package networksem updated to version 0.4 with previous version 0.3 dated 2025-03-20

Title: Network Structural Equation Modeling
Description: Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at <https://bigsem.psychstat.org>.
Author: Zhiyong Zhang [aut, cre] , Ziqian Xu [aut]
Maintainer: Zhiyong Zhang <johnnyzhz@gmail.com>

Diff between networksem versions 0.3 dated 2025-03-20 and 0.4 dated 2025-05-09

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New package MVSKmod with initial version 0.1.0
Package: MVSKmod
Title: Matrix-Variate Skew Linear Regression Models
Version: 0.1.0
Maintainer: Samuel Soon <samksoon2@gmail.com>
Description: An implementation of the alternating expectation conditional maximization (AECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res, and covariates in gaad_cov. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: Bessel, clusterGeneration, DistributionUtils, matlib, maxLik, truncnorm, pracma
URL: https://github.com/soonsk-vcu/MVSKmod
BugReports: https://github.com/soonsk-vcu/MVSKmod/issues
NeedsCompilation: no
Packaged: 2025-05-07 19:56:27 UTC; soonsk
Author: Samuel Soon [aut, cre], Dipankar Bandyopadhyay [aut], Qingyang Liu [aut]
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2025-05-09 14:10:13 UTC

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Package gofCopula updated to version 0.4-2 with previous version 0.4-1 dated 2021-04-22

Title: Goodness-of-Fit Tests for Copulae
Description: Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.
Author: Simon Trimborn [aut, cre], Ostap Okhrin [aut], Martin Waltz [aut]
Maintainer: Simon Trimborn <trimborn.econometrics@gmail.com>

Diff between gofCopula versions 0.4-1 dated 2021-04-22 and 0.4-2 dated 2025-05-09

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Package GGIRread updated to version 1.0.5 with previous version 1.0.4 dated 2025-03-31

Title: Wearable Accelerometer Data File Readers
Description: Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' <https://activinsights.com/>, .bin-format from GENEA devices (not for sale), and .cwa-format from 'Axivity' <https://axivity.com>. Further, it has functions for reading text files with epoch level aggregates from 'Actical', 'Fitbit', 'Actiwatch', 'ActiGraph', and 'PhilipsHealthBand'. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.
Author: Vincent T van Hees [aut, cre], Patrick Bos [aut] , Lena Kushleyeva [ctb], Jing Hua Zhao [ctb], Evgeny Mirkes [ctb], Dan Jackson [ctb], Jairo H Migueles [ctb], Medical Research Council UK [cph, fnd], Accelting [cph, fnd]
Maintainer: Vincent T van Hees <v.vanhees@accelting.com>

Diff between GGIRread versions 1.0.4 dated 2025-03-31 and 1.0.5 dated 2025-05-09

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More information about GGIRread at CRAN
Permanent link

New package DIETCOST with initial version 1.0.0.0
Package: DIETCOST
Title: Calculate the Cost and Environmental Impact of a Ideal Diet
Version: 1.0.0.0
Encoding: UTF-8
Imports: readxl, rlang, dplyr, tidyselect, stats, xlsx, magrittr
Depends: R (>= 2.10)
LazyData: true
Description: Easily perform a Monte Carlo simulation to evaluate the cost and carbon, ecological, and water footprints of a set of ideal diets. Pre-processing tools are also available to quickly treat the data, along with basic statistical features to analyze the simulation results — including the ability to establish confidence intervals for selected parameters, such as nutrients and price/emissions. A 'standard version' of the datasets employed is included as well, allowing users easy access to customization. This package brings to R the 'Python' software initially developed by Vandevijvere, Young, Mackay, Swinburn and Gahegan (2018) <doi:10.1186/s12966-018-0648-6>.
License: MIT + file LICENSE
URL: https://github.com/hbracarense/dietcost
BugReports: https://github.com/hbracarense/dietcost/issues
NeedsCompilation: no
Packaged: 2025-05-07 19:28:15 UTC; hbrac
Author: Henrique Bracarense [cre, aut] , Thais Marquezine [aut] , Rafael Claro [aut]
Maintainer: Henrique Bracarense <hbracarense@hotmail.com>
Repository: CRAN
Date/Publication: 2025-05-09 14:10:16 UTC

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New package TBA with initial version 0.1.0
Package: TBA
Title: Collection of 'shiny' Apps for Tree Breeding Analysis
Version: 0.1.0
Description: A collection of interactive 'shiny' applications for performing comprehensive analyses in the field of tree breeding and genetics. The package is designed to assist users in visualizing and interpreting experimental data through a user-friendly interface. Each application is launched via a simple function, and users can upload data in 'Excel' format for analysis. For more information, refer to Singh, R.K. and Chaudhary, B.D. (1977, ISBN:9788176633079).
Maintainer: Bijoy Chanda <bijoychanda08@gmail.com>
License: GPL-3
Encoding: UTF-8
Imports: shiny, ggplot2, readxl, reshape2, shinybusy
NeedsCompilation: no
Suggests: testthat (>= 3.0.0)
Packaged: 2025-05-07 18:32:57 UTC; Bijoy Chanda
Author: K. Rajarajan [aut, ctb], Bijoy Chanda [aut, ctb, cre], A. Arunachalam [ctb], A. K. Handa [ctb]
Repository: CRAN
Date/Publication: 2025-05-09 14:00:02 UTC

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New package MazamaRollUtils with initial version 0.1.4
Package: MazamaRollUtils
Title: Efficient Rolling Functions
Version: 0.1.4
Date: 2025-05-07
Maintainer: Jonathan Callahan <jonathan.s.callahan@gmail.com>
Description: A suite of compiled functions calculating rolling mins, means, maxes and other statistics. This package is designed to meet the needs of data processing systems for environmental time series.
License: GPL-3
URL: https://github.com/MazamaScience/MazamaRollUtils
BugReports: https://github.com/MazamaScience/MazamaRollUtils/issues
Depends: R (>= 4.0.0)
Imports: Rcpp (>= 1.0.10),
Suggests: knitr, markdown, rmarkdown, roxygen2, testthat (>= 3.2.0), zoo
LinkingTo: Rcpp
Encoding: UTF-8
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: yes
Packaged: 2025-05-07 19:00:13 UTC; jonathancallahan
Author: Jonathan Callahan [aut, cre], Hans Martin [aut]
Repository: CRAN
Date/Publication: 2025-05-09 14:00:05 UTC

More information about MazamaRollUtils at CRAN
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Package Apoderoides updated to version 3.0.1 with previous version 3.0.0 dated 2024-07-10

Title: Prioritize and Delete Erroneous Taxa in a Large Phylogenetic Tree
Description: Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score.
Author: Satoshi Aoki [aut, cph, cre], Keita Fukasawa [ctb]
Maintainer: Satoshi Aoki <aokis1ll1@gmail.com>

Diff between Apoderoides versions 3.0.0 dated 2024-07-10 and 3.0.1 dated 2025-05-09

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Package TiPS updated to version 1.3.0 with previous version 1.2.3 dated 2023-05-23

Title: Trajectories and Phylogenies Simulator
Description: Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) <doi:10.1111/2041-210X.14038>.
Author: Gonche Danesh [aut, cre, cph], Emma Saulnier [aut, cph], Olivier Gascuel [aut, cph], Marc Choisy [aut, cph, ths], Samuel Alizon [aut, cph, ths]
Maintainer: Gonche Danesh <gonche.danesh@gmail.com>

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 TiPS-1.3.0/TiPS/DESCRIPTION                                                                       |   12 
 TiPS-1.3.0/TiPS/MD5                                                                               |  322 -
 TiPS-1.3.0/TiPS/R/treeSimulator.R                                                                 |  620 +-
 TiPS-1.3.0/TiPS/build/vignette.rds                                                                |binary
 TiPS-1.3.0/TiPS/inst/doc/TiPS.R                                                                   |  530 +-
 TiPS-1.3.0/TiPS/inst/doc/TiPS.html                                                                | 2642 +++++++---
 TiPS-1.3.0/TiPS/inst/include/Reaction.h                                                           |   15 
 TiPS-1.3.0/TiPS/src/Phyloepid.cpp                                                                 |    9 
 TiPS-1.3.0/TiPS/src/Reaction.cpp                                                                  |   94 
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More information about TiPS at CRAN
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Package douconca updated to version 1.2.3 with previous version 1.2.2 dated 2024-12-02

Title: Double Constrained Correspondence Analysis for Trait-Environment Analysis in Ecology
Description: Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using the 'vegan' package and native R code. Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the al [...truncated...]
Author: Cajo J.F ter Braak [aut] , Bart-Jan van Rossum [aut, cre]
Maintainer: Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>

Diff between douconca versions 1.2.2 dated 2024-12-02 and 1.2.3 dated 2025-05-09

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Package terra updated to version 1.8-50 with previous version 1.8-42 dated 2025-04-02

Title: Spatial Data Analysis
Description: Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).
Author: Robert J. Hijmans [cre, aut] , Marcia Barbosa [ctb], Roger Bivand [ctb] , Andrew Brown [ctb], Michael Chirico [ctb], Emanuele Cordano [ctb] , Krzysztof Dyba [ctb] , Edzer Pebesma [ctb] , Barry Rowlingson [ctb], Michael D. Sumner [ctb]
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

Diff between terra versions 1.8-42 dated 2025-04-02 and 1.8-50 dated 2025-05-09

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New package vmsae with initial version 0.1.0
Package: vmsae
Title: Variational Multivariate Spatial Small Area Estimation
Version: 0.1.0
Maintainer: Zhenhua Wang <zhenhua.wang@missouri.edu>
Description: Variational Autoencoded Multivariate Spatial Fay-Herriot models are designed to efficiently estimate population parameters in small area estimation. This package implements the variational generalized multivariate spatial Fay-Herriot model (VGMSFH) using 'NumPyro' and 'PyTorch' backends, as demonstrated by Wang, Parker, and Holan (2025) <doi:10.48550/arXiv.2503.14710>. The 'vmsae' package provides utility functions to load weights of the pretrained variational autoencoders (VAEs) as well as tools to train custom VAEs tailored to users specific applications.
Depends: R (>= 3.5.0)
Imports: dplyr, ggplot2, gridExtra, sf, tidyr, reticulate, methods, rlang
URL: https://github.com/zhenhua-wang/vmsae
BugReports: https://github.com/zhenhua-wang/vmsae/issues
License: MIT + file LICENSE
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2025-05-07 18:40:22 UTC; zhenhua
Author: Zhenhua Wang [aut, cre], Paul A. Parker [aut, res], Scott H. Holan [aut, res]
Repository: CRAN
Date/Publication: 2025-05-09 10:30:02 UTC

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Package IsoplotRgui updated to version 6.6 with previous version 6.5 dated 2025-02-13

Title: Web Interface to 'IsoplotR'
Description: Provides a graphical user interface to the 'IsoplotR' package for radiometric geochronology. The GUI runs in an internet browser and can either be used offline, or hosted on a server to provide online access to the 'IsoplotR' toolbox.
Author: Pieter Vermeesch [aut, cre], Tim Band [ctb]
Maintainer: Pieter Vermeesch <p.vermeesch@ucl.ac.uk>

Diff between IsoplotRgui versions 6.5 dated 2025-02-13 and 6.6 dated 2025-05-09

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Package HelpersMG updated to version 6.5 with previous version 6.4.5 dated 2025-03-28

Title: Tools for Environmental Analyses, Ecotoxicology and Various R Functions
Description: Contains miscellaneous functions useful for managing 'NetCDF' files (see <https://en.wikipedia.org/wiki/NetCDF>), get moon phase and time for sun rise and fall, tide level, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Author: Marc Girondot [aut, cre]
Maintainer: Marc Girondot <marc.girondot@gmail.com>

Diff between HelpersMG versions 6.4.5 dated 2025-03-28 and 6.5 dated 2025-05-09

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New package DTEBOP2 with initial version 1.0.3
Package: DTEBOP2
Title: Bayesian Optimal Phase II Randomized Clinical Trial Design with Delayed Outcomes
Version: 1.0.3
Description: Implements a Bayesian Optimal Phase II design (DTE-BOP2) for trials with delayed treatment effects, particularly relevant to immunotherapy studies where treatment benefits may emerge after a delay. The method builds upon the BOP2 framework and incorporates uncertainty in the delay timepoint through a truncated gamma prior, informed by expert knowledge or default settings. Supports two-arm trial designs with functionality for sample size determination, interim and final analyses, and comprehensive simulation under various delay and design scenarios. Ensures rigorous type I and II error control while improving trial efficiency and power when the delay effect is present. A manuscript describing the methodology is under development and will be formally referenced upon publication.
License: GPL-3
Encoding: UTF-8
Imports: truncdist, doParallel, foreach, parallel, invgamma
Suggests: knitr, rmarkdown, survRM2adapt, ggplot2, reconstructKM, survival, survminer
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-05-07 18:49:07 UTC; zcai13
Author: Zhongheng Cai [aut, cre], Haitao Pan [aut]
Maintainer: Zhongheng Cai <zhonghengcai123@gmail.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2025-05-09 10:40:06 UTC

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Package bullseye updated to version 1.0.0 with previous version 0.1.2 dated 2025-01-28

Title: Visualising Multiple Pairwise Variable Correlations and Other Scores
Description: We provide a tidy data structure and visualisations for multiple or grouped variable correlations, general association measures scagnostics and other pairwise scores suitable for numerical, ordinal and nominal variables. Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation.
Author: Amit Chinwan [aut], Catherine Hurley [aut, cre]
Maintainer: Catherine Hurley <catherine.hurley@mu.ie>

Diff between bullseye versions 0.1.2 dated 2025-01-28 and 1.0.0 dated 2025-05-09

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New package wex with initial version 0.1.0
Package: wex
Title: Compute the Exact Observation Weights for the Kalman Filter and Smoother
Version: 0.1.0
Maintainer: Tim Ginker <timginker@gmail.com>
Description: Computes the exact observation weights for the Kalman filter and smoother, based on the method described in Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>. The package supports in-depth exploration of state-space models, enabling researchers and practitioners to extract meaningful insights from time series data. This functionality is especially valuable in dynamic factor models, where the computed weights can be used to decompose the contributions of individual variables to the latent factors. See the README file for examples.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: FKF
LazyData: true
URL: https://github.com/timginker/wex
BugReports: https://github.com/timginker/wex/issues
NeedsCompilation: no
Packaged: 2025-05-07 10:46:59 UTC; timgi
Author: Tim Ginker [aut, cre, cph]
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2025-05-09 09:50:02 UTC

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New package svycoxme with initial version 1.0.0
Package: svycoxme
Title: Mixed-Effects Cox Models for Complex Samples
Version: 1.0.0
Description: Mixed-effect proportional hazards models for multistage stratified, cluster-sampled, unequally weighted survey samples. Provides variance estimation by Taylor series linearisation or replicate weights.
Depends: R (>= 4.1.0)
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: survey, coxme, survival, Rcpp, lme4, Matrix, future, parallelly
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, future.apply, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://github.com/bdrayton/svycoxme
BugReports: https://github.com/bdrayton/svycoxme/issues
NeedsCompilation: yes
Packaged: 2025-05-07 01:35:10 UTC; bdra011
Author: Bradley Drayton [aut, cre, cph]
Maintainer: Bradley Drayton <brad.drayton@auckland.ac.nz>
Repository: CRAN
Date/Publication: 2025-05-09 09:50:06 UTC

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New package SAFEPG with initial version 0.0.1
Package: SAFEPG
Title: A Novel SAFE Model for Predicting Climate-Related Extreme Losses
Version: 0.0.1
Date: 2025-05-01
Maintainer: Qian Tang <qian-tang@uiowa.edu>
Description: The goal of 'SAFEPG' is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment.
Depends: R (>= 3.5.0)
License: GPL-2
Imports: stats, Matrix, methods
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2025-05-07 00:14:45 UTC; qtang7
Author: Qian Tang [aut, cre], Yikai Zhang [aut], Boxiang Wang [aut]
Repository: CRAN
Date/Publication: 2025-05-09 09:40:02 UTC

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New package forensim with initial version 4.3.3
Package: forensim
Title: Interpretation of Forensic DNA Mixtures
Version: 4.3.3
Date: 2025-05-02
Maintainer: Maarten Kruijver <maarten.kruijver@esr.cri.nz>
Depends: methods, tcltk,tcltk2,tkrplot
Description: Statistical methods and simulation tools for the interpretation of forensic DNA mixtures. The methods implemented are described in Haned et al. (2011) <doi:10.1111/j.1556-4029.2010.01550.x>, Haned et al. (2012) <doi:10.1016/j.fsigen.2012.11.002> and Gill & Haned (2013) <doi:10.1016/j.fsigen.2012.08.008>.
License: GPL (>= 2)
LazyLoad: yes
SystemRequirements: Tcl/Tk package TkTable
NeedsCompilation: yes
Packaged: 2025-05-07 15:33:43 UTC; mkruijver
Author: Hinda Haned [aut], Oyvind Bleka [ctb], Maarten Kruijver [cre]
Repository: CRAN
Date/Publication: 2025-05-09 10:00:02 UTC

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New package days2lessons with initial version 0.1.2
Package: days2lessons
Title: Distributes Teachers Lessons On Days in a Balanced Manner
Version: 0.1.2
Description: The set of teacher/class lessons is completed with a column that allocates a day to each lesson, so that the distribution of lessons by day, by class, and by teacher is as uniform as possible. <https://vlad.bazon.net/>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: ro
Depends: R (>= 3.5.0)
LazyData: true
Imports: dplyr, magrittr, purrr, rlang, stats
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-05-07 11:33:44 UTC; vb
Author: Vlad Bazon [aut, cre]
Maintainer: Vlad Bazon <vlad.bazon@gmail.com>
Repository: CRAN
Date/Publication: 2025-05-09 09:50:10 UTC

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Package CvmortalityMult updated to version 1.1.0 with previous version 1.0.9 dated 2025-02-13

Title: Cross-Validation for Multi-Population Mortality Models
Description: Implementation of cross-validation method for testing the forecasting accuracy of several multi-population mortality models. The family of multi-population includes several multi-population mortality models proposed through the actuarial and demography literature. The package includes functions for fitting and forecast the mortality rates of several populations. Additionally, we include functions for testing the forecasting accuracy of different multi-population models. References. Atance, D., Debon, A., and Navarro, E. (2020) <doi:10.3390/math8091550>. Bergmeir, C. & Benitez, J.M. (2012) <doi:10.1016/j.ins.2011.12.028>. Debon, A., Montes, F., & Martinez-Ruiz, F. (2011) <doi:10.1007/s13385-011-0043-z>. Lee, R.D. & Carter, L.R. (1992) <doi:10.1080/01621459.1992.10475265>. Russolillo, M., Giordano, G., & Haberman, S. (2011) <doi:10.1080/03461231003611933>. Santolino, M. (2023) <doi:10.3390/risks11100170>.
Author: David Atance [aut, cre] , Ana Debon [aut]
Maintainer: David Atance <david.atance@uah.es>

Diff between CvmortalityMult versions 1.0.9 dated 2025-02-13 and 1.1.0 dated 2025-05-09

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 MD5                        |   28 +++----
 NAMESPACE                  |    2 
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New package codestral with initial version 0.0.1
Package: codestral
Title: Chat and FIM with 'Codestral'
Version: 0.0.1
Date: 2025-05-07
Description: Create an addin in 'Rstudio' to do fill-in-the-middle (FIM) and chat with latest Mistral AI models for coding, 'Codestral' and 'Codestral Mamba'. For more details about 'Mistral AI API': <https://docs.mistral.ai/getting-started/quickstart/> and <https://docs.mistral.ai/api/>. For more details about 'Codestral' model: <https://mistral.ai/news/codestral>; about 'Codestral Mamba': <https://mistral.ai/news/codestral-mamba>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, httr, jsonlite, magrittr, rstudioapi, stringr, utils
Depends: R (>= 4.1)
LazyData: true
Suggests: testthat (>= 3.0.0)
URL: https://urbs-dev.github.io/codestral/
BugReports: https://github.com/urbs-dev/codestral/issues
NeedsCompilation: no
Packaged: 2025-05-07 08:37:11 UTC; marcgrossouvre2
Author: Marc Grossouvre [aut, cre], URBS company [cph, fnd]
Maintainer: Marc Grossouvre <marcgrossouvre@urbs.fr>
Repository: CRAN
Date/Publication: 2025-05-09 09:50:13 UTC

More information about codestral at CRAN
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Package Rgff updated to version 0.1.7 with previous version 0.1.6 dated 2022-09-30

Title: R Utilities for GFF Files
Description: R utilities for gff files, either general feature format (GFF3) or gene transfer format (GTF) formatted files. This package includes functions for producing summary stats, check for consistency and sorting errors, conversion from GTF to GFF3 format, file sorting, visualization and plotting of feature hierarchy, and exporting user defined feature subsets to SAF format. This tool was developed by the BioinfoGP core facility at CNB-CSIC.
Author: Juan Antonio Garcia-Martin [cre, aut] , Juan Carlos Oliveros [aut, ctb] , Rafael Torres-Perez [aut, ctb]
Maintainer: Juan Antonio Garcia-Martin <ja.garcia@cnb.csic.es>

Diff between Rgff versions 0.1.6 dated 2022-09-30 and 0.1.7 dated 2025-05-09

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Package GeoModels updated to version 2.1.4 with previous version 2.1.3 dated 2025-05-02

Title: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis
Description: Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. Se [...truncated...]
Author: Moreno Bevilacqua [aut, cre, cph], Victor Morales-Onate [ctb], Francisco Cuevas-Pacheco [ctb], Christian Caamano-Carrillo [ctb]
Maintainer: Moreno Bevilacqua <moreno.bevilacqua89@gmail.com>

Diff between GeoModels versions 2.1.3 dated 2025-05-02 and 2.1.4 dated 2025-05-09

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Package Compind updated to version 3.3 with previous version 3.2 dated 2025-01-09

Title: Composite Indicators Functions
Description: A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.
Author: Francesco Vidoli [aut, cre], Elisa Fusco [aut]
Maintainer: Francesco Vidoli <fvidoli@gmail.com>

Diff between Compind versions 3.2 dated 2025-01-09 and 3.3 dated 2025-05-09

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Package forestdata (with last version 0.3.0) was removed from CRAN

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

2025-04-03 0.3.0

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Package fmds (with last version 0.1.2) was removed from CRAN

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

2025-04-25 0.1.2

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Package Radviz updated to version 0.9.4 with previous version 0.9.3 dated 2022-03-25

Title: Project Multidimensional Data in 2D Space
Description: An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) (<doi:10.1145/331770.331775>) for original implementation, see Di Caro *et al* (2012) (<doi:10.1007/978-3-642-13672-6_13>), for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.
Author: Yann Abraham [aut, cre], Nicolas Sauwen [aut]
Maintainer: Yann Abraham <yann.abraham@gmail.com>

Diff between Radviz versions 0.9.3 dated 2022-03-25 and 0.9.4 dated 2025-05-09

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Package connector updated to version 0.1.1 with previous version 0.1.0 dated 2025-04-25

Title: Streamlining Data Access in Clinical Research
Description: Provides a consistent interface for connecting R to various data sources including file systems and databases. Designed for clinical research, 'connector' streamlines access to 'ADAM', 'SDTM' for example. It helps to deal with multiple data formats through a standardized API and centralized configuration.
Author: Cervan Girard [aut, cre], Aksel Thomsen [aut], Vladimir Obucina [aut], Novo Nordisk A/S [cph]
Maintainer: Cervan Girard <cgid@novonordisk.com>

Diff between connector versions 0.1.0 dated 2025-04-25 and 0.1.1 dated 2025-05-09

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