Sun, 26 Oct 2025

Package tind updated to version 0.2.1 with previous version 0.2 dated 2025-10-22

Title: A Common Representation of Time Indices of Different Types
Description: Provides an easy-to-use tind class representing time indices of different types (years, quarters, months, ISO 8601 weeks, dates, time of day, date-time, and arbitrary integer/numeric indices). Includes an extensive collection of functions for calendrical computations (including business applications), index conversions, index parsing, and other operations. Auxiliary classes representing time differences and time intervals (with set operations and index matching functionality) are also provided. All routines have been optimised for speed in order to facilitate computations on large datasets. More details regarding calendars in general and calendrical algorithms can be found in "Calendar FAQ" by Claus Tøndering <https://www.tondering.dk/claus/calendar.html>.
Author: Grzegorz Klima [aut, cre, cph]
Maintainer: Grzegorz Klima <dever@post.pl>

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Package ino updated to version 1.2.0 with previous version 1.1.1 dated 2025-10-21

Title: Initialization of Numerical Optimization
Description: Analysis of the initialization for numerical optimization of real-valued functions, particularly likelihood functions of statistical models. See <https://loelschlaeger.de/ino/> for more details.
Author: Lennart Oelschlaeger [aut, cre] , Marius Oetting [aut] , Dietmar Bauer [ctb]
Maintainer: Lennart Oelschlaeger <oelschlaeger.lennart@gmail.com>

Diff between ino versions 1.1.1 dated 2025-10-21 and 1.2.0 dated 2025-10-26

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Package excerptr readmission to version 2.1.0 with previous version 2.0.1 dated 2021-08-04

Title: Excerpt Structuring Comments from Your Code File and Set a Table of Contents
Description: Ever read or wrote source files containing sectioning comments? If these comments are markdown style section comments, you can excerpt them and set a table of contents using the 'python' package 'excerpts' (<https://pypi.org/project/excerpts/>).
Author: Andreas Dominik Cullmann [aut, cre]
Maintainer: Andreas Dominik Cullmann <fvafrcu@mailbox.org>

This is a re-admission after prior archival of version 2.0.1 dated 2021-08-04

Diff between excerptr versions 2.0.1 dated 2021-08-04 and 2.1.0 dated 2025-10-26

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New package contextFind with initial version 1.0.1
Package: contextFind
Title: Find Code Snippets with Context and Click to Navigate Directly to Results
Version: 1.0.1
Description: Search across R files with contextual results, highlights and clickable links. Includes an add-in for further workflow enhancement.
License: CC0
Encoding: UTF-8
Imports: rstudioapi, cli, shiny, miniUI
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-10-22 19:20:42 UTC; SteveJackson
Author: Steve Jackson [aut, cre]
Maintainer: Steve Jackson <s87jackson@gmail.com>
Repository: CRAN
Date/Publication: 2025-10-26 20:30:08 UTC

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Package tidynorm updated to version 0.4.0 with previous version 0.3.1 dated 2025-10-06

Title: Tools for Tidy Vowel Normalization
Description: An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) <doi:10.5334/labphon.196> Lobanov, Boris (1971) <doi:10.1121/1.1912396> Nearey, Terrance M. (1978) <https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf> Syrdal, Ann K., and Gopal, H. S. (1986) <doi:10.1121/1.393381> Watt, Dominic, and Fabricius, Anne (2002) <https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.
Author: Josef Fruehwald [cre, aut, cph]
Maintainer: Josef Fruehwald <JoFrhwld@gmail.com>

Diff between tidynorm versions 0.3.1 dated 2025-10-06 and 0.4.0 dated 2025-10-26

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Package filibustr updated to version 0.5.0 with previous version 0.4.1 dated 2025-08-19

Title: Data Utilities for Congressional Research
Description: Provides easy-to-understand and consistent interfaces for accessing data on the U.S. Congress. The functions in 'filibustr' streamline the process for importing data on Congress into R, removing the need to download and work from CSV files and the like. Data sources include 'Voteview' (<https://voteview.com/>), the U.S. Senate website (<https://www.senate.gov/>), and more.
Author: Max Feinleib [aut, cre, cph]
Maintainer: Max Feinleib <mhfeinleib@gmail.com>

Diff between filibustr versions 0.4.1 dated 2025-08-19 and 0.5.0 dated 2025-10-26

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Package FBFsearch updated to version 1.3 with previous version 1.2 dated 2022-08-08

Title: Algorithm for Searching the Space of Gaussian Directed Acyclic Graph Models Through Moment Fractional Bayes Factors
Description: We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm uses moment fractional Bayes factors (MFBF) and is suitable for learning sparse graphs. The algorithm is implemented using Armadillo, an open-source C++ linear algebra library.
Author: Davide Altomare [aut, cre], Guido Consonni [aut], Luca La Rocca [aut]
Maintainer: Davide Altomare <davide.altomare@gmail.com>

Diff between FBFsearch versions 1.2 dated 2022-08-08 and 1.3 dated 2025-10-26

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Package fastbeta updated to version 0.5.1 with previous version 0.5.0 dated 2025-10-15

Title: Fast Approximation of Time-Varying Infectious Disease Transmission Rates
Description: A fast method for approximating time-varying infectious disease transmission rates from disease incidence time series and other data, based on a discrete time approximation of an SEIR model, as analyzed in Jagan et al. (2020) <doi:10.1371/journal.pcbi.1008124>.
Author: Mikael Jagan [aut, cre] , David Earn [ctb]
Maintainer: Mikael Jagan <jaganmn@mcmaster.ca>

Diff between fastbeta versions 0.5.0 dated 2025-10-15 and 0.5.1 dated 2025-10-26

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Package FARS updated to version 0.7.0 with previous version 0.6.1 dated 2025-10-08

Title: Factor-Augmented Regression Scenarios
Description: Provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.
Author: Gian Pietro Bellocca [aut, cre], Ignacio Garron [aut], Vladimir Rodriguez-Caballero [aut], Esther Ruiz [aut]
Maintainer: Gian Pietro Bellocca <gbellocc@est-econ.uc3m.es>

Diff between FARS versions 0.6.1 dated 2025-10-08 and 0.7.0 dated 2025-10-26

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New package fairGNN with initial version 0.1.0
Package: fairGNN
Title: Fairness-Aware Gated Neural Networks
Version: 0.1.0
Description: Tools for training and analysing fairness-aware gated neural networks for subgroup-aware prediction and interpretation in clinical datasets. Methods draw on prior work in mixture-of-experts neural networks by Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>, fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>, and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) <doi:10.1016/j.jpsychires.2016.03.016>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, tibble, ggplot2, readr, pROC, magrittr, tidyr, purrr, utils, stats, ggalluvial, tidyselect
Suggests: knitr, torch, testthat, readxl, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/rhysholland/fairGNN
BugReports: https://github.com/rhysholland/fairGNN/issues
Depends: R (>= 4.1.0)
SystemRequirements: Optional 'LibTorch' backend; install via torch::install_torch().
NeedsCompilation: no
Packaged: 2025-10-22 11:32:41 UTC; k2584926
Author: Rhys Holland [aut, cre]
Maintainer: Rhys Holland <rhys.holland@icloud.com>
Repository: CRAN
Date/Publication: 2025-10-26 19:50:10 UTC

More information about fairGNN at CRAN
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Package boinet updated to version 1.5.0 with previous version 1.4.0 dated 2025-06-26

Title: Conduct Simulation Study of Bayesian Optimal Interval Design with BOIN-ET Family
Description: Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.
Author: Yusuke Yamaguchi [aut, cre], Kentaro Takeda [aut]
Maintainer: Yusuke Yamaguchi <yamagubed@gmail.com>

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Package VBel updated to version 1.1.6 with previous version 1.1.0 dated 2024-12-05

Title: Variational Bayes for Fast and Accurate Empirical Likelihood Inference
Description: Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.
Author: Weichang Yu [aut] , Jeremy Lim [cre, aut]
Maintainer: Jeremy Lim <jeremy.lim@unimelb.edu.au>

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Package tidyHeatmap updated to version 1.12.3 with previous version 1.12.2 dated 2025-07-22

Title: A Tidy Implementation of Heatmap
Description: This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes.
Author: Stefano Mangiola [aut, cre], Anthony Papenfuss [ctb]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>

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New package Rsearch with initial version 1.0.0
Package: Rsearch
Title: Processing and Analyzing Amplicon Sequence Data
Version: 1.0.0
Maintainer: Cassandra Stamsaas <cassandra.hjortdahl@nmbu.no>
Description: Processing and analysis of targeted sequencing data. The package provides a user-friendly interface for core 'VSEARCH' (Rognes et al. (2016), <doi:10.7717/peerj.2584>) functions, in addition to tools for visualization and parameter tuning.
License: GPL (>= 3)
Encoding: UTF-8
Imports: ape (>= 5.8.1), cowplot (>= 1.1.3), dplyr, ggExtra (>= 0.10.1), ggplot2 (>= 3.5.1), microseq (>= 2.1.6), phyloseq (>= 1.50.0), RColorBrewer (>= 1.1.3), readr (>= 2.1.5), stats, stringr (>= 1.5.1), tibble (>= 3.2.1), tidyr (>= 1.3.1)
Suggests: rmarkdown, testthat (>= 3.0.0), withr (>= 3.0.2)
URL: https://cassandrahjo.github.io/Rsearch/, https://github.com/CassandraHjo/Rsearch/
BugReports: https://github.com/CassandraHjo/Rsearch/issues/
Depends: R (>= 4.1)
NeedsCompilation: no
Packaged: 2025-10-22 05:46:02 UTC; cassandrahjortdahl
Author: Cassandra Stamsaas [cre, aut], Lars Snipen [aut], Torbjoern Rognes [aut], Hilde Vinje [aut]
Repository: CRAN
Date/Publication: 2025-10-26 18:40:14 UTC

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Package ntfy updated to version 0.1.0 with previous version 0.0.6 dated 2024-11-06

Title: Lightweight Wrapper to the 'ntfy.sh' Service
Description: The 'ntfy' (pronounce: notify) service is a simple HTTP-based pub-sub notification service. It allows you to send notifications to your phone or desktop via scripts from any computer, entirely without signup, cost or setup. It's also open source if you want to run your own. Visit <https://ntfy.sh> for more details.
Author: Jonathan Carroll [aut, cre] , Andrew Heiss [ctb] , Hadley Wickham [ctb]
Maintainer: Jonathan Carroll <rpkg@jcarroll.com.au>

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Package mclink updated to version 1.1.1 with previous version 1.1 dated 2025-08-22

Title: Metabolic Pathway Completeness and Abundance Calculation
Description: Provides tools for analyzing metabolic pathway completeness, abundance, and transcripts using KEGG Orthology (KO) data from (meta)genomic and (meta)transcriptomic studies. Supports both completeness (presence/absence) and abundance-weighted analyses. Includes built-in KEGG reference datasets. For more details see Li et al. (2023) <doi:10.1038/s41467-023-42193-7>.
Author: Liuyang Li [aut, cre]
Maintainer: Liuyang Li <cyanobacteria@yeah.net>

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New package leakr with initial version 0.1.0
Package: leakr
Title: Data Leakage Detection Tools for Machine Learning
Version: 0.1.0
Description: Provides utilities to detect common data leakage patterns including train/test contamination, temporal leakage, and data duplication, enhancing model reliability and reproducibility in machine learning workflows. Generates diagnostic reports and visual summaries to support data validation. Methods based on best practices from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0387848570).
Imports: ggplot2, arrow, data.table, digest, htmltools, openxlsx, readxl, stringr, workflows, jsonlite
Suggests: testthat (>= 3.0.0), caret, mlr3, tidymodels, knitr, rmarkdown
License: MIT + file LICENSE
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-10-22 08:43:45 UTC; Isabella
Author: Cheryl Isabella Lim [aut, cre]
Maintainer: Cheryl Isabella Lim <cheryl.academic@gmail.com>
Repository: CRAN
Date/Publication: 2025-10-26 18:50:02 UTC

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Package bnRep updated to version 0.0.6 with previous version 0.0.5 dated 2025-07-23

Title: A Repository of Bayesian Networks from the Academic Literature
Description: A collection of Bayesian networks (discrete, Gaussian, and conditional linear Gaussian) collated from recent academic literature. The 'bnRep_summary' object provides an overview of the Bayesian networks in the repository and the package documentation includes details about the variables in each network. A Shiny app to explore the repository can be launched with 'bnRep_app()' and is available online at <https://manueleleonelli.shinyapps.io/bnRep>. Reference: 'M. Leonelli' (2025) <doi:10.1016/j.neucom.2025.129502>.
Author: Manuele Leonelli [aut, cre, cph]
Maintainer: Manuele Leonelli <manuele.leonelli@ie.edu>

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Package BayesRegDTR updated to version 1.1.1 with previous version 1.0.1 dated 2025-06-27

Title: Bayesian Regression for Dynamic Treatment Regimes
Description: Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) <doi:10.1093/jrsssb/qkad016> Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing.
Author: Jeremy Lim [aut, cre], Weichang Yu [aut]
Maintainer: Jeremy Lim <jeremylim23@gmail.com>

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Package PRTree updated to version 1.0.2 with previous version 1.0.1 dated 2025-10-18

Title: Probabilistic Regression Trees
Description: Implementation of Probabilistic Regression Trees (PRTree), providing functions for model fitting and prediction, with specific adaptations to handle missing values. The main computations are implemented in 'Fortran' for high efficiency. The package is based on the PRTree methodology described in Alkhoury et al. (2020), "Smooth and Consistent Probabilistic Regression Trees" <https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>. Details on the treatment of missing data and implementation aspects are presented in Prass, T.S.; Neimaier, A.S.; Pumi, G. (2025), "Handling Missing Data in Probabilistic Regression Trees: Methods and Implementation in R" <doi:10.48550/arXiv.2510.03634>.
Author: Alisson Silva Neimaier [aut] , Taiane Schaedler Prass [aut, ths, cre]
Maintainer: Taiane Schaedler Prass <taianeprass@gmail.com>

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Package missForest updated to version 1.6.1 with previous version 1.5 dated 2022-04-14

Title: Nonparametric Missing Value Imputation using Random Forest
Description: The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest (via 'ranger' or 'randomForest') trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.
Author: Daniel J. Stekhoven [aut, cre]
Maintainer: Daniel J. Stekhoven <stekhoven@nexus.ethz.ch>

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Package codebook (with last version 0.9.6) was removed from CRAN

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

2025-01-08 0.9.6
2024-10-14 0.9.5
2020-06-06 0.9.2

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