Fri, 13 Nov 2026

Package rplatypus updated to version 0.3.1 with previous version 0.2.1 dated 2026-11-04

Title: Computer Vision for Medical Imaging
Description: Semantic segmentation and object detection for medical images, from a folder of pictures to a trained model and a figure, in a handful of lines or one configuration file. Architectures, losses and training run on 'PyTorch' through 'pyplatypus', a 'Python' package which is installed and isolated automatically; nothing has to be set up by hand. R keeps the parts that make an analysis fast - reading and combining masks, drawing boxes, plotting, comparing models.
Author: Michal Maj [aut, cre]
Maintainer: Michal Maj <michalmaj116@gmail.com>

Diff between rplatypus versions 0.2.1 dated 2026-11-04 and 0.3.1 dated 2026-11-13

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Package rcccd updated to version 0.4.0 with previous version 0.3.2 dated 2023-04-24

Title: Class Cover Catch Digraph Classification
Description: Fit Class Cover Catch Digraph Classification models that can be used in machine learning. Pure and proper and random walk approaches are available. Methods are explained in Priebe et al. (2001) <doi:10.1016/S0167-7152(01)00129-8>, Priebe et al. (2003) <doi:10.1007/s00357-003-0003-7>, and Manukyan and Ceyhan (2016) <doi:10.48550/arXiv.1904.04564>.
Author: Fatih Saglam [aut, cre]
Maintainer: Fatih Saglam <saglamf89@gmail.com>

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Package nullcat updated to version 0.3.0 with previous version 0.2.0 dated 2026-04-06

Title: Null Models for Categorical and Continuous Community Matrices
Description: Provides null model algorithms for categorical and quantitative community ecology data. Extends classic binary null models (e.g., 'curveball', 'swap') to work with categorical data. Provides a stratified randomization framework for continuous data.
Author: Matthew Kling [aut, cre, cph]
Maintainer: Matthew Kling <mattkling@berkeley.edu>

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Package NNS updated to version 14.0 with previous version 13.3 dated 2026-10-30

Title: Nonlinear Nonparametric Statistics
Description: NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences. Designed for real-world data that violates symmetry, linearity, or distributional assumptions, NNS delivers a comprehensive suite of advanced statistical techniques, including: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic superiority / dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995, Second edition: <https://ovvo-financial.github.io/NNS/book/>).
Author: Fred Viole [aut, cre], Roberto Spadim [ctb], Rasheed Khoshnaw [ctb]
Maintainer: Fred Viole <ovvo.open.source@gmail.com>

Diff between NNS versions 13.3 dated 2026-10-30 and 14.0 dated 2026-11-13

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More information about NNS at CRAN
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Package corteza updated to version 0.8.0 with previous version 0.7.1 dated 2026-08-05

Title: AI Agent Runtime
Description: An agent runtime that gives Large Language Models (LLMs) from 'Anthropic' <https://www.anthropic.com/>, 'OpenAI' <https://openai.com/>, 'Moonshot' <https://www.moonshot.ai/>, and 'Ollama' <https://ollama.com/> direct access to a live R session with managed workspace state. Tools execute as R function calls with provenance tracking, and a deterministic retrieval system keeps relevant objects in context across turns. Three entry points: a shell command-line interface (CLI), a console read-eval-print-loop via chat(), and a Model Context Protocol (MCP) server via serve() for external clients.
Author: Troy Hernandez [aut, cre] , Grant McDermott [ctb] , Jorge Krzyzaniak [ctb], cornball.ai [cph]
Maintainer: Troy Hernandez <troy@cornball.ai>

Diff between corteza versions 0.7.1 dated 2026-08-05 and 0.8.0 dated 2026-11-13

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Package SMOTEWB updated to version 1.2.6 with previous version 1.2.5 dated 2025-10-16

Title: Imbalanced Resampling using SMOTE with Boosting (SMOTEWB)
Description: Provides the SMOTE with boosting (SMOTEWB) algorithm, where SMOTE is the synthetic minority over-sampling technique. See F. Sağlam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. It is a SMOTE-based resampling technique which creates synthetic data on the links between nearest neighbors. SMOTEWB uses boosting weights to determine where to generate new samples and automatically decides the number of neighbors for each sample. It is robust to noise and outperforms most of the alternatives according to Matthew Correlation Coefficient metric. Alternative resampling methods are also available in the package. This package is superseded by the 'imbalanceKit' package, which contains all of its methods and more.
Author: Fatih Saglam [aut, cre]
Maintainer: Fatih Saglam <saglamf89@gmail.com>

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Package PanelMatch updated to version 3.1.6 with previous version 3.1.5 dated 2026-10-01

Title: Matching Methods for Causal Inference with Time-Series Cross-Sectional Data
Description: Implements a set of methodological tools that enable researchers to apply matching methods to time-series cross-sectional data. Imai, Kim, and Wang (2023) <https://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results.
Author: In Song Kim [aut, cre], Adam Rauh [aut], Erik Wang [aut], Kosuke Imai [aut]
Maintainer: In Song Kim <insong@mit.edu>

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Package spmodel updated to version 0.15.0 with previous version 0.14.0 dated 2026-09-10

Title: Spatial Statistical Modeling and Prediction
Description: Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
Author: Michael Dumelle [aut, cre] , Matthew Heaton [ctb] , Matt Higham [aut] , Ryan A. Hill [ctb] , Michael Mahon [ctb] , Jay M. Ver Hoef [aut]
Maintainer: Michael Dumelle <Dumelle.Michael@epa.gov>

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Permanent link

Package LMest updated to version 4.0.1 with previous version 4.0.0 dated 2026-10-08

Title: Generalized Latent Markov Models
Description: Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.
Author: Francesco Bartolucci [aut, cre], Silvia Pandolfi [aut], Fulvia Pennoni [aut], Luca Brusa [ctb], Alessio Farcomeni [ctb], Alessio Serafini [ctb]
Maintainer: Francesco Bartolucci <francesco.bartolucci@unipg.it>

Diff between LMest versions 4.0.0 dated 2026-10-08 and 4.0.1 dated 2026-11-13

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More information about LMest at CRAN
Permanent link

Package basetable updated to version 1.4.6 with previous version 1.4.2 dated 2026-09-18

Title: Fast and Memory-Efficient Base R Table Manipulation
Description: A tabular data manipulation, exploration and validation toolkit with a base R-style interface (subset, transform, aggregate, merge, split) and no external computation dependency. Grouping, joins, ordering, filtering, reshaping and delimited-file reading run in a bundled 'C++' engine that uses multiple threads for the heavier operations. Grouped reducers accumulate in compiled code without materialising intermediate columns, so grouped aggregation and counting allocate close to nothing. Results are returned as an ordinary data frame with a light 'basetable' class.
Author: Imad El Badisy [aut, cre]
Maintainer: Imad El Badisy <elbadisyimad@gmail.com>

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Package ARPobservation updated to version 1.2.3 with previous version 1.2.2 dated 2023-08-25

Title: Tools for Simulating Direct Behavioral Observation Recording Procedures Based on Alternating Renewal Processes
Description: Tools for simulating data generated by direct observation recording. Behavior streams are simulated based on an alternating renewal process, given specified distributions of event durations and interim times. Different procedures for recording data can then be applied to the simulated behavior streams. Functions are provided for the following recording methods: continuous duration recording, event counting, momentary time sampling, partial interval recording, whole interval recording, and augmented interval recording.
Author: James E. Pustejovsky [aut, cre], Daniel M. Swan [ctb]
Maintainer: James E. Pustejovsky <jepusto@gmail.com>

Diff between ARPobservation versions 1.2.2 dated 2023-08-25 and 1.2.3 dated 2026-11-13

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New package tuiksdmx with initial version 0.1.1
Package: tuiksdmx
Title: Access the Turkish Statistical Institute SDMX REST API
Version: 0.1.1
Description: Provides an authenticated, metadata-driven R client for the Turkish Statistical Institute (TURKSTAT) SDMX REST API. Supports dataflow discovery, data structure definitions, concepts, codelists, metadata-based filter validation, SDMX query construction, statistical data retrieval, and observation-based availability exploration. Authentication supports an environment variable or an explicitly supplied API key argument.
URL: https://github.com/MFatihTuzen/tuiksdmx, https://MFatihTuzen.github.io/tuiksdmx/
BugReports: https://github.com/MFatihTuzen/tuiksdmx/issues
License: MIT + file LICENSE
Depends: R (>= 4.1.0)
Encoding: UTF-8
Imports: httr2, xml2, dplyr, purrr, tibble, readr, rlang, digest
Suggests: testthat (>= 3.1.7), withr, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-28 15:22:20 UTC; runner
Author: Muhammed Fatih Tuzen [aut, cre]
Maintainer: Muhammed Fatih Tuzen <mfatihtuzen@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-13 18:10:02 UTC

More information about tuiksdmx at CRAN
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Package qap updated to version 0.2-0 with previous version 0.1-3 dated 2026-10-01

Title: Heuristics for the Quadratic Assignment Problem (QAP)
Description: Implements search heuristics for the Quadratic Assignment Problem (QAP). Originally formulated as a facility location problem in operations research, the QAP also has applications in data analysis. The problem is NP-hard.
Author: Michael Hahsler [aut, cre, cph] , Franz Rendl [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

Diff between qap versions 0.1-3 dated 2026-10-01 and 0.2-0 dated 2026-11-13

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New package eiCR with initial version 0.0.1
Package: eiCR
Title: Ecological Inference for RxC Tables using the Cressie-Read Power Divergence
Version: 0.0.1
Description: Estimates R by C vote transfer matrices (ecological contingency tables) from aggregate data by minimizing the Cressie-Read power measure. Acknowledgements: The authors wish to thank Consellería de Educación, Cultura y Universidades, Generalitat Valenciana (grant CIAICO/2023/031) for supporting this research.
License: GPL (>= 2)
Encoding: UTF-8
Imports: stats
Suggests: ggplot2, numDeriv, Rsolnp, scales
NeedsCompilation: no
Packaged: 2026-10-28 19:03:01 UTC; pavia
Author: Jose M. Pavia [aut, cre] , Antonio Forcina [aut]
Maintainer: Jose M. Pavia <jose.m.pavia@uv.es>
Repository: CRAN
Date/Publication: 2026-11-13 18:30:02 UTC

More information about eiCR at CRAN
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Package bWGR updated to version 2.2.19 with previous version 2.2.18 dated 2026-07-25

Title: Bayesian Whole-Genome Regression
Description: Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).
Author: Alencar Xavier [aut, cre] , William Muir [aut], David Habier [aut], Kyle Kocak [aut], Shizhong Xu [aut], Katy Rainey [aut]
Maintainer: Alencar Xavier <alenxav@gmail.com>

Diff between bWGR versions 2.2.18 dated 2026-07-25 and 2.2.19 dated 2026-11-13

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New package annsearchR with initial version 0.1.0
Package: annsearchR
Title: Fast Approximate Nearest Neighbour Search
Version: 0.1.0
Description: R interface to the Rust crate 'ann-search-rs'. Build an exact or approximate nearest neighbour index once, keep it in memory behind an 'R6' object and query it as often as you like. Index types include hierarchical navigable small world graphs (HNSW), 'Annoy' random projection trees, inverted file indices (IVF), nearest neighbour descent (NNDescent) and 'Vamana' graphs. Multi-threaded via the Rust crate 'Rayon', with 32-bit or 64-bit floating point storage. Methods are described in Malkov and Yashunin (2020) <doi:10.1109/TPAMI.2018.2889473> and Dong et al. (2011) <doi:10.1145/1963405.1963487>.
License: MIT + file LICENSE
Copyright: Rust crate authors and licences are listed in inst/AUTHORS.
Encoding: UTF-8
Imports: checkmate, lifecycle, methods, R6, stats
SystemRequirements: Cargo (Rust's package manager), rustc >= 1.90, xz
Depends: R (>= 4.2)
Suggests: knitr, quarto, tinytest
VignetteBuilder: quarto
URL: https://gregorlueg.github.io/annsearchR/, https://github.com/GregorLueg/annsearchR
BugReports: https://github.com/GregorLueg/annsearchR/issues
NeedsCompilation: yes
Packaged: 2026-10-28 18:40:33 UTC; gregorlueg
Author: Gregor Lueg [aut, cre], Authors of the dependency Rust crates [ctb, cph]
Maintainer: Gregor Lueg <gregorlueg@me.com>
Repository: CRAN
Date/Publication: 2026-11-13 18:30:07 UTC

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Package spatialising updated to version 1.0.0 with previous version 0.6.2 dated 2025-09-30

Title: Ising Model for Spatial Data
Description: Performs simulations of binary spatial raster data using the Ising model (Ising (1925) <doi:10.1007/BF02980577>; Onsager (1944) <doi:10.1103/PhysRev.65.117>). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) <doi:10.1098/rsos.231005>).
Author: Jakub Nowosad [aut, cre]
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>

Diff between spatialising versions 0.6.2 dated 2025-09-30 and 1.0.0 dated 2026-11-13

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Package mfp2 updated to version 1.1.1 with previous version 1.1.0 dated 2026-11-02

Title: Multivariable Fractional Polynomial Models with Extensions
Description: Fits multivariable fractional polynomial models with selection of predictors and functional forms. The mfp2() function supports generalized linear models, including Gamma and inverse-Gaussian regression; negative-binomial, multinomial and ordinal regression; generalized estimating equations for clustered data; Cox and parametric survival models; and Fine-Gray competing-risks models. Extensions include approximate cumulative distribution transformations for sigmoid-shaped associations and spike-at-zero modelling for semi-continuous covariates. The mfpi() function assesses interactions between a categorical grouping variable and continuous, binary, or categorical predictors. Fractional polynomial modelling is described by Royston and Altman (1994) <doi:10.2307/2986270> and Royston and Sauerbrei (2008, ISBN:978-0-470-02842-1). The approximate cumulative distribution extension follows Royston (2014) <doi:10.1177/1536867X1401400206> and Royston and Sauerbrei (2016) <doi:10.11 [...truncated...]
Author: Edwin Kipruto [aut, cre], Michael Kammer [aut], Patrick Royston [aut], Willi Sauerbrei [aut]
Maintainer: Edwin Kipruto <kiprutoedwin2626@gmail.com>

Diff between mfp2 versions 1.1.0 dated 2026-11-02 and 1.1.1 dated 2026-11-13

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New package labelnoise with initial version 0.1.0
Package: labelnoise
Title: Find Label Errors in Classification Data with Confident Learning
Version: 0.1.0
Description: Finds examples whose given class label is likely wrong, using out-of-sample predicted probabilities from any classifier. Implements confident learning (Northcutt, Jiang and Chuang, 2021) <doi:10.1613/jair.1.12125>: per-class confidence thresholds, the confident joint, calibrated estimates of the joint distribution of given and true labels, noise transition matrices, and five rank-and-prune rules for flagging label issues. Also provides model-agnostic label quality scores (Kuan and Mueller, 2022) <doi:10.5281/zenodo.18168246> and a per-class label health summary. Inputs can be a label vector with a probability matrix, a data frame of predictions, resampling results from 'tune', predictions from 'mlr3', or a fitted 'caret' model.
License: MIT + file LICENSE
URL: https://ridsonap.is-a.dev/labelnoise/, https://github.com/ridsonap/labelnoise
BugReports: https://github.com/ridsonap/labelnoise/issues
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: cli, rlang (>= 1.1.0), stats, utils, tibble, tidyselect
Suggests: caret, knitr, lgr, mlr3, nnet, parsnip, rmarkdown, rsample, testthat (>= 3.0.0), tune, withr, workflows, yardstick
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-28 13:06:50 UTC; macbook
Author: Ridson Al Farizal P [aut, cre, cph]
Maintainer: Ridson Al Farizal P <ridsonalfarizal15@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-13 17:10:08 UTC

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New package healthsim with initial version 0.1.0
Package: healthsim
Title: Simulate Synthetic Health Insurance Portfolios and Panels
Version: 0.1.0
Description: Generates synthetic South African health insurance data with a known data-generating process, for teaching and testing pricing models. One function simulates a cross-sectional portfolio of principal members with negative binomial claim counts and a lognormal-Pareto severity mixture. Another simulates a multi-year open-cohort panel with ageing, smoking cessation, wearable activity and sleep data joined by a Clayton copula, claims inflation, annual benefit limits, lapses and deaths. Every parameter can be changed, and the true expected frequency and severity are available for checking fitted models.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: stats
Suggests: testthat (>= 3.0.0)
URL: https://github.com/Sbusiso-Mdingi/healthsim, https://www.kaggle.com/sbusisomdingiayesbu
BugReports: https://github.com/Sbusiso-Mdingi/healthsim/issues
NeedsCompilation: no
Packaged: 2026-10-28 13:13:50 UTC; sbusisomdingi
Author: Sbusiso Mdingi [aut, cre, cph]
Maintainer: Sbusiso Mdingi <sbusiso@mdingi.co.za>
Repository: CRAN
Date/Publication: 2026-11-13 18:00:02 UTC

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Package flows readmission to version 2.0.0 with previous version 1.1.1 dated 2016-06-06

Title: Flow Matrix Selection with Statistics and Visualisation
Description: The analysis and representation of flows often presuppose a selection to facilitate interpretation. Various methods have been proposed for selecting flows, one of the most widely used being based on major flows: it selects only the most important flows, absolute or relative, on a local or global scale. These methods often highlight hierarchies between locations, but the loss of information caused by selection is rarely taken into account. We propose statistical indicators to assess the loss of information and the characteristics of selected flows. We provide functions that select flows (main, dominant or major flows), provide statistics on selections and offer visualizations in the form of maps and graphs. See Beauguitte et al. (2015) <doi:10.4000/netcom.2134> for more details.
Author: Timothee Giraud [cre, aut] , Laurent Beauguitte [aut] , Marianne Guerois [aut]
Maintainer: Timothee Giraud <timothee.giraud@cnrs.fr>

This is a re-admission after prior archival of version 1.1.1 dated 2016-06-06

Diff between flows versions 1.1.1 dated 2016-06-06 and 2.0.0 dated 2026-11-13

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New package easypaper with initial version 0.1.0
Package: easypaper
Title: Automate Reproducible 'Quarto' Manuscripts Ready for Submission
Version: 0.1.0
Description: Creates and builds reproducible scientific manuscripts written in 'Quarto'. create_paper() writes a project that holds only the paper: its sections as separate files, its references and journal style as a CSL (Citation Style Language) file, the setup of the analysis and the data. The functions of the package then render it for any journal, check citations and cross-references before every render, export figures to PNG, JPEG and TIFF at 600 dpi, give the supplementary material a document and a bibliography of its own, and build the submission folder: the manuscript split for double-blind review, together with a data and code compendium ready for a data repository. Every render records the versions of the packages it used, 'easypaper' included, with 'renv', so the manuscript can be rebuilt years later. Every render also sets the scientific names of the reference list in italics, checking genera and species against the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy < [...truncated...]
URL: https://github.com/danielsangarci/easypaper, https://danielsangarci.github.io/easypaper/
BugReports: https://github.com/danielsangarci/easypaper/issues
License: MIT + file LICENSE
Encoding: UTF-8
SystemRequirements: Quarto command line tool (<https://quarto.org>)
Depends: R (>= 4.1)
Imports: dataspice, flextable, haven, here, jsonlite, knitr, magick, quarto, ragg, readxl, renv, rmarkdown, rstudioapi, tools, utils, yaml, zip
Suggests: curl, testthat (>= 3.0.0), trackdown, vegan
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-28 13:35:05 UTC; daniel
Author: Daniel Sanchez-Garcia [aut, cre, cph]
Maintainer: Daniel Sanchez-Garcia <danielsangarci@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-13 17:50:02 UTC

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New package duckdf with initial version 0.2.1
Package: duckdf
Title: SQL for Dataframes, but with Ducks
Version: 0.2.1
Description: An SQL interface for R dataframes built on the DuckDB embedded analytical database. Dataframes are registered directly with an in-process DuckDB instance and queried with standard SQL, so analyses can join, filter, aggregate and reshape R objects with SQL while results are returned as dataframes, without copying data to an external database server. The package wraps the 'duckdb' R package with a DBI-compliant connection and provides a small, checked surface for the common register-query-unnregister workflow. The underlying engine and its vectorised execution model are described in Raasveldt and Mühleisen (2019) <doi:10.1145/3299869.3320212>.
License: AGPL-3
Encoding: UTF-8
Depends: R (>= 4.2.0)
Imports: DBI (>= 1.1.3), checkmate, duckdb (>= 1.5.6), jsonlite
Suggests: bench, data.table, dbplyr, dplyr, duckplyr, knitr, rmarkdown, sqldf, testthat (>= 3.0.0), tibble, withr
URL: https://github.com/phillc73/duckdf
BugReports: https://github.com/phillc73/duckdf/issues
NeedsCompilation: no
Packaged: 2026-10-28 13:43:57 UTC; phillc
Author: Phill Clarke [aut, cre]
Maintainer: Phill Clarke <phill@mimosadev.org>
Repository: CRAN
Date/Publication: 2026-11-13 17:10:14 UTC

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New package dicomtk with initial version 0.1.0
Package: dicomtk
Title: Comprehensive 'DICOM' Toolkit for Medical Imaging
Version: 0.1.0
Description: A comprehensive R interface to the 'DICOM' Toolkit ('DCMTK'), providing native R bindings for reading, writing, modifying, and visualizing Digital Imaging and Communications in Medicine ('DICOM') files. Features include 'tidyverse'-compliant data structures, 'ggplot2'-based image visualization, interactive 'Shiny' viewers, 'PACS' networking (C-ECHO, C-FIND, C-STORE, C-MOVE), structured report handling, 'DICOM' deidentification, and integration with local and cloud-based large language models ('LLM') via 'Ollama' and 'OpenAI'-compatible APIs for AI-assisted medical image analysis. The 'DCMTK' C++ library is bundled and compiled from source, requiring no external installation.
License: GPL (>= 2)
URL: https://github.com/lauyeehow1986-hub/dicomtk, https://lauyeehow1986-hub.github.io/dicomtk/
BugReports: https://github.com/lauyeehow1986-hub/dicomtk/issues
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.12), tibble (>= 3.0.0), dplyr (>= 1.1.0), tidyr (>= 1.3.0), rlang (>= 1.1.0), ggplot2 (>= 3.4.0), cli (>= 3.6.0), httr2 (>= 1.0.0), jsonlite (>= 1.8.0), grDevices, graphics, stats, utils, tools
Suggests: shiny (>= 1.8.0), rgl (>= 1.0.0), RNifti (>= 1.5.0), testthat (>= 3.0.0), knitr, rmarkdown, pkgdown, withr, magick
LinkingTo: Rcpp
SystemRequirements: C++17, CMake (>= 3.10)
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-10-28 15:13:16 UTC; lauye
Author: Yee How Lau [aut, cre], John Muschelli [ctb] , OFFIS e.V. [cph]
Maintainer: Yee How Lau <lauyeehow1986@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-13 18:00:14 UTC

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Package descriptr updated to version 0.6.1 with previous version 0.6.0 dated 2024-11-08

Title: Generate Descriptive Statistics
Description: Generate descriptive statistics such as measures of location, dispersion, frequency tables, cross tables, group summaries and multiple one/two way tables.
Author: Aravind Hebbali [aut, cre]
Maintainer: Aravind Hebbali <aravindhebbali@rsquaredacademy.com>

Diff between descriptr versions 0.6.0 dated 2024-11-08 and 0.6.1 dated 2026-11-13

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Package censored updated to version 0.4.0 with previous version 0.3.5 dated 2026-09-08

Title: 'parsnip' Engines for Survival Models
Description: Engines for survival models from the 'parsnip' package. These include parametric models (e.g., Jackson (2016) <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g., Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).
Author: Emil Hvitfeldt [aut] , Hannah Frick [aut, cre] , Posit Software, PBC [cph, fnd]
Maintainer: Hannah Frick <hannah@posit.co>

Diff between censored versions 0.3.5 dated 2026-09-08 and 0.4.0 dated 2026-11-13

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New package BasinmodlR with initial version 0.1.0
Package: BasinmodlR
Title: Burial and Thermal History Modelling of Sedimentary Basins
Version: 0.1.0
Maintainer: Michiel Arts <michiel.arts@stratigraphy.eu>
Depends: R (>= 3.5.0)
Imports: graphics, grDevices, stats, utils
Suggests: testthat (>= 3.1.9)
Description: Simulates the burial, compaction and thermal history of a sedimentary basin in one dimension, for geologists and stratigraphers. A basin is described by one row per stratigraphic unit and one row per erosion event, from which deposition phases, hiatuses, layer stacks, maximum-burial windows and grain (solid) thicknesses are derived. Compaction follows an exponential porosity-depth law and is irreversible, so uplifted layers keep the porosity acquired at their deepest burial. Temperatures follow the steady-state conductive geotherm of a layered column with radiogenic heat production. The methods follow the textbook treatment of Allen and Allen (2013, ISBN:978-0-470-67377-5). Functions are included to draw a composite figure of burial, temperature and hydrocarbon generation windows and a petroleum system event chart.
License: GPL-3
URL: https://github.com/stratigraphy/BasinmodlR
BugReports: https://github.com/stratigraphy/BasinmodlR/issues
Encoding: UTF-8
Language: en-GB
LazyData: true
NeedsCompilation: no
Packaged: 2026-10-28 13:34:18 UTC; Administrator
Author: Michiel Arts [aut, cre, cph]
Repository: CRAN
Date/Publication: 2026-11-13 17:10:33 UTC

More information about BasinmodlR at CRAN
Permanent link

Package varPro updated to version 3.4.0 with previous version 3.3.0 dated 2026-09-22

Title: Model-Independent Variable Selection via the Rule-Based Variable Priority
Description: A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown.
Author: Min Lu [aut], Aster K. Shear [aut], Udaya B. Kogalur [aut, cre], Hemant Ishwaran [aut]
Maintainer: Udaya B. Kogalur <ubk@kogalur.com>

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New package weasel with initial version 0.4.4
Package: weasel
Title: Wave-Based Extraction and Selection for Longitudinal Data
Version: 0.4.4
Description: Turns wave-and-respondent selection in longitudinal panel and repeated-measures data into an explicit, reproducible workflow. Provides a scope-based pipeline for wave pattern analysis and a planning interface for scenario-based respondent selection with configurable missing-wave tolerances, interior-gap constraints, and endpoint requirements. Wave grids may be consecutive integers or the observed schedule (e.g. biennial panels). Includes tolerance-sensitivity and attrition-selectivity diagnostics, plus a generator of ready-to-paste methods-section justification text. A full workflow guide is available at <https://siardv.github.io/weasel/>.
License: MIT + file LICENSE
URL: https://github.com/siardv/weasel, https://siardv.github.io/weasel/
BugReports: https://github.com/siardv/weasel/issues
Encoding: UTF-8
Depends: R (>= 3.6)
Imports: stats, tools, utils
Suggests: cli, knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-17 18:17:51 UTC; siardv
Author: Siard van den Bosch [aut, cre, cph]
Maintainer: Siard van den Bosch <siardvandenbosch@me.com>
Repository: CRAN
Date/Publication: 2026-11-13 16:10:02 UTC

More information about weasel at CRAN
Permanent link

Package tvgarch updated to version 2.5.0 with previous version 2.4.3 dated 2025-09-01

Title: Time Varying GARCH Modelling
Description: Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) <doi:10.18637/jss.v108.i09>. In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) <doi:10.1016/j.jeconom.2013.03.006> introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates.
Author: Susana Campos-Martins [aut, cre], Genaro Sucarrat [ctb]
Maintainer: Susana Campos-Martins <scmartins@ucp.pt>

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Package tropicalSparse readmission to version 0.1.1 with previous version 0.1.0 dated 2018-07-01

Title: Sparse Tropical Algebra
Description: Some of the basic tropical algebra functionality is provided for sparse matrices by applying sparse matrix storage techniques. Some of these are addition and multiplication of vectors and matrices, dot product of the vectors in tropical form and some general equations are also solved using tropical algebra.
Author: Hamza Farooq [aut], Hafiz Zain Ul Haq [aut, cre], Muhammad Kashif Hanif [aut], Karl-Heinz Zimmermann [aut]
Maintainer: Hafiz Zain Ul Haq <zainalibajwa4u@yahoo.com>

This is a re-admission after prior archival of version 0.1.0 dated 2018-07-01

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Package summarySCI updated to version 1.0.0 with previous version 0.1.2 dated 2026-09-14

Title: Produces Publication-Ready Summary Tables and Plots
Description: Produces tables with descriptive statistics for continuous, categorical and dichotomous variables. It is largely based on the package 'gtsummary'; Sjoberg DD et al. (2021) <doi:10.32614/RJ-2021-053>. The tables can be accompanied by matching boxplots, line plots and stacked barplots, also over time.
Author: Saemi Schaer [aut], Charlotte Micheloud [cre, aut], Tudor Jumuga [aut]
Maintainer: Charlotte Micheloud <Charlotte.Micheloud@swisscancerinstitute.ch>

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Package Rlabkey updated to version 3.5.1 with previous version 3.5.0 dated 2026-07-01

Title: Data Exchange Between R and 'LabKey' Server
Description: The 'LabKey' client library for R makes it easy for R users to load live data from a 'LabKey' Server, <https://www.labkey.com/>, into the R environment for analysis, provided users have permissions to read the data. It also enables R users to insert, update, and delete records stored on a 'LabKey' Server, provided they have appropriate permissions to do so.
Author: Peter Hussey [aut], Cory Nathe [cre]
Maintainer: Cory Nathe <cnathe@labkey.com>

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 man/labkey.setCurlOptions.Rd |   16 +++++++++++-----
 8 files changed, 48 insertions(+), 41 deletions(-)

More information about Rlabkey at CRAN
Permanent link

Package openfhe.R updated to version 1.6.1 with previous version 1.5.1.2 dated 2026-09-25

Title: R Interface to the 'OpenFHE' Fully Homomorphic Encryption Library
Description: Provides an R interface to 'OpenFHE', the open-source C++ library for fully homomorphic encryption (Al Badawi and others, 2022) <https://eprint.iacr.org/2022/915>, which allows computation directly on encrypted data without access to the secret key. Supports the Brakerski-Fan-Vercauteren (BFV, 2012) <https://eprint.iacr.org/2012/144>, Brakerski-Gentry-Vaikuntanathan (BGV, 2014) <doi:10.1145/2633600>, and Cheon-Kim-Kim-Song (CKKS, 2017) <https://eprint.iacr.org/2016/421> schemes for arithmetic on encrypted numbers, together with the Ducas-Micciancio (FHEW, 2015) <https://eprint.iacr.org/2014/816> and Chillotti-Gama-Georgieva-Izabachene (TFHE, 2020) <https://eprint.iacr.org/2018/421> schemes for evaluating arbitrary functions on encrypted bits.
Author: Balasubramanian Narasimhan [aut, cre, cph] , New Jersey Institute of Technology [cph] , Duality Technologies, Inc. [ctb, cph] , Samuel Neves [ctb, cph] , Jean-Philippe Aumasson [ctb, cph] , Randolph Voorhies [ctb, cph] , Shane Grant [ctb, cph] , Juan [...truncated...]
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>

Diff between openfhe.R versions 1.5.1.2 dated 2026-09-25 and 1.6.1 dated 2026-11-13

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 openfhe.R-1.6.1/openfhe.R/MD5                                                                                 | 1063 +-
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 openfhe.R-1.6.1/openfhe.R/R/ciphertext-accessors.R                                                            |    7 
 openfhe.R-1.6.1/openfhe.R/R/context.R                                                                         |   42 
 openfhe.R-1.6.1/openfhe.R/R/cpp11.R                                                                           |  788 +
 openfhe.R-1.6.1/openfhe.R/R/cryptocontext-getters.R                                                           |   15 
 openfhe.R-1.6.1/openfhe.R/R/enums.R                                                                           |  193 
 openfhe.R-1.6.1/openfhe.R/R/eval-key-map.R                                                                    |  100 
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 openfhe.R-1.6.1/openfhe.R/R/methods-encrypt.R                                                                 |   25 
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 openfhe.R-1.6.1/openfhe.R/R/params.R                                                                          |   19 
 openfhe.R-1.6.1/openfhe.R/R/plaintext-accessors.R                                                             |   12 
 openfhe.R-1.6.1/openfhe.R/R/plaintext.R                                                                       |   19 
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 openfhe.R-1.6.1/openfhe.R/R/secret-sharing.R                                                                  |   27 
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 openfhe.R-1.6.1/openfhe.R/R/series-powers.R                                                                   |only
 openfhe.R-1.6.1/openfhe.R/R/utils.R                                                                           |   37 
 openfhe.R-1.6.1/openfhe.R/README.md                                                                           |   13 
 openfhe.R-1.6.1/openfhe.R/build/partial.rdb                                                                   |binary
 openfhe.R-1.6.1/openfhe.R/build/vignette.rds                                                                  |binary
 openfhe.R-1.6.1/openfhe.R/configure                                                                           |   21 
 openfhe.R-1.6.1/openfhe.R/configure.win                                                                       |   15 
 openfhe.R-1.6.1/openfhe.R/inst/CITATION                                                                       |   18 
 openfhe.R-1.6.1/openfhe.R/inst/COPYRIGHTS                                                                     |   17 
 openfhe.R-1.6.1/openfhe.R/inst/build_openfhe.sh                                                               |   24 
 openfhe.R-1.6.1/openfhe.R/inst/doc/binfhe-boolean-circuits.R                                                  |   33 
 openfhe.R-1.6.1/openfhe.R/inst/doc/binfhe-boolean-circuits.Rmd                                                |   30 
 openfhe.R-1.6.1/openfhe.R/inst/doc/binfhe-boolean-circuits.html                                               |   48 
 openfhe.R-1.6.1/openfhe.R/inst/doc/ckks-bootstrapping.R                                                       |    9 
 openfhe.R-1.6.1/openfhe.R/inst/doc/ckks-bootstrapping.Rmd                                                     |   17 
 openfhe.R-1.6.1/openfhe.R/inst/doc/ckks-bootstrapping.html                                                    |   24 
 openfhe.R-1.6.1/openfhe.R/inst/doc/introduction.html                                                          |    8 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/CMakeLists.txt                                                         |  248 
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 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/CMakeLists.txt                                              |   17 
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 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/binfhecontext-ser.h                                 |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/binfhecontext.h                                     |  622 -
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/lwe-ciphertext-fwd.h                                |    8 
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 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/lwe-cryptoparameters.h                              |   97 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/lwe-keypair-fwd.h                                   |    8 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/lwe-keypair.h                                       |   40 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/lwe-keyswitchkey-fwd.h                              |    8 
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 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/lwe-privatekey-fwd.h                                |    8 
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 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/rgsw-cryptoparameters.h                             |  356 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/rgsw-evalkey.h                                      |   77 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/include/rlwe-ciphertext.h                                   |   61 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/binfhe-base-scheme.cpp                                  |  235 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/binfhe-constants-impl.cpp                               |  234 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/binfhecontext.cpp                                       |  274 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/lwe-pke.cpp                                             |  379 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/rgsw-acc-cggi.cpp                                       |  302 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/rgsw-acc-dm.cpp                                         |  140 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/rgsw-acc-lmkcdey.cpp                                    |  272 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/rgsw-acc.cpp                                            |   86 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/rgsw-acckey32.cpp                                       |only
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/binfhe/lib/rgsw-cryptoparameters.cpp                               |  214 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/CMakeLists.txt                                                |   23 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/README.md                                             |    2 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/lattice/constants-lattice.h                           |   28 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/lattice/dgsampling-impl.h                             |   49 
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 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/math/hal/bigintntl/ubintntl.h                         |  976 +-
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/math/hal/integer.h                                    |  623 +
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/math/hal/intnat/mubintvecnat.h                        |  886 +-
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/math/hal/intnat/transformnat-impl.h                   | 1277 +--
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/math/hal/intnat/transformnat.h                        |  942 +-
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/math/hal/intnat/ubintnat.h                            | 2050 ++---
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 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/hashutil.h                                      |   34 
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 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/memory.h                                        |   34 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/openfhebase64.h                                 |   25 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/parallel.h                                      |   85 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/prng/blake2engine.h                             |   57 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/prng/prng.h                                     |   30 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/serial.h                                        |   26 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/serializable.h                                  |   21 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/sertype.h                                       |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/type_name.h                                     |   12 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/utilities-int.h                                 |   20 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/utils/utilities.h                                     |   79 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/include/version.h                                             |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/lattice/constants-lattice-impl.cpp                        |    7 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/lattice/lattice.cpp                                       |   17 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/lattice/stdlatticeparms.cpp                               |  293 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/lattice/trapdoor-dcrtpoly.cpp                             |   28 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/lattice/trapdoor-poly.cpp                                 |   60 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/chebyshev.cpp                                        |   17 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/dftransform.cpp                                      |  164 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/discretegaussiangeneratorgeneric.cpp                 |   67 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/distributiongenerator.cpp                            |   19 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintdyn/be4-math-impl.cpp                      |   23 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintdyn/mubintvecdyn.cpp                       |   52 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintdyn/ubintdyn.cpp                           |  217 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintfxd/be2-math-impl.cpp                      |   23 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintfxd/mubintvecfxd.cpp                       |  177 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintfxd/ubintfxd.cpp                           |  669 -
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintntl/be6-math-impl.cpp                      |   23 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintntl/mubintvecntl.cpp                       |  104 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/bigintntl/ubintntl.cpp                           |   74 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/intnat/benative-math-impl.cpp                    |   39 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hal/intnat/mubintvecnat.cpp                          |  556 +
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/hermite.cpp                                          |    7 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/matrix.cpp                                           |   35 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/math/nbtheory2.cpp                                        |   96 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/debug.cpp                                           |   12 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/demangle.cpp                                        |    7 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/diagnostic_output.cpp                               |only
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/get-call-stack.cpp                                  |   16 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/hashutil.cpp                                        |  100 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/inttypes.cpp                                        |    1 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/memory.cpp                                          |  138 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/prng/blake2b-ref.c                                  |    1 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/prng/blake2engine.cpp                               |   28 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/prng/blake2xb-ref.c                                 |    1 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/core/lib/utils/utilities.cpp                                       |    4 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/CMakeLists.txt                                                 |   19 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/README.md                                                      |    1 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/README.md                                              |    4 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/ciphertext-fwd.h                                       |   70 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/ciphertext-ser.h                                       |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/ciphertext.h                                           |  407 -
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/constants-defs.h                                       |   24 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/constants.h                                            |  215 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/cryptocontext-fwd.h                                    |    9 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/cryptocontext-ser.h                                    |   87 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/cryptocontext.h                                        | 4052 +++++-----
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/cryptocontextfactory.h                                 |   80 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/cryptoobject.h                                         |   69 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/README.md                                     |    4 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/ckkspackedencoding.h                          |  277 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/coefpackedencoding.h                          |  103 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/encodingparams.h                              |  221 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/encodings.h                                   |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/packedencoding.h                              |  177 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/plaintext-fwd.h                               |   11 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/plaintext.h                                   |  406 -
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/plaintextfactory.h                            |   74 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/encoding/stringencoding.h                              |   89 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/gen-cryptocontext.h                                    |   96 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/globals.h                                              |   29 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/evalkey-fwd.h                                      |   10 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/evalkey.h                                          |  121 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/evalkeyrelin-fwd.h                                 |   11 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/evalkeyrelin.h                                     |  150 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/key-ser.h                                          |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/key.h                                              |   35 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/keypair.h                                          |   36 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/privatekey-fwd.h                                   |   10 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/privatekey.h                                       |  100 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/publickey-fwd.h                                    |   10 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/key/publickey.h                                        |   96 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/keyswitch/README.md                                    |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/keyswitch/keyswitch-base.h                             |  186 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/keyswitch/keyswitch-bv.h                               |  139 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/keyswitch/keyswitch-hybrid.h                           |  197 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/keyswitch/keyswitch-rns.h                              |   36 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/metadata-ser.h                                         |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/metadata.h                                             |  108 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/openfhe.h                                              |   48 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-advancedshe.h                     |   15 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-cryptoparameters.h                |  103 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-fhe.h                             |   15 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-leveledshe.h                      |  243 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-multiparty.h                      |   55 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-parametergeneration.h             |   36 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-pke.h                             |   98 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-pre.h                             |   15 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-scheme.h                          |   40 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/bfvrns-ser.h                             |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/gen-cryptocontext-bfvrns-internal.h      |   31 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/gen-cryptocontext-bfvrns-params.h        |   91 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bfvrns/gen-cryptocontext-bfvrns.h               |   46 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-advancedshe.h                     |   15 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-cryptoparameters.h                |   99 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-fhe.h                             |   15 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-leveledshe.h                      |   77 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-multiparty.h                      |   33 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-parametergeneration.h             |  161 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-pke.h                             |   58 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-pre.h                             |   15 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-scheme.h                          |   32 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/bgvrns-ser.h                             |   10 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/gen-cryptocontext-bgvrns-internal.h      |   30 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/gen-cryptocontext-bgvrns-params.h        |   76 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/bgvrns/gen-cryptocontext-bgvrns.h               |   46 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-advancedshe.h                   |  280 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-cryptoparameters.h              |  423 +
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-fhe.h                           | 1415 ++-
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-leveledshe.h                    |  214 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-multiparty.h                    |   32 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-parametergeneration.h           |   64 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-pke.h                           |   52 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-pre.h                           |   15 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-scheme.h                        |   37 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-schemeswitching.h               |  113 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-ser.h                           |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/ckksrns-utils.h                         |  174 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/gen-cryptocontext-ckksrns-internal.h    |   47 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/gen-cryptocontext-ckksrns-params.h      |   68 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/ckksrns/gen-cryptocontext-ckksrns.h             |   27 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/gen-cryptocontext-params-defaults.h             |  207 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/gen-cryptocontext-params-validation.h           |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/gen-cryptocontext-params.h                      |   63 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/scheme-id.h                                     |   54 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/scheme-swch-params.h                            |  214 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/scheme/scheme-utils.h                                  |    8 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-advancedshe.h                          |  781 +
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-cryptoparameters.h                     |  146 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-fhe.h                                  |  772 +
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-leveledshe.h                           | 1171 +-
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-multiparty.h                           |  491 -
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-parametergeneration.h                  |  110 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-pke.h                                  |  131 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-pre.h                                  |   60 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-scheme.h                               |  619 +
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/base-ser.h                                  |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/decrypt-result.h                            |   48 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemebase/rlwe-cryptoparameters.h                     |  445 -
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemelet/rlwe-mp.h                                    |  111 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-advancedshe.h                            |   14 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-cryptoparameters.h                       | 1335 +--
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-fhe.h                                    |   14 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-leveledshe.h                             |  437 -
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-modulus-limits.h                         |   14 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-multiparty.h                             |   76 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-parametergeneration.h                    |   17 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-pke.h                                    |  113 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-pre.h                                    |   22 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-scheme.h                                 |   36 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/include/schemerns/rns-ser.h                                    |    6 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/lib/ciphertext-impl.cpp                                        |    4 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/lib/constants-impl.cpp                                         |    7 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/lib/cryptocontext.cpp                                          |  205 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/lib/cryptocontextfactory.cpp                                   |   10 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/lib/cryptoobject-impl.cpp                                      |    3 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/lib/encoding/ckkspackedencoding.cpp                            |  159 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/lib/encoding/coefpackedencoding.cpp                            |   26 
 openfhe.R-1.6.1/openfhe.R/inst/openfhe/src/pke/lib/encoding/packedencoding.cpp                                |  150 
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 openfhe.R-1.6.1/openfhe.R/inst/tinytest/test_bfv_arithmetic.R                                                 |    2 
 openfhe.R-1.6.1/openfhe.R/inst/tinytest/test_bgv_full.R                                                       |    2 
 openfhe.R-1.6.1/openfhe.R/inst/tinytest/test_binfhe.R                                                         |    7 
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 openfhe.R-1.6.1/openfhe.R/inst/tinytest/test_ckks.R                                                           |    2 
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 openfhe.R-1.6.1/openfhe.R/man/level_reduce_in_place.Rd                                                        |    4 
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 openfhe.R-1.6.1/openfhe.R/src/pke_bindings.cpp                                                                |   55 
 openfhe.R-1.6.1/openfhe.R/src/pke_ccparams_getters.cpp                                                        |   19 
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 openfhe.R-1.6.1/openfhe.R/src/pke_ciphertext_accessors.cpp                                                    |   16 
 openfhe.R-1.6.1/openfhe.R/src/pke_ckks_complex.cpp                                                            |   11 
 openfhe.R-1.6.1/openfhe.R/src/pke_ckks_eval_poly.cpp                                                          |  222 
 openfhe.R-1.6.1/openfhe.R/src/pke_cryptocontext_getters.cpp                                                   |    7 
 openfhe.R-1.6.1/openfhe.R/src/pke_eval.cpp                                                                    |   56 
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 openfhe.R-1.6.1/openfhe.R/src/pke_multiparty.cpp                                                              |   24 
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 openfhe.R-1.6.1/openfhe.R/src/pke_plaintext_accessors.cpp                                                     |   16 
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 openfhe.R-1.6.1/openfhe.R/src/pke_secret_sharing.cpp                                                          |    7 
 openfhe.R-1.6.1/openfhe.R/src/pke_serialization.cpp                                                           |    4 
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 openfhe.R-1.6.1/openfhe.R/src/pke_sum_key_serial.cpp                                                          |    2 
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 openfhe.R-1.6.1/openfhe.R/vignettes/binfhe-boolean-circuits.Rmd                                               |   30 
 openfhe.R-1.6.1/openfhe.R/vignettes/ckks-bootstrapping.Rmd                                                    |   17 
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Package nowcaster updated to version 1.0.1 with previous version 1.0.0 dated 2026-09-17

Title: Statistical Models for Notification Delay Correction of Epidemiological Data
Description: Statistical models for correcting notification delays of epidemiological data. The package wraps statistical models to estimate not yet reported data using empirical delay distribution either from individual-level data or from the differences of aggregated time series of cases. For more details see Bastos et al. (2019) <doi:10.1002/sim.8303>.
Author: Rafael Lopes [aut] , Tatiana Portella [aut] , Theo Economou [aut] , Marcelo Gomes [aut] , Leo Bastos [aut, cre]
Maintainer: Leo Bastos <leonardo.bastos@fiocruz.br>

Diff between nowcaster versions 1.0.0 dated 2026-09-17 and 1.0.1 dated 2026-11-13

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Package nnfor updated to version 0.10.0 with previous version 0.9.9 dated 2023-11-14

Title: Time Series Forecasting with Neural Networks
Description: Automatic time series modelling with neural networks. Allows fully automatic, semi-manual or fully manual specification of networks. For details of the specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>; and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.
Author: Nikolaos Kourentzes [aut, cre]
Maintainer: Nikolaos Kourentzes <nikolaos@kourentzes.com>

Diff between nnfor versions 0.9.9 dated 2023-11-14 and 0.10.0 dated 2026-11-13

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New package imbalanceKit with initial version 0.1.0
Package: imbalanceKit
Title: Resampling Methods and Tools for Imbalanced Classification
Version: 0.1.0
Description: A collection of resampling methods for class imbalance in classification problems. Includes the oversampling methods SMOTE with boosting (SMOTEWB) by F. Sağlam and M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>, Markov chain SMOTE (MCSMOTE) by F. Sağlam and M. A. Cengiz (2025) <doi:10.1016/j.asoc.2025.113906>, uncertainty guided oversampling (UGO) by F. Sağlam and M. A. Cengiz (2026) <doi:10.1016/j.knosys.2026.115886>, and bi-directional SMOTE (BiSMOTE) by F. Sağlam and U. Yamak (2027) <doi:10.1016/j.eswa.2026.134648>, and the data relocation approach DatRel by F. Sağlam (2025) <doi:10.1007/s10994-025-06755-8>, together with commonly used alternatives such as SMOTE, ADASYN, Borderline-SMOTE, Safe-Level-SMOTE, geometric SMOTE, random oversampling and undersampling. The methods share the same interface: they take a feature matrix and a factor class variable and return the resampled data together with the synthetic samples.
Depends: R (>= 4.0.0)
Imports: stats, utils, FNN, rpart, Rfast
Suggests: randomForest, nnet, testthat (>= 3.0.0)
License: MIT + file LICENSE
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-10-28 11:49:12 UTC; fatih
Author: Fatih Saglam [aut, cre]
Maintainer: Fatih Saglam <saglamf89@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-13 16:30:02 UTC

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Package glmmrBase updated to version 1.4.3 with previous version 1.4.1 dated 2026-05-28

Title: Monte Carlo Maximum Likelihood and Analysis of Generalised Linear Mixed Models
Description: Specification, analysis, simulation, and fitting of generalised linear mixed models. Monte Carlo Maximum likelihood model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions including Gaussian Process approximations. Methods described in Watson, Wang, and Giorgi (2026) <doi:10.48550/arXiv.2601.16022>.
Author: Sam Watson [aut, cre]
Maintainer: Sam Watson <S.I.Watson@bham.ac.uk>

Diff between glmmrBase versions 1.4.1 dated 2026-05-28 and 1.4.3 dated 2026-11-13

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Package GIFT updated to version 1.3.5 with previous version 1.3.4 dated 2026-08-24

Title: Access to the Global Inventory of Floras and Traits (GIFT)
Description: Retrieving regional plant checklists, species traits and distributions, and environmental data from the Global Inventory of Floras and Traits (GIFT). More information about the GIFT database can be found at <https://gift.uni-goettingen.de/about> and the map of available floras can be visualized at <https://gift.uni-goettingen.de/map>. The API and associated queries can be accessed according the following scheme: <https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.
Author: Pierre Denelle [aut, cre] , Patrick Weigelt [aut]
Maintainer: Pierre Denelle <pierre.denelle@posteo.com>

Diff between GIFT versions 1.3.4 dated 2026-08-24 and 1.3.5 dated 2026-11-13

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New package docketscape with initial version 0.1.0
Package: docketscape
Title: Legal Network and Congestion-Aware Delay Modeling for Court Dockets
Version: 0.1.0
Description: Splits court delay into two parts: (1) what a case is, represented through the statutory Acts and Sections invoked by the case, and (2) where it sits, represented by time-varying court docket load. Builds a weighted similarity graph of cases from legal provisions using 'igraph' and 'Matrix', estimates censoring-aware expected resolution timelines using legally similar neighbours, calculates relative lateness, and models delay in relation to filing-time court docket load using 'survival'. Designed for case-level judicial records such as the Development Data Lab (DDL) extract of Indian lower courts (<https://www.devdatalab.org/judicial-data>). Observational and non-causal; does not rank judges, evaluate judicial performance, or claim causal delay effects.
License: MIT + file LICENSE
URL: https://github.com/shraddhaajit/docketscape, https://www.devdatalab.org/judicial-data
BugReports: https://github.com/shraddhaajit/docketscape/issues
Encoding: UTF-8
Language: en-GB
Depends: R (>= 4.1.0)
Imports: igraph, Matrix, stats, survival
Suggests: knitr, rmarkdown, spelling, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-28 12:20:36 UTC; shrad
Author: Shraddha Ajit [aut, cre], DK Janani [aut]
Maintainer: Shraddha Ajit <shra19076@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-13 16:40:15 UTC

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New package cvpa with initial version 0.3.0
Package: cvpa
Title: Canadian Voting and Policy Attitudes
Version: 0.3.0
Description: The main focus of the package is to give weighted party support and issue attitudes by demographic groups over time. As of 0.2.0, policy-issue and vote data are read on demand from the cp3 project's hosted Parquet files (see update_cvpa_data()) rather than bundled with the package, covering several hundred policy questions across seven issue domains in addition to vote choice/intention. The vote data are described in Lucas, Romualdi, Armstrong, and Goodyear-Grant (2025) <doi:10.1017/S0008423925000241>.
License: MIT + file LICENSE
URL: https://policyattitudes.ca/app/, https://github.com/lucasjacklucas/cvpa_dataset
BugReports: https://github.com/lucasjacklucas/cvpa_dataset/issues
Depends: R (>= 3.5.0)
Imports: broom, dplyr, nanoparquet, rio, rlang, stats, tidyr
Suggests: ggplot2, knitr, rmarkdown, testthat (>= 3.1.5), withr
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-10-28 12:09:07 UTC; david
Author: Dave Armstrong [aut, cre] , Jack Lucas [aut] , Tyler Romualdi [aut]
Maintainer: Dave Armstrong <davearmstrong.ps@gmail.com>
Repository: CRAN
Date/Publication: 2026-11-13 16:40:22 UTC

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Package colvR updated to version 0.2.2 with previous version 0.2.1 dated 2026-11-12

Title: Poisson Lognormal with Dimension Reduction for Count Matrices with Missing Data
Description: Impute missing data in count tables using a Poisson lognormal model with dimension reduction, with and without zero inflation, as described in Bricout et al. (2026) <doi:10.48550/arXiv.2602.10673>. Parameters are estimated with a variational expectation-maximization algorithm. The package also provides the asymptotic variance of the estimators, prediction intervals for the imputed counts, and model selection criteria (Bayesian information criterion, BIC, and integrated completed likelihood, ICL) to choose the dimension of the latent space.
Author: Barbara Bricout [aut, cre]
Maintainer: Barbara Bricout <barbara.bricout@gmail.com>

Diff between colvR versions 0.2.1 dated 2026-11-12 and 0.2.2 dated 2026-11-13

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New package bsikompendium with initial version 0.2.1
Package: bsikompendium
Title: Offline Checks Supporting BSI 'IT-Grundschutz' Assessments
Version: 0.2.1
Description: Supports structured information security assessments based on the 'IT-Grundschutz' Compendium of the German Federal Office for Information Security (BSI) (2023, ISBN:978-3-8462-0906-6), including errata dated 2026-03-26, and BSI Standards 200-1, 200-2 and 200-3 (2017). Provides an indexed requirement catalogue, individually derived assessment contracts, protection needs, risk assessment and selected technical checks. Runs locally and requires qualified user assessments; it does not certify compliance. The Compendium is catalogued at <https://epflicht.ulb.uni-bonn.de/content/titleinfo/750346>.
License: Apache License (== 2.0)
Encoding: UTF-8
Language: de
Depends: R (>= 4.1.0)
Imports: jsonlite, stats
NeedsCompilation: no
Packaged: 2026-10-28 10:27:12 UTC; qwert123
Author: Dimitrios Geromichalos [aut, cre], RiskDataScience GmbH [cph]
Maintainer: Dimitrios Geromichalos <riskdatascience@web.de>
Repository: CRAN
Date/Publication: 2026-11-13 16:30:09 UTC

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New package appkitr with initial version 2.0.0.5
Package: appkitr
Title: Utilities to Work with 'GESIS AppKit' Data
Version: 2.0.0.5
Description: Provides helper functions for importing and processing survey data exported from the 'GESIS AppKit' platform <https://www.gesis.org/en/gesis-appkit>. Includes support for extracting variable and value labels from 'SPSS' syntax files, and automatically attaching these labels to raw CSV survey data for clean analysis.
License: Apache License (>= 2)
URL: https://gitlab.com/gesis-appkit/appkitr, https://gesis-appkit.gitlab.io/appkitr
BugReports: https://gitlab.com/gesis-appkit/appkitr/-/work_items
Depends: R (>= 4.1.0)
Imports: cli, stats, utils, withr
Suggests: testthat (>= 3.0.0)
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2026-10-28 12:14:38 UTC; maxhaag
Author: Maximilian Haag [aut, cre] , Charlotte de Alwis [aut] , Julian Kohne [aut] , GESIS - Leibniz Institute for the Social Sciences [cph]
Maintainer: Maximilian Haag <mail@mxhg.org>
Repository: CRAN
Date/Publication: 2026-11-13 16:40:27 UTC

More information about appkitr at CRAN
Permanent link

Package AIscreenR updated to version 0.5.1 with previous version 0.5.0 dated 2026-10-14

Title: AI Screening Tools in R for Systematic Reviewing
Description: Provides functions to conduct title and abstract screening in systematic reviews using large language models, such as the Generative Pre-trained Transformer (GPT) models from 'OpenAI' <https://developers.openai.com/>. These functions can enhance the quality of title and abstract screenings while reducing the total screening time significantly. In addition, the package includes tools for quality assessment of title and abstract screenings, as described in Vembye, Christensen, Mølgaard, and Schytt (2025) <DOI:10.1037/met0000769>.
Author: Mikkel H. Vembye [aut, cre] , Thomas Olsen [aut]
Maintainer: Mikkel H. Vembye <mikkel.vembye@gmail.com>

Diff between AIscreenR versions 0.5.0 dated 2026-10-14 and 0.5.1 dated 2026-11-13

 DESCRIPTION                                           |    6 
 MD5                                                   |  114 ++--
 NEWS.md                                               |   16 
 R/api_key_functions.R                                 |    2 
 R/approximate_price.R                                 |   33 -
 R/claude_engine.r                                     |   15 
 R/data.R                                              |    2 
 R/function-calling.R                                  |  452 +++++++++++++++++-
 R/gemini_engine.r                                     |   15 
 R/groq_engine.r                                       |   41 +
 R/misc.func.hidden.price.R                            |  183 +++----
 R/misc.func.hidden.tabscreen.R                        |   87 ++-
 R/misc.helpers.R                                      |    6 
 R/misc.hidden.func.analyzer.R                         |    2 
 R/ollama_engine.r                                     |   31 +
 R/rate_limits.R                                       |    6 
 R/report_errors.r                                     |   15 
 R/screen_analyzer.R                                   |   11 
 R/screen_errors.R                                     |    2 
 R/screen_errors.gpt.R                                 |   12 
 R/solve_or_guess.R                                    |    4 
 R/tabscreen_claude.R                                  |   20 
 R/tabscreen_gemini.R                                  |   34 -
 R/tabscreen_gpt.R                                     |  112 +---
 R/tabscreen_gpt.original.R                            |    4 
 R/tabscreen_gpt.tools.R                               |   98 +--
 R/tabscreen_groq.r                                    |   22 
 R/tabscreen_mistral.R                                 |   20 
 R/tabscreen_ollama.r                                  |   20 
 build/partial.rdb                                     |binary
 build/vignette.rds                                    |binary
 data/claude_model_prizes.rda                          |binary
 data/gemini_model_prizes.rda                          |binary
 data/model_prizes.rda                                 |binary
 inst/doc/Using-GPT-API-Models-For-Screening.html      |    8 
 man/approximate_price_gpt.Rd                          |   26 -
 man/gemini_model_prizes.Rd                            |    2 
 man/get_api_key_gemini.Rd                             |    2 
 man/rate_limits_per_minute.Rd                         |    6 
 man/report.Rd                                         |    5 
 man/screen_analyzer.Rd                                |    2 
 man/screen_errors.Rd                                  |    2 
 man/screen_errors.gpt.Rd                              |    2 
 man/solve_or_guess.Rd                                 |    4 
 man/tabscreen_claude.Rd                               |   11 
 man/tabscreen_gemini.Rd                               |   25 
 man/tabscreen_gpt.original.Rd                         |    4 
 man/tabscreen_gpt.tools.Rd                            |   34 -
 man/tabscreen_gpt.tools_responses.Rd                  |   42 -
 man/tabscreen_groq.Rd                                 |   11 
 man/tabscreen_mistral.Rd                              |   11 
 man/tabscreen_ollama.Rd                               |   11 
 tests/testthat/test-approximate_price_gpt.R           |   55 +-
 tests/testthat/test-misc.func.hidden.solve_or_guess.R |    6 
 tests/testthat/test-rate_limits.R                     |    7 
 tests/testthat/test-report_errors.r                   |   33 +
 tests/testthat/test-solve_or_guess.R                  |    6 
 tests/testthat/test-tabscreen_gpt.R                   |  277 ++++-------
 58 files changed, 1261 insertions(+), 716 deletions(-)

More information about AIscreenR at CRAN
Permanent link

New package vantorr with initial version 0.3.0
Package: vantorr
Title: Access 'Maxar' and 'Vantor' Satellite Imagery APIs
Version: 0.3.0
Description: Provides a client for the 'Maxar' and 'Vantor' 'REST' and 'OGC' APIs (<https://developers.maxar.com/docs/>), enabling programmatic access to high-resolution satellite imagery (such as 'WorldView', 'Vivid' base maps, and analysis-ready data). Includes functions for 'OAuth2' and API key authentication, discovery and 'STAC' catalog search, 'OGC' web services ('WMS', 'WMTS', 'WFS'), survey fieldwork resource generation (printable field atlases, index grids, 'Survey Solutions' and 'ODK' assignment tables, offline 'GeoPackage' and 'Esri' tile packages), interactive 'leaflet' and 'shiny' application modules, and spatial data retrieval integrated with 'sf', 'terra', and 'stars'.
License: MIT + file LICENSE
URL: https://github.com/michael-cw/vantorr, https://michael-cw.github.io/vantorr/, https://developers.maxar.com/docs/
BugReports: https://github.com/michael-cw/vantorr/issues
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.1.0)
Imports: cli, httr2, sf
Suggests: bslib, knitr, leaflet, markdown, rmarkdown, shiny, stars, terra, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-28 09:10:10 UTC; michaelwild
Author: Michael Wild [aut, cre]
Maintainer: Michael Wild <michael.wild@me.com>
Repository: CRAN
Date/Publication: 2026-11-13 15:50:02 UTC

More information about vantorr at CRAN
Permanent link

New package SSPICE with initial version 0.1.0
Package: SSPICE
Title: Systematic Skip-Pattern Integrity and Consistency Engine
Version: 0.1.0
Description: Provides a graph-based framework for representing, analyzing, and validating structured statistical data governed by explicit logical dependencies, including questionnaire routing and other rule-based data structures. It supports the construction and structural analysis of directed acyclic graphs, preparation of record-level data, detection and localization of logical and skip-pattern inconsistencies, and generation of variable domains and formal compatibility rules. It also provides tools for evaluating empirical closure and minimality properties of the resulting Horn theory and for supporting the correction of inconsistent statistical records.
License: GPL (>= 3)
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.2.0)
Imports: gmp, cli, doParallel, dplyr, foreach, jsonlite, parallel, pdftools, readr, stringi, stringr, tibble, withr (>= 2.5.0)
Suggests: knitr, pkgload, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Author: Pierpaolo Massoli [aut, cre]
Maintainer: Pierpaolo Massoli <pimassol@istat.it>
Packaged: 2026-10-26 09:16:45 UTC; root
Repository: CRAN
Date/Publication: 2026-11-13 15:50:08 UTC

More information about SSPICE at CRAN
Permanent link

Package npRmpi updated to version 0.80-2 with previous version 0.70-5 dated 2026-07-14

Title: Parallel Nonparametric Kernel Smoothing Methods for Mixed Data Types Using 'MPI'
Description: Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. This package is a parallel implementation of the 'np' package based on the 'MPI' specification that incorporates the 'Rmpi' package (Hao Yu <hyu@stats.uwo.ca>) with minor modifications and we are extremely grateful to Hao Yu for his contributions to the 'R' community. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://sshrc-crsh.canada.ca/en.aspx>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the 'GNU GSL' authors. In particular, we adapt the 'GNU GSL' B-spline routine 'gsl_bspline.c' adding automated support for quantile knots (in add [...truncated...]
Author: Jeffrey S. Racine [aut, cre], Tristen Hayfield [aut], Hao Yu [ctb, cph], The GSL Team [cph], Numerical Recipes Software [cph]
Maintainer: Jeffrey S. Racine <racinej@mcmaster.ca>

Diff between npRmpi versions 0.70-5 dated 2026-07-14 and 0.80-2 dated 2026-11-13

 npRmpi-0.70-5/npRmpi/R/gsl_bspline.R                                                                       |only
 npRmpi-0.70-5/npRmpi/man/nptgauss.Rd                                                                       |only
 npRmpi-0.70-5/npRmpi/src/gsl_bspline.c                                                                     |only
 npRmpi-0.70-5/npRmpi/src/kernele.c                                                                         |only
 npRmpi-0.70-5/npRmpi/src/kernelw.c                                                                         |only
 npRmpi-0.70-5/npRmpi/src/linalg.c                                                                          |only
 npRmpi-0.70-5/npRmpi/src/linalg.h                                                                          |only
 npRmpi-0.70-5/npRmpi/tests/testthat/test-bounded-distribution-truncated-cdf-contract.R                     |only
 npRmpi-0.70-5/npRmpi/tests/testthat/test-gsl-bs-s3-contract.R                                              |only
 npRmpi-0.70-5/npRmpi/tests/testthat/test-local-linear-raw-basis-error-guidance.R                           |only
 npRmpi-0.70-5/npRmpi/tests/testthat/test-npreg-nomad-shadow-fast-contract.R                                |only
 npRmpi-0.70-5/npRmpi/tests/testthat/test-profile-direct-autodispatch-contract.R                            |only
 npRmpi-0.80-2/npRmpi/CHANGELOG                                                                             |   12 
 npRmpi-0.80-2/npRmpi/DESCRIPTION                                                                           |   21 
 npRmpi-0.80-2/npRmpi/MD5                                                                                   | 2108 
 npRmpi-0.80-2/npRmpi/NAMESPACE                                                                             |   27 
 npRmpi-0.80-2/npRmpi/NEWS.md                                                                               | 4676 
 npRmpi-0.80-2/npRmpi/R/Rcoll.R                                                                             |  183 
 npRmpi-0.80-2/npRmpi/R/Rmpi.R                                                                              |   50 
 npRmpi-0.80-2/npRmpi/R/Rparutilities.R                                                                     |  471 
 npRmpi-0.80-2/npRmpi/R/a.bw.dispatch.R                                                                     |  313 
 npRmpi-0.80-2/npRmpi/R/bandwidth.R                                                                         |   33 
 npRmpi-0.80-2/npRmpi/R/conbandwidth.R                                                                      |   81 
 npRmpi-0.80-2/npRmpi/R/condbandwidth.R                                                                     |   81 
 npRmpi-0.80-2/npRmpi/R/condensity.R                                                                        |   91 
 npRmpi-0.80-2/npRmpi/R/condistribution.R                                                                   |   91 
 npRmpi-0.80-2/npRmpi/R/conmode.R                                                                           |   58 
 npRmpi-0.80-2/npRmpi/R/dbandwidth.R                                                                        |   33 
 npRmpi-0.80-2/npRmpi/R/density.R                                                                           |   25 
 npRmpi-0.80-2/npRmpi/R/distribution.R                                                                      |   25 
 npRmpi-0.80-2/npRmpi/R/kbandwidth.R                                                                        |   64 
 npRmpi-0.80-2/npRmpi/R/lsqregression.R                                                                     |  248 
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 npRmpi-0.80-2/npRmpi/R/np.ann.uniform.se.R                                                                 |only
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 npRmpi-0.80-2/npRmpi/R/np.bandwidth.procedure.R                                                            |only
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 npRmpi-0.80-2/npRmpi/R/np.bias.process.R                                                                   |only
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 npRmpi-0.80-2/npRmpi/R/np.bias.refinement.R                                                                |only
 npRmpi-0.80-2/npRmpi/R/np.bias.regression.R                                                                |only
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 npRmpi-0.80-2/npRmpi/R/np.bias.transition.R                                                                |only
 npRmpi-0.80-2/npRmpi/R/np.categorical.profile.tile.R                                                       |only
 npRmpi-0.80-2/npRmpi/R/np.cdhat.helpers.R                                                                  |  676 
 npRmpi-0.80-2/npRmpi/R/np.cmstest.R                                                                        |  377 
 npRmpi-0.80-2/npRmpi/R/np.condensity.R                                                                     |  331 
 npRmpi-0.80-2/npRmpi/R/np.condensity.bw.R                                                                  | 1100 
 npRmpi-0.80-2/npRmpi/R/np.condensity.proper.R                                                              |  123 
 npRmpi-0.80-2/npRmpi/R/np.condensity.slice.helpers.R                                                       |   67 
 npRmpi-0.80-2/npRmpi/R/np.condensity.slice.proper.R                                                        |   26 
 npRmpi-0.80-2/npRmpi/R/np.condistribution.R                                                                |  320 
 npRmpi-0.80-2/npRmpi/R/np.condistribution.bw.R                                                             |  833 
 npRmpi-0.80-2/npRmpi/R/np.condistribution.proper.R                                                         |   31 
 npRmpi-0.80-2/npRmpi/R/np.condistribution.slice.helpers.R                                                  |   35 
 npRmpi-0.80-2/npRmpi/R/np.condistribution.slice.proper.R                                                   |   28 
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 npRmpi-0.80-2/npRmpi/R/np.conmode.R                                                                        |  491 
 npRmpi-0.80-2/npRmpi/R/np.copula.R                                                                         |  563 
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 npRmpi-0.80-2/npRmpi/R/np.density.R                                                                        |  192 
 npRmpi-0.80-2/npRmpi/R/np.density.bw.R                                                                     |  342 
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 npRmpi-0.80-2/npRmpi/R/np.lsqregression.R                                                                  |  971 
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 npRmpi-0.80-2/npRmpi/R/np.plot.engine.condbandwidth.R                                                      |  606 
 npRmpi-0.80-2/npRmpi/R/np.plot.engine.dbandwidth.R                                                         |  315 
 npRmpi-0.80-2/npRmpi/R/np.plot.engine.plbandwidth.R                                                        |  361 
 npRmpi-0.80-2/npRmpi/R/np.plot.engine.rbandwidth.R                                                         |  609 
 npRmpi-0.80-2/npRmpi/R/np.plot.engine.scbandwidth.R                                                        |  411 
 npRmpi-0.80-2/npRmpi/R/np.plot.engine.sibandwidth.R                                                        |  211 
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Package mlAEWMA updated to version 0.1.1 with previous version 0.1.0 dated 2026-11-08

Title: Machine Learning Based Adaptive EWMA Control Charts for Process Dispersion
Description: Monitors the variance of a process with adaptive exponentially weighted moving average (AEWMA) control charts whose smoothing weight is predicted by a regression model fitted to the transformed subgroup variance. The subgroup variance is transformed as in Castagliola (2005) <doi:10.1002/qre.686>; the smoothing weight is predicted by one of nine pre-fitted models from four families (neural networks, support vector machines, regression trees and boosting, and a quantile regression with lagged inputs), following the parameter-free approach of Noor-ul-Amin et al. (2024) <doi:10.1038/s41598-024-82699-8>, related work on support vector regression based adaptive EWMA charts by Kazmi and Noor-ul-Amin (2024) <doi:10.1002/qre.3603>, and the adaptive EWMA idea of Capizzi and Masarotto (2003) <doi:10.1198/004017003000000023>. The comparison of the models is described in Atar, Purutçuoğlu and Bursalı (2026), accepted at the 3rd International Congress on Data Science and Info [...truncated...]
Author: Gizem Atar [aut, cre], Vilda Purutcuoglu [aut], Ahmet Bursali [aut]
Maintainer: Gizem Atar <gizem.atar@ostimteknik.edu.tr>

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Package gdtools updated to version 0.6.0 with previous version 0.5.3 dated 2026-11-05

Title: Font Metrics and Font Management Utilities for R Graphics
Description: Compute text metrics (width, ascent, descent), with or without kerning, from font files with 'FreeType', independently of the active graphic device. Font lookup is delegated to 'systemfonts'. Additional utilities let users register 'Google Fonts', the bundled 'Liberation' fonts or the bundled math font 'STIX Two Math', check font availability, and assemble 'htmlDependency' objects so that fonts are correctly embedded in 'Shiny' applications, 'R Markdown' documents, and 'htmlwidgets' outputs such as 'ggiraph'.
Author: David Gohel [aut, cre], Hadley Wickham [aut], Lionel Henry [aut], Jeroen Ooms [aut] , Yixuan Qiu [ctb], ArData [cph], RStudio [cph], The STIX Fonts Project Authors [cph]
Maintainer: David Gohel <david.gohel@ardata.fr>

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Package deltabreedquery updated to version 1.0.5 with previous version 1.0.4 dated 2026-09-30

Title: Fast, Simple API Tools for Retrieving Data from 'DeltaBreed'
Description: Simplified data retrieval from the 'DeltaBreed' breeding data management platform (<https://sandbox.breedinginsight.net/>) via the 'BrAPI' open-source breeding data API (<https://brapi.org/specification>). Each of the four main data types stored in 'DeltaBreed' (germplasm, trait variables, experiments/environments, and observations) are handled by a get_datatype() function that constructs and executes the request, handles paginated responses, and parses the retrieved data. Responses are reformatted into a consistent, R-friendly data frame format that resembles how the data appears on the 'DeltaBreed' web interface as closely as possible.
Author: Tyr Wiesner-Hanks [aut, cre]
Maintainer: Tyr Wiesner-Hanks <twiesnerhanks@ufl.edu>

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Package coda.base updated to version 1.0.8 with previous version 1.0.6 dated 2026-05-08

Title: A Basic Set of Functions for Compositional Data Analysis
Description: A minimum set of functions to perform compositional data analysis using the log-ratio approach introduced by John Aitchison (1982) <doi:10.1111/j.2517-6161.1982.tb01195.x>. Main functions have been implemented in C++ for better performance.
Author: Marc Comas-Cufi [aut, cre]
Maintainer: Marc Comas-Cufi <mcomas@imae.udg.edu>

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Package ssMRCD updated to version 2.0.3 with previous version 2.0.1 dated 2025-09-16

Title: Robust Estimators for Multi-Group and Spatial Data
Description: Estimation of robust estimators for multi-group and spatial data including the casewise robust Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage for local outlier detection as described in Puchhammer and Filzmoser (2023) <doi:10.1080/10618600.2023.2277875> as well as for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2026) <doi:10.1007/s11222-025-10771-y>. Moreover, a cellwise robust multi-group Gaussian mixture model (MG-GMM) is implemented as described in Puchhammer, Wilms and Filzmoser (2025) <doi:10.48550/arXiv.2504.02547>. Included are also complementary visualization and parameter tuning tools.
Author: Patricia Puchhammer [aut, cre, cph], Peter Filzmoser [aut], Ines Wilms [aut]
Maintainer: Patricia Puchhammer <patricia.puchhammer@tuwien.ac.at>

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Package rewind updated to version 0.4.0 with previous version 0.3.0 dated 2026-09-25

Title: Undo and Redo for 'shiny' Applications
Description: Adds a user-facing undo and redo history to 'shiny' applications. Application state, comprising registered inputs and optionally server-side reactive values, is captured as the user interacts with the application. Users may then step backwards and forwards through that history with the keyboard, with buttons, or by scrubbing a visual history rail. Rapid successive changes, such as dragging a slider, are coalesced into a single history entry, and related changes may be grouped explicitly into semantic steps.
Author: Tanmay Chanda [aut, cre, cph]
Maintainer: Tanmay Chanda <tanmaychanda96@gmail.com>

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Package np updated to version 0.80-2 with previous version 0.80-1 dated 2026-10-26

Title: Nonparametric Kernel Smoothing Methods for Mixed Data Types
Description: Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://sshrc-crsh.canada.ca/en.aspx>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.
Author: Jeffrey S. Racine [aut, cre], Tristen Hayfield [aut]
Maintainer: Jeffrey S. Racine <racinej@mcmaster.ca>

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Package ModalNet updated to version 0.3.1 with previous version 0.3.0 dated 2026-11-04

Title: Regularized Parametric Modal Regression: Lasso, Elastic Net and Ridge
Description: Variable selection and estimation for parametric modal regression with continuous positive responses (Gamma, Weibull and Lognormal families) under Lasso, elastic-net, ridge and adaptive-Lasso penalties. The conditional mode is linked to a linear predictor through a logarithmic link using the mode reparameterization of Galarza and Lachos (2026) <doi:10.48550/arXiv.2603.07099>. The penalized log-likelihood is maximized with a proximal Newton algorithm (iteratively reweighted penalized least squares): at each iteration the modal log-likelihood is replaced by a local quadratic surrogate in the linear predictor, which yields a pseudo-response and a working weight in closed form, and the resulting weighted penalized least-squares subproblem is solved by the coordinate-descent solver of 'glmnet' (Friedman, Hastie and Tibshirani, 2010, <doi:10.18637/jss.v033.i01>). The regularization parameter is chosen along a warm-started path by the extended Bayesian information criterion of Che [...truncated...]
Author: Christian E. Galarza [aut, cre, cph] , Victor H. Lachos [aut] , Dashun Liu [aut]
Maintainer: Christian E. Galarza <chedgala@espol.edu.ec>

Diff between ModalNet versions 0.3.0 dated 2026-11-04 and 0.3.1 dated 2026-11-13

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Package liver updated to version 1.31 with previous version 1.30 dated 2026-09-10

Title: Toolkit and Datasets for Data Science
Description: Provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book <https://book-data-science-r.netlify.app>, making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge.
Author: Reza Mohammadi [aut, cre] , Jeroen van Raak [ctb] , Kevin Burke [ctb]
Maintainer: Reza Mohammadi <a.mohammadi@uva.nl>

Diff between liver versions 1.30 dated 2026-09-10 and 1.31 dated 2026-11-13

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More information about liver at CRAN
Permanent link

Package bones updated to version 0.2.0 with previous version 0.1.1 dated 2026-10-16

Title: Content-Shaped Loading Placeholders for 'shiny' Outputs
Description: Shows a grey placeholder in the shape of the content while a 'shiny' output loads, in place of a spinner. The user can thus see what is coming, and not only that something happens. The shape comes from the type of the output. The placeholder keeps the space of the content, so the page does not move when the content arrives. When an output calculates again, the old content stays on the screen, dimmed, and a placeholder does not replace it.
Author: Tanmay Chanda [aut, cre, cph]
Maintainer: Tanmay Chanda <tanmaychanda96@gmail.com>

Diff between bones versions 0.1.1 dated 2026-10-16 and 0.2.0 dated 2026-11-13

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Permanent link

Package bhetGP updated to version 1.0.3 with previous version 1.0.2 dated 2026-02-08

Title: Bayesian Heteroskedastic Gaussian Processes
Description: Performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) <doi:10.1080/10618600.2018.1458625>) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), <doi:10.1080/10618600.2022.2129662>). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood.
Author: Parul V. Patil [aut, cre]
Maintainer: Parul V. Patil <parulvijay@vt.edu>

Diff between bhetGP versions 1.0.2 dated 2026-02-08 and 1.0.3 dated 2026-11-13

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Package BFI updated to version 3.4.0 with previous version 3.3.0 dated 2026-10-28

Title: Bayesian Federated Inference
Description: The Bayesian Federated Inference ('BFI') method combines inference results obtained from local data sets in the separate centers. In this version of the package, the 'BFI' methodology is programmed for linear, logistic and survival regression models. For GLMs, see Jonker, Pazira and Coolen (2024) <doi:10.1002/sim.10072>; for survival models, see Pazira, Massa, Weijers, Coolen and Jonker (2026) <doi:10.1080/02664763.2025.2511932>; and for heterogeneous populations, see Jonker, Pazira and Coolen (2025) <doi:10.1017/rsm.2025.6>.
Author: Hassan Pazira [aut, cre] , Emanuele Massa [aut] , Marianne A. Jonker [aut]
Maintainer: Hassan Pazira <h.pazira@arq.org>

Diff between BFI versions 3.3.0 dated 2026-10-28 and 3.4.0 dated 2026-11-13

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Package dartR.data updated to version 1.2.7 with previous version 1.2.2 dated 2026-03-17

Title: Auxiliary Data Package for Our Main Package 'dartR'
Description: Data package for 'dartR'. Provides data sets to run examples in 'dartR'. This was necessary due to the size limit imposed by 'CRAN'. The data in 'dartR.data' is needed to run the examples provided in the 'dartR' functions. All available data sets are either based on actual data (but reduced in size) and/or simulated data sets to allow the fast execution of examples and demonstration of the functions.
Author: Bernd Gruber [aut, cre], Arthur Georges [aut], Jose L. Mijangos [aut], Carlo Pacioni [aut], Oliver Berry [ctb]
Maintainer: Bernd Gruber <bernd.gruber@canberra.edu.au>

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Package tidytree updated to version 0.4.9 with previous version 0.4.8 dated 2026-07-02

Title: A Tidy Tool for Phylogenetic Tree Data Manipulation
Description: Phylogenetic trees contain components such as nodes, edges, branches, and associated data. As the core data manipulation tool in the 'ggtree' ecosystem, 'tidytree' provides tools for converting tree objects to tidy data frames and tidy interfaces for manipulating, analyzing, and visualizing tree data.
Author: Guangchuang Yu [aut, cre, cph] , Bradley Jones [ctb], Zebulun Arendsee [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

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Package mlt updated to version 1.9-0 with previous version 1.8-4 dated 2026-10-13

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] , Michael Thimo Brunner [aut]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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Package grates updated to version 1.8.2 with previous version 1.8.1 dated 2026-11-11

Title: Grouped Date Classes
Description: Provides a coherent interface and implementation for creating grouped date classes.
Author: Tim Taylor [aut, cre]
Maintainer: Tim Taylor <tim.taylor@hiddenelephants.co.uk>

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Package azr updated to version 0.3.7 with previous version 0.3.6 dated 2026-09-22

Title: Credential Chain for OAuth 2.0 Authentication to 'Azure'
Description: Implements a credential chain for 'Azure' OAuth 2.0 authentication, built on the OAuth framework of the 'httr2' package. Tries authentication methods in turn until one succeeds, so the same code runs on a laptop, in continuous integration and on an 'Azure' host. Supports client secret, workload identity, managed identity and 'Azure CLI' credentials, plus interactive device code and authorization code flows during development.
Author: Pedro Baltazar [aut, cre, cph]
Maintainer: Pedro Baltazar <pedrobtz@gmail.com>

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Package sondage updated to version 0.15.0 with previous version 0.9.1 dated 2026-08-04

Title: Survey Sampling Algorithms
Description: Fast implementations of survey sampling algorithms for single-stage probability sampling from finite populations, written in C. Provides equal probability methods (simple random sampling, systematic, Bernoulli), unequal probability methods (conditional Poisson / maximum entropy, Sampford, Brewer, systematic PPS, Pareto, sequential Poisson, Poisson, Chromy's sequential and systematic PPS with minimum replacement, multinomial), balanced sampling via the cube method, and spatially balanced sampling via the local pivotal method and spatially correlated Poisson sampling. All sampling functions return design objects carrying sample indices, inclusion probabilities, and design metadata. Generics compute joint inclusion probabilities, pairwise expectations, and sampling covariances for variance estimation. Mostly based on algorithms from Tillé (2006, <doi:10.1007/0-387-34240-0>).
Author: Ahmadou Dicko [aut, cre] , Thomas Lumley [ctb], Wilmer Prentius [ctb, cph] cube implementation from which src/cube.c was ported)
Maintainer: Ahmadou Dicko <mail@ahmadoudicko.com>

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Package upndown updated to version 1.0.0 with previous version 0.3.0 dated 2025-06-16

Title: Utilities and Design Aids for Up-and-Down Dose-Finding Studies
Description: Up-and-Down (UD) is the most popular design approach to dose-finding, but it has been severely under-served by the statistical and computing communities. This is the first package that comprehensively addresses UD's needs. Development version available on Github: assaforon/upndown. Recent applied UD tutorial: Oron et al., 2022 <doi:10.1097/ALN.0000000000004282>. Recent methodological overview: Oron and Flournoy, 2025 <doi:10.51387/24-NEJSDS74>.
Author: Assaf P. Oron [cre, aut]
Maintainer: Assaf P. Oron <assaf.oron@gmail.com>

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Package SSIMmap updated to version 0.4.3 with previous version 0.4.0 dated 2026-05-07

Title: The Structural Similarity Index Measure for Maps
Description: Computes the Structural Similarity Index Measure (SSIM) and its components for raster images and irregular polygon maps. The raster method follows Wang et al. (2004) <doi:10.1109/TIP.2003.819861>. The polygon method uses geographically weighted summary statistics described by Brunsdon et al. (2002) <doi:10.1016/S0198-9715(01)00009-6> with an adaptive nearest-neighbor kernel. Provides bandwidth selection, value transformations, permutation-based inference, Fire Weather Index rasters, and Toronto census-derived example data.
Author: Hui Jeong Ha [aut, cre] , Jed Long [aut]
Maintainer: Hui Jeong (Hailyee) Ha <hha24@uwo.ca>

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Package rD3plot updated to version 1.1.71 with previous version 1.1.45 dated 2026-01-10

Title: Interactive Networks, Timelines, Barplots, Galleries with 'D3.js'
Description: Creates interactive analytic graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as 'D3.js' graphs embedded in a web page ready for its interactive analysis and exploration.
Author: Modesto Escobar [aut, cph, cre] , Carlos Prieto [aut] , David Barrios [aut]
Maintainer: Modesto Escobar <modesto@usal.es>

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Package propertee updated to version 1.0.7 with previous version 1.0.6 dated 2026-07-20

Title: Standardization-Based Effect Estimation with Optional Prior Covariance Adjustment
Description: The Prognostic Regression Offsets with Propagation of ERrors (for Treatment Effect Estimation) package facilitates direct adjustment for experiments and observational studies that is compatible with a range of study designs and covariance adjustment strategies. It uses explicit specification of clusters, blocks and treatment allocations to furnish probability of assignment-based weights targeting any of several average treatment effect parameters, and for standard error calculations reflecting these design parameters. For covariance adjustment of its Hajek and (one-way) fixed effects estimates, it enables offsetting the outcome against predictions from a dedicated covariance model, with standard error calculations propagating error as appropriate from the covariance model.
Author: Josh Errickson [aut], Josh Wasserman [cre, aut], Mark Fredrickson [ctb], Tim Lycurgus [ctb], Adam Sales [ctb], Xinhe Wang [ctb], Ben Hansen [aut]
Maintainer: Josh Wasserman <jwass@umich.edu>

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More information about propertee at CRAN
Permanent link

Package lactater updated to version 1.0.0 with previous version 0.2.0 dated 2023-11-16

Title: Tools for Analyzing Lactate Thresholds
Description: Set of tools for analyzing lactate thresholds from a step incremental test to exhaustion. Estimate Log-log, Onset of Blood Lactate Accumulation (OBLA), Baseline plus (Bsln+), the Dmax family, Lactate Turn Points (LTP), the Lactate / Intensity ratio (LTratio), and the Individual Anaerobic Thresholds of Dickhuth (IAT) and Stegmann in cycling, running, swimming, or rowing, with bootstrap confidence intervals, data-quality checks, training zones, test comparisons, training load, and the maximal lactate accumulation rate (VLamax). Beaver WL, Wasserman K, Whipp BJ (1985) <doi:10.1152/jappl.1985.59.6.1936>. Heck H, Mader A, Hess G, Mücke S, Müller R, Hollmann W (1985) <doi:10.1055/s-2008-1025824>. Kindermann W, Simon G, Keul J (1979) <doi:10.1007/BF00421101>. Skinner JS, Mclellan TH (1980) <doi:10.1080/02701367.1980.10609285>. Berg A, Jakob E, Lehmann M, Dickhuth HH, Huber G, Keul J (1990) PMID 2408033. Zoladz JA, Rademaker AC, Sargeant AJ (1995) <doi:10.1113/jphysi [...truncated...]
Author: Felipe Mattioni Maturana [aut, cre]
Maintainer: Felipe Mattioni Maturana <felipe.mattioni@med.uni-tuebingen.de>

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Permanent link

Package INLAvaan updated to version 0.3.4 with previous version 0.3.3 dated 2026-10-26

Title: Approximate Bayesian Latent Variable Analysis
Description: Implements approximate Bayesian inference for Structural Equation Models (SEM) using a custom adaptation of the Integrated Nested Laplace Approximation (Rue et al., 2009) <doi:10.1111/j.1467-9868.2008.00700.x> as described in Jamil and Rue (2026a) <doi:10.48550/arXiv.2603.25690>. Provides a computationally efficient alternative to Markov Chain Monte Carlo (MCMC) for Bayesian estimation, allowing users to fit latent variable models using the 'lavaan' syntax. See also the companion paper on implementation and workflows, Jamil and Rue (2026b) <doi:10.48550/arXiv.2604.00671>.
Author: Haziq Jamil [aut, cre, cph] , Havard Rue [ctb] , Alvin Bong [ctb]
Maintainer: Haziq Jamil <haziq.jamil@gmail.com>

Diff between INLAvaan versions 0.3.3 dated 2026-10-26 and 0.3.4 dated 2026-11-13

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Package heumilkr updated to version 0.4.0 with previous version 0.3.0 dated 2025-04-24

Title: Heuristic Capacitated Vehicle Routing Problem Solver
Description: Implements the Clarke-Wright algorithm to find a quasi-optimal solution to the Capacitated Vehicle Routing Problem. See Clarke, G. and Wright, J.R. (1964) <doi:10.1287/opre.12.4.568> for details. Sites can also have goods picked up and brought back to the depot ("backhaul"). The implementation is accompanied by helper functions to inspect its solution.
Author: Lukas Schneiderbauer [aut, cre, cph]
Maintainer: Lukas Schneiderbauer <lukas.schneiderbauer@gmail.com>

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Package fCWTr readmission to version 0.4.1 with previous version 0.2.1 dated 2024-06-17

Title: Fast Continuous Wavelet Transform
Description: Enables the usage of the fast continuous wavelet transform, originally implemented in the 'C++' library 'fCWT' by Lukas Arts. See Arts, P.A. and Van den Broek, E.L. (2022) <doi:10.1038/s43588-021-00183-z> for details.
Author: Lukas Schneiderbauer [aut, cre, cph] , Lukas P. A. Arts [cph] , Egon L. van den Broek [cph]
Maintainer: Lukas Schneiderbauer <lukas.schneiderbauer@gmail.com>

This is a re-admission after prior archival of version 0.2.1 dated 2024-06-17

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Package BetaDanish updated to version 1.0.0 with previous version 0.3.0 dated 2026-07-31

Title: The Beta-Danish Distribution for Lifetime Data Analysis
Description: Implements the four-parameter Beta-Danish distribution and its three-parameter Exponentiated Danish submodel for survival, reliability and lifetime data analysis, following Ahmad (2026), "Modeling Diverse Survival Patterns: The Development and Characterization of a New Four-Parameter Lifetime Distribution", PhD thesis, Allama Iqbal Open University. Density, distribution, quantile, survival, hazard and random generation functions are evaluated so as to retain accuracy in both tails; the upper tail is heavy, with a regularly varying survival function. Estimation covers maximum likelihood for complete and right-censored samples by a two-stage multi-start search that guarantees nesting of the submodel, with fits on the edge of the parameter space reported as such, ridge-penalized fitting for weakly identified regimes, a grouped likelihood for times recorded on a coarse grid, and Bayesian sampling by adaptive multi-chain Metropolis with convergence checks. Goodness of fit uses Kolmogorov-Sm [...truncated...]
Author: Bilal Ahmad [aut, cre], Muhammad Yameen Danish [aut]
Maintainer: Bilal Ahmad <bilalahmad.imcbh9@gmail.com>

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Package actisomni updated to version 0.5.0 with previous version 0.4.0 dated 2026-10-26

Title: Create Sleep Measures of Actigraphy and Activity Analysis
Description: Estimates sleep from wrist accelerometry and epoch-level activity data. Implements sleep-period-time guiders based on angle changes, posture, least-active periods, sustained inactivity, low activity, and diary or fixed time windows; combines guider windows with sustained-inactivity bouts to produce sleep/wake labels and consensus estimates. Provides diary helpers, and interfaces to the 'asleep', 'sleeper', and 'rester' machine-learning estimators. The angle-based and sustained-inactivity methods are described by van Hees et al. (2015) <doi:10.1371/journal.pone.0142533> and van Hees et al. (2018) <doi:10.1038/s41598-018-31266-z>; the model estimators are described by Yuan et al. (2024) <doi:10.1038/s41746-024-01065-0>, Sundararajan et al. (2021) <doi:10.1038/s41598-020-79217-x>, and Lorenzen et al. (2026) <doi:10.64898/2026.01.28.26345056>.
Author: John Muschelli [aut, cre]
Maintainer: John Muschelli <muschellij2@gmail.com>

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Package oddsapiio updated to version 0.2.1 with previous version 0.2.0 dated 2026-11-06

Title: Client for the 'Odds-API.io' Sports Betting Odds API
Description: Query live and upcoming sports betting odds from the 'Odds-API.io' REST API <https://docs.odds-api.io>. Covers sports, markets, bookmakers, leagues, participants, events, per-event and multi-event odds, odds deltas, odds movements, odds summaries, value bets, arbitrage bets, dropping odds, historical events, odds and closing lines, bet settlement and grading, outright (futures) odds and results, and bookmaker id mappings across 450+ bookmakers, returned as tidy data frames. An API key from a paid plan is required.
Author: Markus Bjermeland [aut, cre]
Maintainer: Markus Bjermeland <markus@odds-api.io>

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Package stratifyR updated to version 2.1-0 with previous version 2.0-4 dated 2026-10-24

Title: Optimal Stratification of Univariate Populations
Description: Determines Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for univariate stratified sampling designs under Neyman allocation. The stratification variable is described by a best-fitting parametric distribution, selected automatically by AIC from a set of continuous families (normal, log-normal, gamma, Weibull, exponential, Cauchy, uniform, Pareto, triangular and right-triangular), and the optimum boundaries are obtained by minimising the Neyman objective. Version 2.0 keeps the Dynamic Programming (DP) solver of Reddy and Khan (2020), which is globally optimal for the fitted distribution, as the default. It adds two fast derivative-free solvers, a multi-start 'COBYLA' solver and a two-phase global solver that couples 'DIRECT-L' with 'BOBYQA' refinement, and an exact solver that runs dynamic programming on the sorted data and returns the partition with the smallest empirical Neyman cost. It also provides cost-constrained allocation with unequal per-stratum costs, a desi [...truncated...]
Author: Karuna G. Reddy [aut, cre] , M. G. M. Khan [aut]
Maintainer: Karuna G. Reddy <karuna.reddy@auckland.ac.nz>

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Package FitVerse updated to version 1.0-3 with previous version 1.0-2 dated 2026-09-06

Title: Parametric Distribution Fitting and Analysis
Description: Provides a unified, user-friendly interface for fitting parametric probability distributions to continuous univariate data. 'FitVerse' supports 52 distribution families spanning symmetric, right-skewed, heavy-tailed, bounded, and extreme-value shapes, and three estimation methods: Maximum Likelihood Estimation (MLE), Method of Moments (MOM), and L-Moments (L-MOM). Automatic best-fit selection is performed using AIC, BIC, and goodness-of-fit tests (Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises (CvM)). Every fitted model produces a publication-quality diagnostic plot: a histogram overlaid with the fitted density curve and the estimated PDF formula annotated directly on the figure. An optional interactive version is produced via 'plotly'. Additional tools include bootstrap confidence intervals for parameter estimates and return levels, batch fitting across multiple columns for automated workflows and web-upload use cases, JSON serialisation for integration with 'Shiny' web applic [...truncated...]
Author: Karuna G. Reddy [aut, cre] , M. G. M. Khan [aut]
Maintainer: Karuna G. Reddy <karuna.reddy@auckland.ac.nz>

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 143 files changed, 768 insertions(+), 518 deletions(-)

More information about FitVerse at CRAN
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