Tue, 28 Apr 2026

Package seqinr updated to version 4.2-44 with previous version 4.2-36 dated 2023-12-08

Title: Biological Sequences Retrieval and Analysis
Description: Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Seqinr includes utilities for sequence data management under the ACNUC system described in Gouy, M. et al. (1984) Nucleic Acids Res. 12:121-127 <doi:10.1093/nar/12.1Part1.121>.
Author: Delphine Charif [aut], Olivier Clerc [ctb], Carolin Frank [ctb], Jean R. Lobry [aut, cph], Anamaria Necsulea [ctb], Leonor Palmeira [ctb], Simon Penel [cre], Guy Perriere [ctb]
Maintainer: Simon Penel <simon.penel@univ-lyon1.fr>

Diff between seqinr versions 4.2-36 dated 2023-12-08 and 4.2-44 dated 2026-04-28

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Package randomForestRHF updated to version 1.0.1 with previous version 1.0.0 dated 2026-04-23

Title: Random Hazard Forests
Description: Random Hazard Forests (RHF) extend Random Survival Forests (RSF) by directly estimating the hazard function and by accommodating time-dependent covariates through counting-process style inputs. The package fits tree ensembles for dynamic survival prediction, returning hazard, cumulative hazard, integrated hazard, and related performance summaries for training and test data. The methods build on Random Survival Forests described by Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> and on nonparametric hazard modeling with time-dependent covariates described by Lee et al. (2021) <doi:10.1214/20-AOS2028>.
Author: Hemant Ishwaran [aut], Udaya B. Kogalur [aut, cre]
Maintainer: Udaya B. Kogalur <ubk@kogalur.com>

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Package plotthis updated to version 0.12.0 with previous version 0.11.1 dated 2026-03-15

Title: High-Level Plotting Built Upon 'ggplot2' and Other Plotting Packages
Description: Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. It is built upon 'ggplot2' and other plotting packages, and is designed to be easy to use and to work seamlessly with 'ggplot2' objects. It is particularly useful for creating complex plots with multiple layers, facets, and annotations. It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees. The package is designed to be flexible and customizable, and to work well with the 'ggplot2' ecosystem. The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.
Author: Panwen Wang [aut, cre]
Maintainer: Panwen Wang <pwwang@pwwang.com>

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Package luz updated to version 0.5.2 with previous version 0.5.1 dated 2025-10-30

Title: Higher Level 'API' for 'torch'
Description: A high level interface for 'torch' providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to support expressing a large range of models. It's heavily inspired by 'fastai' by Howard et al. (2020) <doi:10.48550/arXiv.2002.04688>, 'Keras' by Chollet et al. (2015) and 'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.
Author: Tomasz Kalinowski [ctb, cre], Daniel Falbel [aut, cph], Christophe Regouby [ctb], RStudio [cph]
Maintainer: Tomasz Kalinowski <tomasz@posit.co>

Diff between luz versions 0.5.1 dated 2025-10-30 and 0.5.2 dated 2026-04-28

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Package EHRtemporalVariability readmission to version 1.2.2 with previous version 1.2.1 dated 2024-01-26

Title: Delineating Temporal Dataset Shifts in Electronic Health Records
Description: Functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. 'EHRtemporalVariability' is particularly suitable for multi-modal data and categorical variables with a high number of values, common features of biomedical data where traditional statistical process control or time-series methods may not be appropriate. 'EHRtemporalVariability' allows you to explore and identify dataset shifts through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. An additional 'EHRtemporalVariability' Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R codi [...truncated...]
Author: Carlos Saez [aut, cre], Alba Gutierrez-Sacristan [aut], Isaac Kohane [aut], Juan M Garcia-Gomez [aut], Paul Avillach [aut], Biomedical Data Science Lab, Universitat Politecnica de Valencia [cph], Department of Biomedical Informatics, Harvard Medical [...truncated...]
Maintainer: Carlos Saez <carsaesi@upv.es>

This is a re-admission after prior archival of version 1.2.1 dated 2024-01-26

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Package PNADCperiods updated to version 0.1.2 with previous version 0.1.1 dated 2026-04-21

Title: Identify Reference Periods in Brazil's PNADC Survey Data
Description: Identifies reference periods (months, fortnights, and weeks) in Brazil's quarterly PNADC (Pesquisa Nacional por Amostra de Domicilios Continua) survey data and computes calibrated weights for sub-quarterly analysis. The core algorithm uses IBGE (Instituto Brasileiro de Geografia e Estatistica) 'Parada Tecnica' (technical break) rules combined with respondent birthdates to determine which temporal period each survey observation refers to. Period identification follows a nested hierarchy enforced by construction: fortnights require months, weeks require fortnights. Achieves approximately 97% monthly determination rate with the full series (2012-2025). Strict fortnight and week rates are approximately 9% and 3% respectively, as they cannot leverage cross-quarter panel aggregation. Experimental strategies (probabilistic assignment and UPA (Primary Sampling Unit) aggregation) further improve these determination rates. The package provides adaptive hierarchical weight calibration (4/2/1 cell [...truncated...]
Author: Rogerio Barbosa [aut, cre] , Marcos Hecksher [aut]
Maintainer: Rogerio Barbosa <rogerio.barbosa@iesp.uerj.br>

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Package hydroTSM updated to version 0.8-6 with previous version 0.7-0.1 dated 2024-11-04

Title: Time Series Management and Analysis for Hydrological Modelling
Description: S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
Author: Mauricio Zambrano-Bigiarini [aut, cre, cph] , Joschka Thurner [ctb]
Maintainer: Mauricio Zambrano-Bigiarini <mzb.devel@gmail.com>

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Package disdat updated to version 1.1-0 with previous version 1.0-1 dated 2023-02-07

Title: Data for Comparing Species Distribution Modeling Methods
Description: Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.
Author: Robert J. Hijmans [aut] , Roozbeh Valavi [cre, aut], Jane Elith [aut]
Maintainer: Roozbeh Valavi <valavi.r@gmail.com>

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Package ZetaSuite updated to version 1.0.3 with previous version 1.0.2 dated 2025-09-24

Title: Analyze High-Dimensional High-Throughput Dataset and Quality Control Single-Cell RNA-Seq
Description: The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In 'ZetaSuite', we have the following steps: QC of input datasets, normalizati [...truncated...]
Author: Junhui Li [aut, cre] , Yajing Hao [aut] , Shuyang Zhang [ctb] , Guofeng Zhao [ctb], Xiang-Dong Fu [cph, fnd]
Maintainer: Junhui Li <ljh.biostat@gmail.com>

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Package Rfssa (with last version 3.2.0) was removed from CRAN

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

2025-07-29 3.2.0
2024-01-10 3.1.0
2023-10-26 3.0.2
2023-10-23 3.0.1
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2022-09-08 2.1.0
2022-01-09 2.0.1

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Package camtrapR (with last version 3.0.3) was removed from CRAN

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2026-02-08 3.0.3
2026-01-25 3.0.2
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Package temper (with last version 1.1.0) was removed from CRAN

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

2025-08-18 1.1.0
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Package nFunNN (with last version 1.0) was removed from CRAN

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2024-04-28 1.0

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Package geodl (with last version 0.3.1) was removed from CRAN

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

2025-12-02 0.3.1
2025-11-12 0.3.0
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Package promor (with last version 0.2.2) was removed from CRAN

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

2025-11-11 0.2.2
2023-07-17 0.2.1
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Package scan updated to version 0.68.1 with previous version 0.68.0 dated 2026-04-01

Title: Single-Case Data Analyses for Single and Multiple Baseline Designs
Description: A collection of procedures for analysing, visualising, and managing single-case data. Multi-phase and multi-baseline designs are supported. Analysing methods include regression models (multilevel, multivariate, bayesian), between case standardised mean difference, overlap indices ('PND', 'PEM', 'PAND', 'NAP', 'PET', 'tau-u', 'IRD', 'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions support outlier detection, handling missing values, scaling, and custom transformations. An export function helps to generate html, word, and latex tables in a publication friendly style. A shiny app allows to use scan in a graphical user interface. More details can be found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2026) <https://jazznbass.github.io/scan-Book/>.
Author: Juergen Wilbert [cre, aut] , Timo Lueke [aut]
Maintainer: Juergen Wilbert <juergen.wilbert@uni-muenster.de>

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 scan-0.68.1/scan/R/private_check.R                 |    8 -
 scan-0.68.1/scan/R/private_check_scdf.R            |    4 
 scan-0.68.1/scan/R/private_kendall.R               |    4 
 scan-0.68.1/scan/R/private_mc_function.R           |    2 
 scan-0.68.1/scan/R/private_options.R               |    4 
 scan-0.68.1/scan/R/private_options_mc.R            |    8 -
 scan-0.68.1/scan/R/private_recombine_phases.R      |    8 -
 scan-0.68.1/scan/R/random_scdf.R                   |    2 
 scan-0.68.1/scan/R/rci.R                           |    2 
 scan-0.68.1/scan/R/read_scdf.R                     |    2 
 scan-0.68.1/scan/R/scan-package.R                  |    1 
 scan-0.68.1/scan/R/scdf-functions.R                |    6 -
 scan-0.68.1/scan/R/scdf.R                          |   18 +---
 scan-0.68.1/scan/R/shinyscan.R                     |    6 -
 scan-0.68.1/scan/man/abort.Rd                      |only
 scan-0.68.1/scan/man/notify.Rd                     |only
 scan-0.68.1/scan/man/warn.Rd                       |only
 scan-0.68.1/scan/tests/testthat/test-add_l2.R      |    2 
 scan-0.68.1/scan/tests/testthat/test-anova.R       |    6 -
 scan-0.68.1/scan/tests/testthat/test-bacht_apply.R |    2 
 scan-0.68.1/scan/tests/testthat/test-between_smd.R |    3 
 scan-0.68.1/scan/tests/testthat/test-cdc.R         |    5 -
 scan-0.68.1/scan/tests/testthat/test-corrected.R   |    3 
 43 files changed, 194 insertions(+), 158 deletions(-)

More information about scan at CRAN
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Package fracdiff updated to version 1.5-4 with previous version 1.5-3 dated 2024-02-01

Title: Fractionally Differenced ARIMA aka ARFIMA(P,d,q) Models
Description: Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); including inference and basic methods. Some alternative algorithms to estimate "H".
Author: Martin Maechler [aut, cre] , Chris Fraley [ctb, cph] , Friedrich Leisch [ctb] , Valderio Reisen [ctb] & fdSperio), Artur Lemonte [ctb] & fdSperio), Rob Hyndman [ctb] & fitted, ORCID: <https://orcid.org/0000-0002-2140-5352>)
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

Diff between fracdiff versions 1.5-3 dated 2024-02-01 and 1.5-4 dated 2026-04-28

 ChangeLog              |   12 +++++++++
 DESCRIPTION            |   19 ++++++++-------
 MD5                    |   26 ++++++++++----------
 R/fd-methods.R         |    1 
 R/fracdiff.R           |   41 ++++++++++++++++++++------------
 build/partial.rdb      |binary
 man/fd-methods.Rd      |    7 -----
 man/fracdiff.var.Rd    |   15 +++++++-----
 src/fdhess.c           |    2 -
 tests/ex.R             |   32 +++++++++++++++++--------
 tests/ex.Rout.save     |   56 +++++++++++++++++++++++++++-----------------
 tests/sim-2.R          |   47 ++++++++++++++++++++++++++++++++++---
 tests/sim-ex.R         |    6 ++--
 tests/sim-ex.Rout.save |   61 ++++++++++++++++++++++++-------------------------
 14 files changed, 204 insertions(+), 121 deletions(-)

More information about fracdiff at CRAN
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