Sun, 07 Dec 2025

Package DataGraph updated to version 1.2.15 with previous version 1.2.14 dated 2023-03-02

Title: Export Data from 'R' to 'DataGraph'
Description: Functions to pipe data from 'R' to 'DataGraph', a graphing and analysis application for mac OS. Create a live connection using either '.dtable' or '.dtbin' files that can be read by 'DataGraph'. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://community.visualdatatools.com/datagraph/knowledge-base/r-package/>.
Author: David Adalsteinsson [aut, cre]
Maintainer: David Adalsteinsson <david@visualdatatools.com>

Diff between DataGraph versions 1.2.14 dated 2023-03-02 and 1.2.15 dated 2025-12-07

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Package BEKKs updated to version 1.4.6 with previous version 1.4.5 dated 2024-11-25

Title: Multivariate Conditional Volatility Modelling and Forecasting
Description: Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>. For an overview, we refer the reader to Fülle et al. (2024) <doi:10.18637/jss.v111.i04>.
Author: Markus J. Fuelle [aut, cre], Alexander Lange [aut], Christian M. Hafner [aut], Helmut Herwartz [aut]
Maintainer: Markus J. Fuelle <markus.fuelle@gmail.com>

Diff between BEKKs versions 1.4.5 dated 2024-11-25 and 1.4.6 dated 2025-12-07

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Package mixgb updated to version 2.0.3 with previous version 1.5.3 dated 2025-04-06

Title: Multiple Imputation Through 'XGBoost'
Description: Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.
Author: Yongshi Deng [aut, cre] , Thomas Lumley [ths]
Maintainer: Yongshi Deng <agnes.yongshideng@gmail.com>

Diff between mixgb versions 1.5.3 dated 2025-04-06 and 2.0.3 dated 2025-12-07

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Package Rcpp updated to version 1.1.0.8 with previous version 1.1.0 dated 2025-07-02

Title: Seamless R and C++ Integration
Description: The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <https://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
Author: Dirk Eddelbuettel [aut, cre] , Romain Francois [aut] , JJ Allaire [aut] , Kevin Ushey [aut] , Qiang Kou [aut] , Nathan Russell [aut], Inaki Ucar [aut] , Doug Bates [aut] , John Chambers [aut]
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package nodbi updated to version 0.14.0 with previous version 0.13.1 dated 2025-06-26

Title: 'NoSQL' Database Connector
Description: Simplified JSON document database access and manipulation, providing a common API across supported 'NoSQL' databases 'Elasticsearch', 'CouchDB', 'MongoDB' as well as 'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.
Author: Ralf Herold [aut, cre] , Scott Chamberlain [aut] , Rich FitzJohn [aut], Jeroen Ooms [aut], Ivan Tarbakou [cph]
Maintainer: Ralf Herold <ralf.herold@mailbox.org>

Diff between nodbi versions 0.13.1 dated 2025-06-26 and 0.14.0 dated 2025-12-07

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Package datetoiso updated to version 1.2.0 with previous version 1.1.1 dated 2025-11-18

Title: Convert and Impute Dates to ISO Standard ("International Organization for Standardization")
Description: Provides functions to convert and impute date values to the ISO 8601 standard format. The package automatically recognizes date patterns within a data frame and transforms them into consistent ISO-formatted dates. It also supports imputing missing month or day components in partial date strings using user-defined rules. Only one date format can be applied within a single data frame column.
Author: Lukasz Andrzejewski [aut, cre]
Maintainer: Lukasz Andrzejewski <lukasz.coding@gmail.com>

Diff between datetoiso versions 1.1.1 dated 2025-11-18 and 1.2.0 dated 2025-12-07

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Package CFM updated to version 0.8.0 with previous version 0.7.0 dated 2025-11-13

Title: Analyzing Censored Factor Models
Description: Provides generation and estimation of censored factor models for high-dimensional data with censored errors (normal, t, logistic). Includes Sparse Orthogonal Principal Components (SOPC), and evaluation metrics. Based on Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
Author: Guangbao Guo [aut, cre], Tong Meng [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>

Diff between CFM versions 0.7.0 dated 2025-11-13 and 0.8.0 dated 2025-12-07

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Package Rwofost updated to version 0.8-7 with previous version 0.8-6 dated 2025-12-04

Title: WOFOST Crop Growth Simulation Model
Description: An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.
Author: Robert J. Hijmans [cre, aut], Huang Fang [ctb], C.A. van Diepen [ctb], Allard de Wit [ctb], Daniel van Kraalingen [ctb], Tamme van der Wal [ctb], C. Rappoldt [ctb], Hendrik Boogard [ctb], I.G.A.M. Noy [ctb], Alterra, Wageningen-UR [cph]
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

Diff between Rwofost versions 0.8-6 dated 2025-12-04 and 0.8-7 dated 2025-12-07

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Package rSpectral readmission to version 1.0.0.14 with previous version 1.0.0.10 dated 2023-01-18

Title: Spectral Modularity Clustering
Description: Implements the network clustering algorithm described in Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and o [...truncated...]
Author: Colin Mclean [aut] , Anatoly Sorokin [aut, cre]
Maintainer: Anatoly Sorokin <lptolik@gmail.com>

This is a re-admission after prior archival of version 1.0.0.10 dated 2023-01-18

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Package rrpack updated to version 0.1-14 with previous version 0.1-13 dated 2022-06-16

Title: Reduced-Rank Regression
Description: Multivariate regression methodologies including classical reduced-rank regression (RRR) studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>, reduced-rank regression via adaptive nuclear norm penalization proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>, robust reduced-rank regression (R4) proposed by She and Chen (2017) <doi:10.1093/biomet/asx032>, generalized/mixed-response reduced-rank regression (mRRR) proposed by Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>, row-sparse reduced-rank regression (SRRR) proposed by Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>, reduced-rank regression with a sparse singular value decomposition (RSSVD) proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x> and sparse and orthogonal factor regression (SOFAR) proposed by Uematsu et al. (2019) <d [...truncated...]
Author: Kun Chen [aut, cre] , Wenjie Wang [aut] , Jun Yan [ctb]
Maintainer: Kun Chen <kun.chen@uconn.edu>

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Package rintcal updated to version 1.3.1 with previous version 1.3.0 dated 2025-07-09

Title: Radiocarbon Calibration Curves
Description: The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98), other curves (e.g., NOTCal04 [van der Plicht et al. 2004], Arnold & Libby 1951, Stuiver & Suess 1966, Pearson & Stuiver 1986) and postbomb curves. Also provided are functions to copy the curves into memory, and to read, query and plot the data underlying the IntCal20 curves.
Author: Maarten Blaauw [aut, cre] , Christopher Bronk Ramsey [ctb]
Maintainer: Maarten Blaauw <maarten.blaauw@qub.ac.uk>

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Package openxlsx2 updated to version 1.22 with previous version 1.21 dated 2025-10-31

Title: Read, Write and Edit 'xlsx' Files
Description: Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets.
Author: Jordan Mark Barbone [aut] , Jan Marvin Garbuszus [aut, cre], Olivier Roy [ctb], openxlsx authors [cph] , Arseny Kapoulkine [ctb, cph]
Maintainer: Jan Marvin Garbuszus <jan.garbuszus@ruhr-uni-bochum.de>

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Package nebula updated to version 1.5.6 with previous version 1.5.3 dated 2024-02-15

Title: Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data
Description: A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.
Author: Liang He [aut, cre], Raghav Sharma [ctb]
Maintainer: Liang He <hyx520101@gmail.com>

Diff between nebula versions 1.5.3 dated 2024-02-15 and 1.5.6 dated 2025-12-07

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Package isoband updated to version 0.3.0 with previous version 0.2.7 dated 2022-12-20

Title: Generate Isolines and Isobands from Regularly Spaced Elevation Grids
Description: A fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.
Author: Hadley Wickham [aut] , Claus O. Wilke [aut] , Thomas Lin Pedersen [aut, cre] , Posit, PBC [cph, fnd]
Maintainer: Thomas Lin Pedersen <thomas.pedersen@posit.co>

Diff between isoband versions 0.2.7 dated 2022-12-20 and 0.3.0 dated 2025-12-07

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Package heplots updated to version 1.8.1 with previous version 1.7.8 dated 2025-08-18

Title: Visualizing Hypothesis Tests in Multivariate Linear Models
Description: Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.
Author: Michael Friendly [aut, cre] , John Fox [aut] , Georges Monette [aut] , Phil Chalmers [ctb] , Duncan Murdoch [ctb]
Maintainer: Michael Friendly <friendly@yorku.ca>

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Package fBasics updated to version 4052.98 with previous version 4041.97 dated 2024-08-19

Title: Rmetrics - Markets and Basic Statistics
Description: Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management.
Author: Diethelm Wuertz [aut] , Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] , CRAN Team [ctb], Georgi N. Boshnakov [cre, aut]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

Diff between fBasics versions 4041.97 dated 2024-08-19 and 4052.98 dated 2025-12-07

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Package Colossus updated to version 1.4.5 with previous version 1.4.3 dated 2025-10-30

Title: "Risk Model Regression and Analysis with Complex Non-Linear Models"
Description: Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.
Author: Eric Giunta [aut, cre] , Amir Bahadori [ctb] , Dan Andresen [ctb] , Linda Walsh [ctb] , Benjamin French [ctb] , Lawrence Dauer [ctb] , John Boice Jr [ctb] , Kansas State University [cph], NASA [fnd], NCRP [fnd], NRC [fnd]
Maintainer: Eric Giunta <egiunta@ksu.edu>

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Sat, 06 Dec 2025

Package worldbank readmission to version 0.7.1 with previous version 0.7.0 dated 2025-11-10

Title: Client for World Banks's 'Indicators' and 'Poverty and Inequality Platform (PIP)' APIs
Description: Download and search data from the 'World Bank Indicators API', which provides access to nearly 16,000 time series indicators. See <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation> for further details about the API.
Author: Maximilian Muecke [aut, cre]
Maintainer: Maximilian Muecke <muecke.maximilian@gmail.com>

This is a re-admission after prior archival of version 0.7.0 dated 2025-11-10

Diff between worldbank versions 0.7.0 dated 2025-11-10 and 0.7.1 dated 2025-12-06

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Package traktok readmission to version 0.1.1 with previous version 0.1.0 dated 2025-11-24

Title: Collecting 'TikTok' Data
Description: Getting 'TikTok' data (<https://www.tiktok.com/>) through the official and unofficial APIs—in other words, you can track 'TikTok'.
Author: Johannes B. Gruber [aut, cre]
Maintainer: Johannes B. Gruber <JohannesB.Gruber@gmail.com>

This is a re-admission after prior archival of version 0.1.0 dated 2025-11-24

Diff between traktok versions 0.1.0 dated 2025-11-24 and 0.1.1 dated 2025-12-06

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Package tmt updated to version 0.3.6-4 with previous version 0.3.6-0 dated 2025-06-20

Title: Estimation of the Rasch Model for Multistage Tests
Description: Provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2024, <doi:10.1007/s41237-024-00228-3>). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) as well as functions for simulating various types of multistage designs.
Author: Jan Steinfeld [cre, aut] , Alexander Robitzsch [aut]
Maintainer: Jan Steinfeld <jan.d.steinfeld@gmail.com>

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Package tidytreatment updated to version 0.3.2 with previous version 0.3.1 dated 2025-01-10

Title: Tidy Methods for Bayesian Treatment Effect Models
Description: Functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in 'tidybayes' <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.
Author: Joshua J Bon [aut, cre]
Maintainer: Joshua J Bon <joshuajbon@gmail.com>

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Package REFA updated to version 0.2.0 with previous version 0.1.0 dated 2023-11-19

Title: Robust Exponential Factor Analysis
Description: A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) <doi:10.1016/j.jmva.2025.105567>.
Author: Jiaqi Hu [cre, aut], Xueqin Wang [aut]
Maintainer: Jiaqi Hu <hujiaqi@mail.ustc.edu.cn>

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Package osrm.backend updated to version 0.1.1 with previous version 0.1.0 dated 2025-12-03

Title: Bindings for 'Open Source Routing Machine'
Description: Install and control 'Open Source Routing Machine' ('OSRM') backend executables to prepare routing data and run/stop a local 'OSRM' server. For computations with the running server use the 'osrm' R package (<https://cran.r-project.org/package=osrm>).
Author: Egor Kotov [aut, cre, cph]
Maintainer: Egor Kotov <kotov.egor@gmail.com>

Diff between osrm.backend versions 0.1.0 dated 2025-12-03 and 0.1.1 dated 2025-12-06

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Package modeltuning updated to version 0.1.3 with previous version 0.1.2 dated 2025-10-28

Title: Model Selection and Tuning Utilities
Description: Provides a lightweight framework for model selection and hyperparameter tuning in R. The package offers intuitive tools for grid search, cross-validation, and combined grid search with cross-validation that work seamlessly with virtually any modeling package. Designed for flexibility and ease of use, it standardizes tuning workflows while remaining fully compatible with a wide range of model interfaces and estimation functions.
Author: Daniel Molitor [aut, cre]
Maintainer: Daniel Molitor <molitdj97@gmail.com>

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Package gumboot updated to version 1.0.2 with previous version 1.0.1 dated 2023-10-18

Title: Bootstrap Analyses of Sampling Uncertainty in Goodness-of-Fit Statistics
Description: Uses jackknife and bootstrap methods to quantify the sampling uncertainty in goodness-of-fit statistics. Full details are in Clark et al. (2021), "The abuse of popular performance metrics in hydrologic modeling", Water Resources Research, <doi:10.1029/2020WR029001>.
Author: Martyn Clark [aut], Kevin Shook [aut, trl, cre]
Maintainer: Kevin Shook <kshook@kshook.ca>

Diff between gumboot versions 1.0.1 dated 2023-10-18 and 1.0.2 dated 2025-12-06

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Package gsDesign updated to version 3.8.0 with previous version 3.7.0 dated 2025-08-25

Title: Group Sequential Design
Description: Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.
Author: Keaven Anderson [aut, cre], Merck & Co., Inc., Rahway, NJ, USA and its affiliates [cph]
Maintainer: Keaven Anderson <keaven_anderson@merck.com>

Diff between gsDesign versions 3.7.0 dated 2025-08-25 and 3.8.0 dated 2025-12-06

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Package GrFA updated to version 0.2.2 with previous version 0.2.1 dated 2024-12-22

Title: Group Factor Analysis
Description: Implements statistical methods for group factor analysis, focusing on estimating the number of global and local factors and extracting them. Several algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008>, Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the Aggregated Projection Method by Hu et al. (2025) <doi:10.1080/01621459.2025.2491154>.
Author: Jiaqi Hu [cre, aut], Ting Li [aut], Xueqin Wang [aut]
Maintainer: Jiaqi Hu <hujiaqi@mail.ustc.edu.cn>

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Package FAfA updated to version 0.4 with previous version 0.3 dated 2025-05-23

Title: Factor Analysis for All
Description: Provides a comprehensive Shiny-based graphical user interface for conducting a wide range of factor analysis procedures. 'FAfA' (Factor Analysis for All) guides users through data uploading, assumption checking (descriptives, collinearity, multivariate normality, outliers), data wrangling (variable exclusion, data splitting), factor retention analysis (e.g., Parallel Analysis, Hull method, EGA), Exploratory Factor Analysis (EFA) with various rotation and extraction methods, Confirmatory Factor Analysis (CFA) for model testing, Reliability Analysis (e.g., Cronbach's Alpha, McDonald's Omega), Measurement Invariance testing across groups, and item weighting techniques. The application leverages established R packages such as 'lavaan' and 'psych' to perform these analyses, offering an accessible platform for researchers and students. Results are presented in user-friendly tables and plots, with options for downloading outputs.
Author: Abdullah Faruk KILIC [aut, cre]
Maintainer: Abdullah Faruk KILIC <afarukkilic@trakya.edu.tr>

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Package CimpleG updated to version 1.0.1 with previous version 1.0.0 dated 2025-12-03

Title: A Method to Identify Single CpG Sites for Classification and Deconvolution
Description: DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. 'CimpleG' is a method for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. 'CimpleG' is time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type (but users can also select more sites if they so wish). Users can train cell type classifiers ('CimpleG' based, and others) and directly apply these in a deconvolution of cell mixes context. Altogether, 'CimpleG' provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution. For more details see Maié et al. (2023) <doi:10.1186/s13059-023-03000-0>.
Author: Tiago F.V. Maie [aut, cre]
Maintainer: Tiago F.V. Maie <tiagomaie@hotmail.com>

Diff between CimpleG versions 1.0.0 dated 2025-12-03 and 1.0.1 dated 2025-12-06

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Package boutliers updated to version 2.1-3 with previous version 2.1-2 dated 2025-11-25

Title: Outlier Detection and Influence Diagnostics for Meta-Analysis
Description: Computational tools for outlier detection and influence diagnostics in meta-analysis (Noma et al. (2025) <doi:10.1101/2025.09.18.25336125>). Bootstrap distributions of influence statistics are computed, and explicit thresholds for identifying outliers are provided. These methods can also be applied to the analysis of influential centers or regions in multicenter or multiregional clinical trials (Aoki, Noma and Gosho (2021) <doi:10.1080/24709360.2021.1921944>, Nakamura and Noma (2021) <doi:10.5691/jjb.41.117>).
Author: Hisashi Noma [aut, cre], Kazushi Maruo [aut], Masahiko Gosho [aut]
Maintainer: Hisashi Noma <noma@ism.ac.jp>

Diff between boutliers versions 2.1-2 dated 2025-11-25 and 2.1-3 dated 2025-12-06

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Package VWPre updated to version 1.2.5 with previous version 1.2.4 dated 2020-11-29

Title: Tools for Preprocessing Visual World Data
Description: Gaze data from the Visual World Paradigm requires significant preprocessing prior to plotting and analyzing the data. This package provides functions for preparing visual world eye-tracking data for statistical analysis and plotting. It can prepare data for linear analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM). Additionally, it contains various plotting functions for creating grand average and conditional average plots. See the vignette for samples of the functionality. Currently, the functions in this package are designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer. While we would like to add functionality for data collected with other systems in the future, the current package is considered to be feature-complete; further updates will mainly entail maintenance and the addition of minor functionality.
Author: Vincent Porretta [aut, cre], Aki-Juhani Kyroelaeinen [aut], Jacolien van Rij [ctb], Juhani Jaervikivi [ctb]
Maintainer: Vincent Porretta <vincentporretta@gmail.com>

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Package statsExpressions updated to version 1.7.2 with previous version 1.7.1 dated 2025-07-27

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

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Package tall updated to version 0.5.0 with previous version 0.4.0 dated 2025-10-23

Title: Text Analysis for All
Description: An R 'shiny' app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. It is a versatile, general-purpose tool for analyzing textual data. 'tall' features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.
Author: Massimo Aria [aut, cre, cph] , Maria Spano [aut] , Luca D'Aniello [aut] , Corrado Cuccurullo [ctb] , Michelangelo Misuraca [ctb]
Maintainer: Massimo Aria <aria@unina.it>

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Package spocc updated to version 1.2.4 with previous version 1.2.3 dated 2024-03-06

Title: Interface to Species Occurrence Data Sources
Description: A programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility ('GBIF'), 'iNaturalist', 'eBird', Integrated Digitized 'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes functionality for retrieving species occurrence data, and combining those data.
Author: Hannah Owens [aut, cre] , Vijay Barve [aut] , Scott Chamberlain [aut] , Karthik Ram [ctb], Ted Hart [ctb], rOpenSci [fnd]
Maintainer: Hannah Owens <hannah.owens@gmail.com>

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Package testit updated to version 0.14 with previous version 0.13 dated 2021-04-14

Title: A Simple Package for Testing R Packages
Description: Provides two convenience functions assert() and test_pkg() to facilitate testing R packages.
Author: Yihui Xie [aut, cre] , Tomas Kalibera [ctb], Steven Mortimer [ctb]
Maintainer: Yihui Xie <xie@yihui.name>

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Package sugarbag updated to version 0.1.9 with previous version 0.1.6 dated 2022-11-09

Title: Create Tessellated Hexagon Maps
Description: Create a hexagon tile map display from spatial polygons. Each polygon is represented by a hexagon tile, placed as close to it's original centroid as possible, with a focus on maintaining spatial relationship to a focal point. Developed to aid visualisation and analysis of spatial distributions across Australia, which can be challenging due to the concentration of the population on the coast and wide open interior.
Author: Dianne Cook [aut, cre, ths] , Stephanie Kobakian [aut] , Matt Cowgill [ctb]
Maintainer: Dianne Cook <dicook@monash.edu>

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Package rqlm updated to version 4.1-1 with previous version 3.2-1 dated 2025-11-24

Title: Modified Poisson Regression for Binary Outcome and Related Methods
Description: Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090>, Noma (2025)<Forthcoming>, and Cheung (2007) <doi:10.1093/aje/kwm223> have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate alternative accurate confidence intervals.
Author: Hisashi Noma [aut, cre]
Maintainer: Hisashi Noma <noma@ism.ac.jp>

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Package ratematrix updated to version 1.2.5 with previous version 1.2.4 dated 2022-06-03

Title: Bayesian Estimation of the Evolutionary Rate Matrix
Description: The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.
Author: Daniel Caetano [aut, cre], Luke Harmon [aut]
Maintainer: Daniel Caetano <caetanods1@gmail.com>

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Package MMDvariance updated to version 0.1.0 with previous version 0.0.9 dated 2018-07-27

Title: Detecting Differentially Variable Genes Using the Mixture of Marginal Distributions
Description: Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
Author: Xuan Li [aut, ctb], Yuejiao Fu [aut, ctb], Xiaogang Wang [aut, ctb], Dawn L. DeMeo [aut, ctb], Kelan Tantisira [aut, ctb], Scott T. Weiss [aut, ctb], Weiliang Qiu [aut, cre]
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>

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Package kerasnip updated to version 0.1.0 with previous version 0.0.3 dated 2025-09-18

Title: A Bridge Between 'keras' and 'tidymodels'
Description: Provides a seamless bridge between 'keras' and the 'tidymodels' frameworks. It allows for the dynamic creation of 'parsnip' model specifications for 'keras' models.
Author: David Diaz [aut, cre]
Maintainer: David Diaz <daviddrsch@gmail.com>

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Package EGAnet updated to version 2.4.0 with previous version 2.3.0 dated 2025-04-09

Title: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics
Description: Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments.
Author: Hudson Golino [aut, cre] , Alexander Christensen [aut] , Robert Moulder [ctb] , Luis E. Garrido [ctb] , Laura Jamison [ctb] , Dingjing Shi [ctb]
Maintainer: Hudson Golino <hfg9s@virginia.edu>

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Package CytoProfile updated to version 0.2.3 with previous version 0.2.2 dated 2025-09-04

Title: Cytokine Profiling Analysis Tool
Description: Provides comprehensive cytokine profiling analysis through quality control using biologically meaningful cutoffs on raw cytokine measurements and by testing for distributional symmetry to recommend appropriate transformations. Offers exploratory data analysis with summary statistics, enhanced boxplots, and barplots, along with univariate and multivariate analytical capabilities for in-depth cytokine profiling such as Principal Component Analysis based on Andrzej Maćkiewicz and Waldemar Ratajczak (1993) <doi:10.1016/0098-3004(93)90090-R>, Sparse Partial Least Squares Discriminant Analysis based on Lê Cao K-A, Boitard S, and Besse P (2011) <doi:10.1186/1471-2105-12-253>, Random Forest based on Breiman, L. (2001) <doi:10.1023/A:1010933404324>, and Extreme Gradient Boosting based on Tianqi Chen and Carlos Guestrin (2016) <doi:10.1145/2939672.2939785>.
Author: Shubh Saraswat [cre, aut, cph] , Xiaohua Douglas Zhang [aut]
Maintainer: Shubh Saraswat <shubh.saraswat00@gmail.com>

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Package bayesSSM updated to version 0.7.1 with previous version 0.7.0 dated 2025-08-26

Title: Bayesian Methods for State Space Models
Description: Implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.
Author: Bjarke Hautop [aut, cre, cph]
Maintainer: Bjarke Hautop <bjarke.hautop@gmail.com>

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Package odbc updated to version 1.6.4 with previous version 1.6.3 dated 2025-09-05

Title: Connect to ODBC Compatible Databases (using the DBI Interface)
Description: A DBI-compatible interface to ODBC databases.
Author: Jim Hester [aut], Hadley Wickham [aut, cre], Oliver Gjoneski [aut], Simon Couch [aut], lexicalunit [cph] , Google Inc. [cph] , Posit Software, PBC [cph, fnd]
Maintainer: Hadley Wickham <hadley@posit.co>

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More information about odbc at CRAN
Permanent link

Package neotoma2 updated to version 1.0.10 with previous version 1.0.9 dated 2025-11-25

Title: Working with the Neotoma Paleoecology Database
Description: Access and manipulation of data using the Neotoma Paleoecology Database. <https://api.neotomadb.org/api-docs/>.
Author: Dominguez Vidana Socorro [aut, cre] , Simon Goring [aut]
Maintainer: Dominguez Vidana Socorro <dominguezvid@wisc.edu>

Diff between neotoma2 versions 1.0.9 dated 2025-11-25 and 1.0.10 dated 2025-12-06

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More information about neotoma2 at CRAN
Permanent link

Package fastRG updated to version 0.4.0 with previous version 0.3.3 dated 2025-07-24

Title: Sample Generalized Random Dot Product Graphs in Linear Time
Description: Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.
Author: Alex Hayes [aut, cre, cph] , Karl Rohe [aut, cph], Jun Tao [aut], Xintian Han [aut], Norbert Binkiewicz [aut]
Maintainer: Alex Hayes <alexpghayes@gmail.com>

Diff between fastRG versions 0.3.3 dated 2025-07-24 and 0.4.0 dated 2025-12-06

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Permanent link

Package rswipl updated to version 10.0.0 with previous version 9.3.21 dated 2025-03-21

Title: Embed 'SWI'-'Prolog'
Description: Interface to 'SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.
Author: Matthias Gondan [aut, com, cre] , Jan Wielemaker [ctb, cph] , European Commission [fnd]
Maintainer: Matthias Gondan <Matthias.Gondan-Rochon@uibk.ac.at>

Diff between rswipl versions 9.3.21 dated 2025-03-21 and 10.0.0 dated 2025-12-06

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Permanent link

Package GitStats updated to version 2.3.8 with previous version 2.3.7 dated 2025-10-16

Title: Standardized Git Repository Data
Description: Obtain standardized data from multiple 'Git' services, including 'GitHub' and 'GitLab'. Designed to be 'Git' service-agnostic, this package assists teams with activities spread across various 'Git' platforms by providing a unified way to access repository data.
Author: Maciej Banas [aut, cre], Kamil Koziej [aut], Karolina Marcinkowska [aut], Matt Secrest [aut]
Maintainer: Maciej Banas <banasmaciek@gmail.com>

Diff between GitStats versions 2.3.7 dated 2025-10-16 and 2.3.8 dated 2025-12-06

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Package flevr updated to version 0.0.5 with previous version 0.0.4 dated 2023-11-30

Title: Flexible, Ensemble-Based Variable Selection with Potentially Missing Data
Description: Perform variable selection in settings with possibly missing data based on extrinsic (algorithm-specific) and intrinsic (population-level) variable importance. Uses a Super Learner ensemble to estimate the underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2024) <doi:10.1515/ijb-2023-0059>.
Author: Brian D. Williamson [aut, cre]
Maintainer: Brian D. Williamson <brian.d.williamson@kp.org>

Diff between flevr versions 0.0.4 dated 2023-11-30 and 0.0.5 dated 2025-12-06

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More information about flevr at CRAN
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Package feather updated to version 0.4.0 with previous version 0.3.5 dated 2019-09-14

Title: R Bindings to the Feather 'API'
Description: Read and write feather files, a lightweight binary columnar data store designed for maximum speed.
Author: Hadley Wickham [aut, cre], RStudio [cph], LevelDB Authors [ctb]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between feather versions 0.3.5 dated 2019-09-14 and 0.4.0 dated 2025-12-06

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Package rsconnect updated to version 1.7.0 with previous version 1.6.2 dated 2025-11-18

Title: Deploy Docs, Apps, and APIs to 'Posit Connect', 'shinyapps.io', and 'RPubs'
Description: Programmatic deployment interface for 'RPubs', 'shinyapps.io', and 'Posit Connect'. Supported content types include R Markdown documents, Shiny applications, Plumber APIs, plots, and static web content.
Author: Aron Atkins [aut, cre], Toph Allen [aut], Hadley Wickham [aut], Jonathan McPherson [aut], JJ Allaire [aut], Posit Software, PBC [cph, fnd]
Maintainer: Aron Atkins <aron@posit.co>

Diff between rsconnect versions 1.6.2 dated 2025-11-18 and 1.7.0 dated 2025-12-06

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Permanent link

Package quickr updated to version 0.2.1 with previous version 0.2.0 dated 2025-08-25

Title: Compiler for R
Description: Compile R functions annotated with type and shape declarations for extremely fast performance and robust runtime type checking. Supports both just-in-time (JIT) and ahead-of-time (AOT) compilation. Compilation is performed by lowering R code to Fortran.
Author: Tomasz Kalinowski [aut, cre], Posit Software, PBC [cph, fnd]
Maintainer: Tomasz Kalinowski <tomasz@posit.co>

Diff between quickr versions 0.2.0 dated 2025-08-25 and 0.2.1 dated 2025-12-06

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More information about quickr at CRAN
Permanent link

Package projpred updated to version 2.10.0 with previous version 2.9.1 dated 2025-10-28

Title: Projection Predictive Feature Selection
Description: Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2025, <doi:10.1007/s00180-024-01506-0>), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, <doi:10.48550/arXiv.2109.04702>, which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples.
Author: Juho Piironen [aut], Markus Paasiniemi [aut], Alejandro Catalina [aut], Frank Weber [aut], Osvaldo Martin [cre, aut], Aki Vehtari [aut], Jonah Gabry [ctb], Marco Colombo [ctb], Paul-Christian Buerkner [ctb], Hamada S. Badr [ctb], Brian Sullivan [ctb], [...truncated...]
Maintainer: Osvaldo Martin <aloctavodia@gmail.com>

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More information about projpred at CRAN
Permanent link

Package MolgenisArmadillo updated to version 2.9.3 with previous version 2.9.1 dated 2025-06-13

Title: Armadillo Client for the Armadillo Service
Description: A set of functions to manage data shared on a 'MOLGENIS Armadillo' server.
Author: Mariska Slofstra [aut, cre] , Sido Haakma [aut] , Tommy de Boer [aut] , Fleur Kelpin [aut] , Morris Swertz [aut] , Tim Cadman [aut]
Maintainer: Mariska Slofstra <m.k.slofstra@umcg.nl>

Diff between MolgenisArmadillo versions 2.9.1 dated 2025-06-13 and 2.9.3 dated 2025-12-06

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More information about MolgenisArmadillo at CRAN
Permanent link

Package LFM updated to version 0.3.3 with previous version 0.3.2 dated 2025-10-10

Title: Laplace Factor Model Analysis and Evaluation
Description: Enables the generation of Laplace factor models across diverse Laplace distributions and facilitates the application of Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods and Farm Test methods to these models. Evaluates the efficacy of these methods within the context of Laplace factor models by scrutinizing parameter estimation accuracy, mean square error, and the degree of sparsity.
Author: Guangbao Guo [aut, cre], Siqi Liu [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>

Diff between LFM versions 0.3.2 dated 2025-10-10 and 0.3.3 dated 2025-12-06

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More information about LFM at CRAN
Permanent link

Package IPEDSuploadables updated to version 3.0.0 with previous version 2.11.0 dated 2025-09-09

Title: Transforms Institutional Data into Text Files for IPEDS Automated Import/Upload
Description: Starting from user-supplied institutional data, these scripts transform, aggregate, and reshape the information to produce key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System) through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes. Final files are saved locally to a location of the user's choice. User-friendly readable files can also be produced for purposes of data review and validation.
Author: Alison Lanski [aut, cre], Shiloh Fling [aut], Edwin Welch [aut]
Maintainer: Alison Lanski <alanski@nd.edu>

Diff between IPEDSuploadables versions 2.11.0 dated 2025-09-09 and 3.0.0 dated 2025-12-06

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Permanent link

Package insight updated to version 1.4.4 with previous version 1.4.3 dated 2025-11-24

Title: Easy Access to Model Information for Various Model Objects
Description: A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.
Author: Daniel Luedecke [aut, cre] , Dominique Makowski [aut, ctb] , Indrajeet Patil [aut, ctb] , Philip Waggoner [aut, ctb] , Mattan S. Ben-Shachar [aut, ctb] , Brenton M. Wiernik [aut, ctb] , Vincent Arel-Bundock [aut, ctb] , Etienne Bacher [aut, ctb] , Ale [...truncated...]
Maintainer: Daniel Luedecke <officialeasystats@gmail.com>

Diff between insight versions 1.4.3 dated 2025-11-24 and 1.4.4 dated 2025-12-06

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More information about insight at CRAN
Permanent link

Package crane updated to version 0.3.0 with previous version 0.2.0 dated 2025-08-29

Title: Supplements the 'gtsummary' Package for Pharmaceutical Reporting
Description: Tables summarizing clinical trial results are often complex and require detailed tailoring prior to submission to a health authority. The 'crane' package supplements the functionality of the 'gtsummary' package for creating these often highly bespoke tables in the pharmaceutical industry.
Author: Daniel D. Sjoberg [aut, cre] , Emily de la Rua [aut] , Davide Garolini [aut], Abinaya Yogasekaram [aut], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

Diff between crane versions 0.2.0 dated 2025-08-29 and 0.3.0 dated 2025-12-06

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More information about crane at CRAN
Permanent link

Package blockr.core updated to version 0.1.1 with previous version 0.1.0 dated 2025-05-20

Title: Graphical Web-Framework for Data Manipulation and Visualization
Description: A framework for data manipulation and visualization using a web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks.
Author: Nicolas Bennett [aut, cre], David Granjon [aut], Christoph Sax [aut], Karma Tarap [ctb], John Coene [ctb], Bristol Myers Squibb [fnd]
Maintainer: Nicolas Bennett <nicolas@cynkra.com>

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Package biostats updated to version 1.1.0 with previous version 1.0.0 dated 2025-11-13

Title: Biostatistics and Clinical Data Analysis
Description: Biostatistical and clinical data analysis, including descriptive statistics, exploratory data analysis, sample size and power calculations, statistical inference, and data visualization. Normality tests are implemented following Mishra et al. (2019) <doi:10.4103/aca.ACA_157_18>, omnibus test procedures are based on Blanca et al. (2017) <doi:10.3758/s13428-017-0918-2> and Field et al. (2012, ISBN:9781446200469), while sample size and power calculation methods follow Chow et al. (2017) <doi:10.1201/9781315183084>.
Author: Sebastian Quirarte-Justo [aut, cre] , Angela Carolina Montano-Ruiz [aut] , Jose Maria Torres-Arellano [aut] , Laboratorios Sophia S.A. de C.V. [cph, fnd]
Maintainer: Sebastian Quirarte-Justo <sebastian.quirarte@sophia.com.mx>

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Package ALFAM2 updated to version 4.2.14 with previous version 4.2 dated 2024-11-08

Title: Dynamic Model of Ammonia Emission from Field-Applied Manure
Description: An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. More information is available via citation("ALFAM2").
Author: Sasha D. Hafner [aut, cre] , Christoph Haeni [aut] , Roland Fuss [aut] , Frederik Dalby [aut] , Johanna Pedersen [ctb] , Valdemar Petersen [ctb]
Maintainer: Sasha D. Hafner <sasha.hafner@bce.au.dk>

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Package splineCox updated to version 0.0.7 with previous version 0.0.5 dated 2025-07-10

Title: A Two-Stage Estimation Approach to Cox Regression Using M-Spline Function
Description: Implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters.
Author: Ren Teranishi [aut, cre]
Maintainer: Ren Teranishi <ren.teranishi1227@gmail.com>

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Package ronfig updated to version 0.0.7 with previous version 0.0.6 dated 2025-11-21

Title: Load Configuration Values
Description: A simple approach to configuring R projects with different parameter values. Configurations are specified using a reduced subset of base R and parsed accordingly.
Author: Tim Taylor [aut, cre, cph]
Maintainer: Tim Taylor <tim.taylor@hiddenelephants.co.uk>

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Package grec updated to version 1.6.3 with previous version 1.6.2 dated 2025-09-04

Title: Gradient-Based Recognition of Spatial Patterns in Environmental Data
Description: Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
Author: Wencheng Lau-Medrano [aut, cre]
Maintainer: Wencheng Lau-Medrano <luis.laum@gmail.com>

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Package ggdibbler updated to version 0.6.1 with previous version 0.1.0 dated 2025-07-31

Title: Add Uncertainty to Data Visualisations
Description: A 'ggplot2' extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas 'ggdibbler' differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace any of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart.
Author: Harriet Mason [aut, cre] , Dianne Cook [aut, ths] , Sarah Goodwin [aut, ths] , Susan VanderPlas [aut, ths]
Maintainer: Harriet Mason <harriet.m.mason@gmail.com>

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Package DLFM updated to version 0.1.4 with previous version 0.1.1 dated 2025-11-14

Title: Distributed Laplace Factor Model
Description: Distributed estimation method is based on a Laplace factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.
Author: Guangbao Guo [aut, cre], Siqi Liu [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>

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Package StratPal updated to version 0.7.1 with previous version 0.7.0 dated 2025-11-24

Title: Stratigraphic Paleobiology Modeling Pipelines
Description: The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.
Author: Niklas Hohmann [aut, cre]
Maintainer: Niklas Hohmann <N.H.Hohmann@uu.nl>

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Package shinydrive updated to version 0.1.5 with previous version 0.1.3 dated 2022-09-29

Title: File Sharing Shiny Module
Description: Shiny module for easily sharing files between users. Admin can add, remove, edit and download file. User can only download file. It's also possible to manage files using R functions directly.
Author: Benoit Thieurmel [aut, cre], Thibaut Dubois [aut], Jeremy Boussaguet [ctb]
Maintainer: Benoit Thieurmel <bthieurmel@gmail.com>

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Package semlbci updated to version 0.11.5 with previous version 0.11.3 dated 2025-01-25

Title: Likelihood-Based Confidence Interval in Structural Equation Models
Description: Forms likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling, introduced in Cheung and Pesigan (2023) <doi:10.1080/10705511.2023.2183860>. Currently implements the algorithm illustrated by Pek and Wu (2018) <doi:10.1037/met0000163>, and supports the robust LBCI proposed by Falk (2018) <doi:10.1080/10705511.2017.1367254>.
Author: Shu Fai Cheung [aut, cre] , Ivan Jacob Agaloos Pesigan [ctb]
Maintainer: Shu Fai Cheung <shufai.cheung@gmail.com>

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 190 files changed, 1015 insertions(+), 622 deletions(-)

More information about semlbci at CRAN
Permanent link

Package DSMolgenisArmadillo updated to version 3.0.2 with previous version 3.0.1 dated 2025-09-16

Title: 'DataSHIELD' Client for 'MOLGENIS Armadillo'
Description: 'DataSHIELD' is an infrastructure and series of R packages that enables the remote and 'non-disclosive' analysis of sensitive research data. This package is the 'DataSHIELD' interface implementation to analyze data shared on a 'MOLGENIS Armadillo' server. 'MOLGENIS Armadillo' is a light-weight 'DataSHIELD' server using a file store and an 'RServe' server.
Author: Mariska Slofstra [aut, cre] , Sido Haakma [aut] , Tommy de Boer [aut] , Fleur Kelpin [aut] , MOLGENIS org [cph, fnd]
Maintainer: Mariska Slofstra <m.k.slofstra@umcg.nl>

Diff between DSMolgenisArmadillo versions 3.0.1 dated 2025-09-16 and 3.0.2 dated 2025-12-06

 DESCRIPTION                               |   12 
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 tests/testthat/test-ArmadilloResult.R     |  160 +++++----
 tests/testthat/test-utils.R               |  229 ++++++++-----
 14 files changed, 1052 insertions(+), 897 deletions(-)

More information about DSMolgenisArmadillo at CRAN
Permanent link

Package dockViewR updated to version 0.3.0 with previous version 0.2.0 dated 2025-07-10

Title: Layout Manager Widget for R and 'shiny' Apps
Description: Provides R bindings to the 'dockview' 'JavaScript' library <https://dockview.dev/>. Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or 'Quarto' or in 'shiny' apps <https://shiny.posit.co/>. In 'shiny' mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later.
Author: David Granjon [aut, cre], Nelson Stevens [aut], Nicolas Bennett [aut], mathuo [cph], cynkra GmbH [fnd]
Maintainer: David Granjon <dgranjon@ymail.com>

Diff between dockViewR versions 0.2.0 dated 2025-07-10 and 0.3.0 dated 2025-12-06

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Package disclapmix updated to version 1.7.5 with previous version 1.7.4 dated 2022-06-29

Title: Discrete Laplace Mixture Inference using the EM Algorithm
Description: Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
Author: Mikkel Meyer Andersen [aut, cre], Poul Svante Eriksen [aut]
Maintainer: Mikkel Meyer Andersen <mikl@math.aau.dk>

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Package Countr updated to version 3.6 with previous version 3.5.8 dated 2024-01-14

Title: Flexible Univariate Count Models Based on Renewal Processes
Description: Flexible univariate count models based on renewal processes. The models may include covariates and can be specified with familiar formula syntax as in glm() and package 'flexsurv'. The methodology is described by Kharrat et all (2019) <doi:10.18637/jss.v090.i13> (included as vignette 'Countr_guide' in the package).
Author: Tarak Kharrat [aut] , Georgi N. Boshnakov [aut, cre]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package clustcurv updated to version 3.0.1 with previous version 3.0.0 dated 2025-11-14

Title: Determining Groups in Multiples Curves
Description: A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods or other approaches with lower computational cost. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.
Author: Nora M. Villanueva [aut, cre] , Marta Sestelo [aut]
Maintainer: Nora M. Villanueva <nmvillanueva@uvigo.es>

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Fri, 05 Dec 2025

Package vcr updated to version 2.1.0 with previous version 2.0.0 dated 2025-07-23

Title: Record 'HTTP' Calls to Disk
Description: Record test suite 'HTTP' requests and replays them during future runs. A port of the Ruby gem of the same name (<https://github.com/vcr/vcr/>). Works by recording real 'HTTP' requests/responses on disk in 'cassettes', and then replaying matching responses on subsequent requests.
Author: Scott Chamberlain [aut, cre] , Aaron Wolen [aut] , Maelle Salmon [aut] , Daniel Possenriede [aut] , Hadley Wickham [aut], rOpenSci [fnd]
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package TFORGE updated to version 0.1.15 with previous version 0.1.14 dated 2025-12-04

Title: Tests for Geophysical Eigenvalues
Description: The eigenvalues of observed symmetric matrices are often of intense scientific interest. This package offers single sample tests for the eigenvalues of the population mean or the eigenvalue-multiplicity of the population mean. For k-samples, this package offers tests for equal eigenvalues between samples. Included is support for matrices with constraints common to geophysical tensors (constant trace, sum of squared eigenvalues, or both) and eigenvectors are usually considered nuisance parameters. Pivotal bootstrap methods enable these tests to have good performance for small samples (n=15 for 3x3 matrices). These methods were developed and studied by Hingee, Scealy and Wood (2026, "Nonparametric bootstrap inference for the eigenvalues of geophysical tensors", accepted by the Journal of American Statistical Association). Also available is a 2-sample test using a Gaussian orthogonal ensemble approximation and an eigenvalue-multiplicity test that assumes orthogonally-invariant covariance.
Author: Kassel Liam Hingee [aut, cre] , Art B. Owen [cph] , Board of Trustees Leland Stanford Junior University [cph]
Maintainer: Kassel Liam Hingee <kassel.hingee@anu.edu.au>

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Package PublicationBiasBenchmark updated to version 0.1.3 with previous version 0.1.2 dated 2025-11-26

Title: Benchmark for Publication Bias Correction Methods
Description: Implements a unified interface for benchmarking meta-analytic publication bias correction methods through simulation studies (see Bartoš et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides 1) predefined data-generating mechanisms from the literature, 2) functions for running meta-analytic methods on simulated data, 3) pre-simulated datasets and pre-computed results for reproducible benchmarks, 4) tools for visualizing and comparing method performance.
Author: Frantisek Bartos [aut, cre] , Samuel Pawel [aut] , Bjoern S. Siepe [aut]
Maintainer: Frantisek Bartos <f.bartos96@gmail.com>

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Package gtsummary updated to version 2.5.0 with previous version 2.4.0 dated 2025-08-28

Title: Presentation-Ready Data Summary and Analytic Result Tables
Description: Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.
Author: Daniel D. Sjoberg [aut, cre] , Joseph Larmarange [aut] , Michael Curry [aut] , Emily de la Rua [aut] , Jessica Lavery [aut] , Karissa Whiting [aut] , Emily C. Zabor [aut] , Xing Bai [ctb], Malcolm Barrett [ctb] , Esther Drill [ctb] , Jessica Flynn [c [...truncated...]
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

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Package GRAB updated to version 0.2.4 with previous version 0.2.3 dated 2025-08-19

Title: Genome-Wide Robust Analysis for Biobank Data (GRAB)
Description: Provides a comprehensive suite of genome-wide association study (GWAS) methods specifically designed for biobank-scale data, including but not limited to, robust approaches for time-to-event traits (Li et al., 2025 <doi:10.1038/s43588-025-00864-z>) and ordinal categorical traits (Bi et al., 2021 <doi:10.1016/j.ajhg.2021.03.019>). The package also offers general frameworks for GWAS of any trait type (Bi et al., 2020 <doi:10.1016/j.ajhg.2020.06.003>), while accounting for sample relatedness (Xu et al., 2025 <doi:10.1038/s41467-025-56669-1>) or population structure (Ma et al., 2025 <doi:10.1186/s13059-025-03827-9>). By accurately approximating score statistic distributions using saddlepoint approximation (SPA), these methods can effectively control type I error rates for rare variants and in the presence of unbalanced phenotype distributions. Additionally, the package includes functions for simulating genotype and phenotype data to support research and method [...truncated...]
Author: Wenjian Bi [aut], Wei Zhou [aut], Rounak Dey [aut], Zhangchen Zhao [aut], Seunggeun Lee [aut], Woody Miao [cre]
Maintainer: Woody Miao <miaolin@pku.edu.cn>

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Package filibustr readmission to version 0.5.1 with previous version 0.5.0 dated 2025-10-26

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

This is a re-admission after prior archival of version 0.5.0 dated 2025-10-26

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Package excessmort updated to version 0.8.2 with previous version 0.8.1 dated 2025-04-07

Title: Excess Mortality
Description: Implementation of method for estimating excess mortality and other health related outcomes from weekly or daily count data described in Acosta and Irizarry (2021) "A Flexible Statistical Framework for Estimating Excess Mortality".
Author: Rafael A. Irizarry [aut, cre], Rolando Acosta [aut], Monica Robles-Fontan [aut], Ferenci Tamas [ctb]
Maintainer: Rafael A. Irizarry <rafael_irizarry@dfci.harvard.edu>

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Package CopernicusClimate updated to version 0.0.4 with previous version 0.0.3 dated 2025-10-23

Title: Search Download and Handle Data from Copernicus Climate Data Service
Description: Subset and download data from EU Copernicus Climate Data Service: <https://cds.climate.copernicus.eu/>. Import information about the Earth's past, present and future climate from Copernicus into R without the need of external software.
Author: Pepijn de Vries [aut, cre]
Maintainer: Pepijn de Vries <pepijn.devries@outlook.com>

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Package nhdplusTools updated to version 1.4.1 with previous version 1.4.0 dated 2025-11-19

Title: NHDPlus Tools
Description: Tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'nhdplusTools' are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>.
Author: David Blodgett [aut, cre] , Mike Johnson [aut] , Marc Weber [ctb] , Josh Erickson [ctb], Lauren Koenig [ctb]
Maintainer: David Blodgett <dblodgett@usgs.gov>

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Package LMMsolver updated to version 1.0.12 with previous version 1.0.11 dated 2025-08-20

Title: Linear Mixed Models with Sparse Matrix Methods and Smoothing
Description: Provides tools for fitting linear mixed models using sparse matrix methods and variance component estimation. Applications include spline-based modeling of spatial and temporal trends using penalized splines (Boer, 2023) <doi:10.1177/1471082X231178591>.
Author: Martin Boer [aut] , Bart-Jan van Rossum [aut, cre]
Maintainer: Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>

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Package DNAtools updated to version 0.2-5 with previous version 0.2-4 dated 2022-03-17

Title: Tools for Analysing Forensic Genetic DNA Data
Description: Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results. See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. Compute the distribution of the numbers of alleles in DNA mixtures. See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.
Author: Torben Tvedebrink [aut], James Curran [aut], Mikkel Meyer Andersen [aut, cre]
Maintainer: Mikkel Meyer Andersen <mikl@math.aau.dk>

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Package memshare updated to version 1.1.0 with previous version 1.0.3 dated 2025-11-08

Title: Shared Memory Multithreading
Description: This project extends 'R' with a mechanism for efficient parallel data access by utilizing 'C++' shared memory. Large data objects can be accessed and manipulated directly from 'R' without redundant copying, providing both speed and memory efficiency.
Author: Julian Maerte [aut, ctr] , Romain Francois [ctb], Michael Thrun [aut, ths, rev, cph, cre]
Maintainer: Michael Thrun <m.thrun@gmx.net>

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Package visor updated to version 0.1.1 with previous version 0.1.0 dated 2025-04-07

Title: Geospatial Tools for Visibility Analysis
Description: Provides tools for visibility analysis in geospatial data. It offers functionality to perform isovist calculations, using arbitrary geometries as both viewpoints and occluders.
Author: Claudiu Forgaci [aut, cre, cph] , Francesco Nattino [aut] , Netherlands eScience Center [fnd]
Maintainer: Claudiu Forgaci <c.forgaci@tudelft.nl>

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Package terrainmeshr updated to version 1.0.1 with previous version 0.1.0 dated 2020-04-29

Title: Triangulate and Simplify 3D Terrain Meshes
Description: Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the 'hmm' library written by Michael Fogleman <https://github.com/fogleman/hmm>.
Author: Tyler Morgan-Wall [aut, cph, cre] , Michael Fogleman [ctb, cph]
Maintainer: Tyler Morgan-Wall <tylermw@gmail.com>

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Package supernova updated to version 3.0.1 with previous version 3.0.0 dated 2024-02-07

Title: Judd, McClelland, & Ryan Formatting for ANOVA Output
Description: Produces ANOVA tables in the format used by Judd, McClelland, and Ryan (2017, ISBN: 978-1138819832) in their introductory textbook, Data Analysis. This includes proportional reduction in error and formatting to improve ease the transition between the book and R.
Author: Adam Blake [cre, aut] , Jeff Chrabaszcz [aut], Ji Son [aut] , Jim Stigler [aut]
Maintainer: Adam Blake <adam@coursekata.org>

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 inst/WORDLIST                                                     |    3 
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Package highlight updated to version 0.5.2 with previous version 0.5.1 dated 2023-01-18

Title: Syntax Highlighter
Description: Syntax highlighter for R code based on the results of the R parser. Rendering in HTML and latex markup. Custom Sweave driver performing syntax highlighting of R code chunks.
Author: Hadley Wickham [cre], Romain Francois [aut], Andre Simon [ctb]
Maintainer: Hadley Wickham <hadley@rstudio.com>

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Package hce updated to version 0.8.8 with previous version 0.8.5 dated 2025-08-22

Title: Design and Analysis of Hierarchical Composite Endpoints
Description: Simulate and analyze hierarchical composite endpoints. Includes implementation for the kidney hierarchical composite endpoint as defined in Heerspink HL et al (2023) “Development and validation of a new hierarchical composite end point for clinical trials of kidney disease progression” (Journal of the American Society of Nephrology 34 (2): 2025–2038, <doi:10.1681/ASN.0000000000000243>). Win odds, also called Wilcoxon-Mann-Whitney or success odds, is the main analysis method. Other win statistics (win probability, win ratio, net benefit) are also implemented in the univariate case, provided there is no censoring. The win probability analysis is based on the Brunner-Munzel test and uses the DeLong-DeLong-Clarke-Pearson variance estimator, as described by Brunner and Konietschke (2025) in “An unbiased rank-based estimator of the Mann–Whitney variance including the case of ties” (Statistical Papers 66 (1): 20, <doi:10.1007/s00362-024-01635-0>). Includes implementation of a new [...truncated...]
Author: Samvel B. Gasparyan [aut, cre]
Maintainer: Samvel B. Gasparyan <gasparyan.co@gmail.com>

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Package bookdown updated to version 0.46 with previous version 0.45 dated 2025-10-03

Title: Authoring Books and Technical Documents with R Markdown
Description: Output formats and utilities for authoring books and technical documents with R Markdown.
Author: Yihui Xie [aut, cre] , Christophe Dervieux [ctb] , JJ Allaire [ctb], Albert Kim [ctb], Alessandro Samuel-Rosa [ctb], Andrzej Oles [ctb], Atsushi Yasumoto [ctb] , Aust Frederik [ctb] , Bastiaan Quast [ctb], Ben Marwick [ctb], Chester Ismay [ctb], Clif [...truncated...]
Maintainer: Yihui Xie <xie@yihui.name>

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Package funtimes updated to version 10.0 with previous version 9.1 dated 2023-03-21

Title: Functions for Time Series Analysis
Description: Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.
Author: Vyacheslav Lyubchich [aut, cre] , Yulia R. Gel [aut], Alexander Brenning [ctb], Calvin Chu [ctb], Xin Huang [ctb], Umar Islambekov [ctb], Palina Niamkova [ctb], Dorcas Ofori-Boateng [ctb], Ethan D. Schaeffer [ctb], Srishti Vishwakarma [aut], Xingyu W [...truncated...]
Maintainer: Vyacheslav Lyubchich <lyubchich@umces.edu>

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Thu, 04 Dec 2025

Package rankdifferencetest updated to version 2025.12.4 with previous version 2025.4.21 dated 2025-04-22

Title: Kornbrot's Rank Difference Test
Description: Implements Kornbrot's rank difference test as described in <doi:10.1111/j.2044-8317.1990.tb00939.x>. This method is a modified Wilcoxon signed-rank test which produces consistent and meaningful results for ordinal or monotonically-transformed data.
Author: Brett Klamer [aut, cre]
Maintainer: Brett Klamer <code@brettklamer.com>

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Package GCCfactor updated to version 1.1.4 with previous version 1.1.2 dated 2025-11-28

Title: GCC Estimation of the Multilevel Factor Model
Description: Provides methods for model selection, estimation, inference, and simulation for the multilevel factor model, based on the principal component estimation and generalised canonical correlation approach. Details can be found in "Generalised Canonical Correlation Estimation of the Multilevel Factor Model." Lin and Shin (2025) <doi:10.2139/ssrn.4295429>.
Author: Rui Lin [aut, cre], Yongcheol Shin [aut]
Maintainer: Rui Lin <ruilin1081@gmail.com>

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Package Rwofost updated to version 0.8-6 with previous version 0.8-3 dated 2021-10-01

Title: WOFOST Crop Growth Simulation Model
Description: An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.
Author: Robert J. Hijmans [cre, aut], Huang Fang [ctb], C.A. van Diepen [ctb], Allard de Wit [ctb], Daniel van Kraalingen [ctb], Tamme van der Wal [ctb], C. Rappoldt [ctb], Hendrik Boogard [ctb], I.G.A.M. Noy [ctb], Alterra, Wageningen-UR [cph]
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

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Package armspp updated to version 0.0.3 with previous version 0.0.2 dated 2019-05-24

Title: Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp'
Description: An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.
Author: Michael Bertolacci [aut, cre]
Maintainer: Michael Bertolacci <m.bertolacci@gmail.com>

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Package xrf updated to version 0.3.0 with previous version 0.2.2 dated 2022-10-04

Title: eXtreme RuleFit
Description: An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result is a model that learns similarly to a tree ensemble, while often offering improved interpretability and achieving improved scoring runtime in live applications. Several algorithms for reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to several million rows and support sparse representations to handle tens of thousands of dimensions.
Author: Karl Holub [aut, cre]
Maintainer: Karl Holub <karljholub@gmail.com>

Diff between xrf versions 0.2.2 dated 2022-10-04 and 0.3.0 dated 2025-12-04

 DESCRIPTION                      |   39 +-
 MD5                              |   26 -
 NAMESPACE                        |    1 
 R/glmnot.R                       |  118 ++++---
 R/hyperrectangle_overlap.R       |  350 +++++++++++++--------
 R/utils.R                        |   49 ++-
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 README.md                        |   19 -
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 tests/testthat.R                 |    8 
 tests/testthat/helpers.R         |only
 tests/testthat/test_deoverlap.R  |   69 ++--
 tests/testthat/test_model.R      |  159 +++++----
 tests/testthat/test_xgb_1_7_11.R |only
 15 files changed, 941 insertions(+), 528 deletions(-)

More information about xrf at CRAN
Permanent link

Package mlsurvlrnrs updated to version 0.0.7 with previous version 0.0.6 dated 2025-09-09

Title: R6-Based ML Survival Learners for 'mlexperiments'
Description: Enhances 'mlexperiments' <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners for survival analysis. The package provides R6-based survival learners for the following algorithms: 'glmnet' <https://CRAN.R-project.org/package=glmnet>, 'ranger' <https://CRAN.R-project.org/package=ranger>, 'xgboost' <https://CRAN.R-project.org/package=xgboost>, and 'rpart' <https://CRAN.R-project.org/package=rpart>. These can be used directly with the 'mlexperiments' R package.
Author: Lorenz A. Kapsner [cre, aut, cph]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner@gmail.com>

Diff between mlsurvlrnrs versions 0.0.6 dated 2025-09-09 and 0.0.7 dated 2025-12-04

 DESCRIPTION                                    |   13 
 MD5                                            |   84 +-
 NAMESPACE                                      |   24 
 R/learner_surv_coxph_cox.R                     |  316 +++----
 R/learner_surv_glmnet_cox.R                    |  526 ++++++------
 R/learner_surv_ranger_cox.R                    |  587 +++++++-------
 R/learner_surv_xgboost_aft.R                   |  514 ++++++------
 R/learner_surv_xgboost_cox.R                   |  335 ++++----
 R/surv_c_index.R                               |   54 -
 R/zzz.R                                        |   50 -
 build/vignette.rds                             |binary
 inst/doc/mlsurvlrnrs_glmnet_survival.html      |  180 +++-
 inst/doc/mlsurvlrnrs_glmnet_survival.qmd       | 1022 ++++++++++++-------------
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 inst/doc/mlsurvlrnrs_rpart_survival.html       |  180 +++-
 inst/doc/mlsurvlrnrs_rpart_survival.qmd        |  868 ++++++++++-----------
 inst/doc/mlsurvlrnrs_xgboost_survival_aft.R    |  139 +--
 inst/doc/mlsurvlrnrs_xgboost_survival_aft.html |  377 ++++++---
 inst/doc/mlsurvlrnrs_xgboost_survival_aft.qmd  |  933 +++++++++++-----------
 inst/doc/mlsurvlrnrs_xgboost_survival_cox.R    |  150 ++-
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 inst/doc/mlsurvlrnrs_xgboost_survival_cox.qmd  |  938 +++++++++++-----------
 man/LearnerSurvCoxPHCox.Rd                     |  284 +++---
 man/LearnerSurvGlmnetCox.Rd                    |  308 +++----
 man/LearnerSurvRangerCox.Rd                    |  314 +++----
 man/LearnerSurvRpartCox.Rd                     |  342 ++++----
 man/LearnerSurvXgboostAft.Rd                   |  356 ++++----
 man/LearnerSurvXgboostCox.Rd                   |  348 ++++----
 man/c_index.Rd                                 |   66 -
 tests/testthat.R                               |   30 
 tests/testthat/test-lints.R                    |   26 
 tests/testthat/test-surv_glmnet_cox.R          |  214 ++---
 tests/testthat/test-surv_ranger_cox.R          |  236 ++---
 tests/testthat/test-surv_rpart_cox.R           |  230 ++---
 tests/testthat/test-surv_xgboost_aft.R         |  295 ++++---
 tests/testthat/test-surv_xgboost_cox.R         |  296 ++++---
 vignettes/mlsurvlrnrs_glmnet_survival.qmd      | 1022 ++++++++++++-------------
 vignettes/mlsurvlrnrs_ranger_survival.qmd      |  907 +++++++++++-----------
 vignettes/mlsurvlrnrs_rpart_survival.qmd       |  868 ++++++++++-----------
 vignettes/mlsurvlrnrs_xgboost_survival_aft.qmd |  933 +++++++++++-----------
 vignettes/mlsurvlrnrs_xgboost_survival_cox.qmd |  938 +++++++++++-----------
 43 files changed, 8849 insertions(+), 7981 deletions(-)

More information about mlsurvlrnrs at CRAN
Permanent link

Package ambiorix updated to version 2.2.2 with previous version 2.2.1 dated 2025-07-29

Title: Web Framework Inspired by 'Express.js'
Description: A web framework inspired by 'express.js' to build any web service from multi-page websites to 'RESTful' application programming interfaces.
Author: John Coene [aut] , Opifex [fnd], Kennedy Mwavu [cre] , Julio Collazos [ctb]
Maintainer: Kennedy Mwavu <mwavukennedy@gmail.com>

Diff between ambiorix versions 2.2.1 dated 2025-07-29 and 2.2.2 dated 2025-12-04

 DESCRIPTION                     |   18 ++---
 MD5                             |   29 ++++----
 NEWS.md                         |   17 ++++
 R/ambiorix.R                    |   15 +++-
 R/assertions.R                  |   12 +++
 R/parser.R                      |    3 
 R/render.R                      |    2 
 R/response.R                    |   21 +++++-
 R/route.R                       |    3 
 R/routing.R                     |  138 +++++++++++++++++++++++++++++++++++-----
 man/Ambiorix.Rd                 |    2 
 man/Router.Rd                   |    2 
 man/Routing.Rd                  |  104 ++++++++++++++++++++++++++++++
 tests/testthat/test-param.R     |only
 tests/testthat/test-parser.R    |   77 +++++++++++++++++-----
 tests/testthat/test-serialise.R |   48 +++++++++++++
 16 files changed, 427 insertions(+), 64 deletions(-)

More information about ambiorix at CRAN
Permanent link

Package armadillo4r updated to version 0.7.0 with previous version 0.6.0 dated 2025-11-03

Title: An 'Armadillo' Interface
Description: Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. This implementation is derived from Vargas Sepulveda and Schneider Malamud (2024) <doi:10.1016/j.softx.2025.102087>.
Author: Mauricio Vargas Sepulveda [aut, cre] , Jonathan Schneider Malamud [ctb], Conrad Sanderson [aut] )
Maintainer: Mauricio Vargas Sepulveda <m.vargas.sepulveda@gmail.com>

Diff between armadillo4r versions 0.6.0 dated 2025-11-03 and 0.7.0 dated 2025-12-04

 armadillo4r-0.6.0/armadillo4r/inst/extdata/src/00_main.h                                                  |only
 armadillo4r-0.6.0/armadillo4r/inst/extdata/src/01_ols.cpp                                                 |only
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 armadillo4r-0.7.0/armadillo4r/DESCRIPTION                                                                 |    6 
 armadillo4r-0.7.0/armadillo4r/MD5                                                                         | 1318 -
 armadillo4r-0.7.0/armadillo4r/NEWS.md                                                                     |    7 
 armadillo4r-0.7.0/armadillo4r/R/unvendor.R                                                                |   29 
 armadillo4r-0.7.0/armadillo4r/R/vendor.R                                                                  |   12 
 armadillo4r-0.7.0/armadillo4r/R/version.R                                                                 |    4 
 armadillo4r-0.7.0/armadillo4r/README.md                                                                   |    2 
 armadillo4r-0.7.0/armadillo4r/configure                                                                   |   30 
 armadillo4r-0.7.0/armadillo4r/inst/extdata/configure                                                      |   25 
 armadillo4r-0.7.0/armadillo4r/inst/extdata/src/01_ols.h                                                   |only
 armadillo4r-0.7.0/armadillo4r/inst/extdata/src/02_your_functions.h                                        |only
 armadillo4r-0.7.0/armadillo4r/inst/extdata/src/Makevars.in                                                |    4 
 armadillo4r-0.7.0/armadillo4r/inst/extdata/src/main.cpp                                                   |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r.hpp                                                |   35 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo.hpp                                      |   73 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/BaseCube_bones.hpp                       |  105 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/BaseCube_meat.hpp                        |  609 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Base_bones.hpp                           |  247 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Base_meat.hpp                            | 1316 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Col_bones.hpp                            |  418 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Col_meat.hpp                             | 2243 +
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/CubeToMatOp_bones.hpp                    |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/CubeToMatOp_meat.hpp                     |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Cube_bones.hpp                           | 1031 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Cube_meat.hpp                            | 7262 +++--
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/GenCube_bones.hpp                        |   63 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/GenCube_meat.hpp                         |  213 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Gen_bones.hpp                            |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Gen_meat.hpp                             |  278 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/GlueCube_bones.hpp                       |   32 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/GlueCube_meat.hpp                        |   38 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Glue_bones.hpp                           |   75 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Glue_meat.hpp                            |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/MapMat_bones.hpp                         |  277 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/MapMat_meat.hpp                          | 2080 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Mat_bones.hpp                            | 1692 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Mat_meat.hpp                             |12789 ++++++----
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/OpCube_bones.hpp                         |   53 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/OpCube_meat.hpp                          |  111 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Op_bones.hpp                             |   77 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Op_meat.hpp                              |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Proxy.hpp                                | 4087 +--
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/ProxyCube.hpp                            |  791 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Row_bones.hpp                            |  421 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/Row_meat.hpp                             | 2252 +
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SizeCube_bones.hpp                       |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SizeCube_meat.hpp                        |  188 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SizeMat_bones.hpp                        |   33 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SizeMat_meat.hpp                         |  160 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpBase_bones.hpp                         |  167 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpBase_meat.hpp                          | 1070 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpCol_bones.hpp                          |  116 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpCol_meat.hpp                           |  467 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpGlue_bones.hpp                         |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpGlue_meat.hpp                          |   63 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpMat_bones.hpp                          | 1195 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpMat_iterators_meat.hpp                 |  965 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpMat_meat.hpp                           | 8291 +++---
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpOp_bones.hpp                           |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpOp_meat.hpp                            |   74 
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 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpRow_bones.hpp                          |  113 
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 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpToDGlue_bones.hpp                      |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpToDGlue_meat.hpp                       |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/SpToDOp_bones.hpp                        |   59 
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 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/def_fftw3.hpp                            |   57 
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 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/def_superlu.hpp                          |  180 
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 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/diskio_meat.hpp                          | 6863 ++---
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/distr_param.hpp                          |  111 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eGlueCube_bones.hpp                      |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eGlueCube_meat.hpp                       |  189 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eGlue_bones.hpp                          |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eGlue_meat.hpp                           |  185 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eOpCube_bones.hpp                        |   78 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eOpCube_meat.hpp                         |  219 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eOp_bones.hpp                            |   73 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eOp_meat.hpp                             |  172 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eglue_core_bones.hpp                     |  100 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eglue_core_meat.hpp                      | 1938 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eop_aux.hpp                              |  552 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eop_core_bones.hpp                       |  200 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/eop_core_meat.hpp                        | 1690 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fft_engine_fftw3.hpp                     |  202 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fft_engine_kissfft.hpp                   |  497 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/field_bones.hpp                          |  627 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/field_meat.hpp                           | 3552 +-
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fill.hpp                                 |  284 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_accu.hpp                              | 1143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_all.hpp                               |  100 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_any.hpp                               |  100 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_approx_equal.hpp                      |  702 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_as_scalar.hpp                         |  544 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_balance.hpp                           |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_chi2rnd.hpp                           |  207 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_chol.hpp                              |  174 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_circshift.hpp                         |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_clamp.hpp                             |  147 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cond_rcond.hpp                        |   81 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_conv.hpp                              |   80 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_conv_to.hpp                           | 1258 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cor.hpp                               |   48 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cov.hpp                               |   48 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cross.hpp                             |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cumprod.hpp                           |   90 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_cumsum.hpp                            |   90 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_det.hpp                               |   77 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_diagmat.hpp                           |   95 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_diags_spdiags.hpp                     |  163 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_diagvec.hpp                           |   61 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_diff.hpp                              |   91 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_dot.hpp                               |  439 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eig_gen.hpp                           |  222 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eig_pair.hpp                          |  179 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eig_sym.hpp                           |  190 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eigs_gen.hpp                          |  509 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eigs_sym.hpp                          |  309 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_elem.hpp                              | 1428 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_expmat.hpp                            |  100 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_eye.hpp                               |  124 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_fft.hpp                               |  158 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_fft2.hpp                              |  147 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_find.hpp                              |  568 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_find_unique.hpp                       |   67 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_flip.hpp                              |   75 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_hess.hpp                              |  194 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_hist.hpp                              |   81 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_histc.hpp                             |   59 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_index_max.hpp                         |  179 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_index_min.hpp                         |  179 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_inplace_strans.hpp                    |  106 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_inplace_trans.hpp                     |  131 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_interp1.hpp                           |  431 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_interp2.hpp                           |  350 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_intersect.hpp                         |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_inv.hpp                               |  143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_inv_sympd.hpp                         |  142 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_join.hpp                              |  574 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_kmeans.hpp                            |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_kron.hpp                              |   95 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_log_det.hpp                           |  163 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_log_normpdf.hpp                       |  230 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_logmat.hpp                            |  143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_lu.hpp                                |   84 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_max.hpp                               |  312 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_mean.hpp                              |  160 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_median.hpp                            |   74 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_min.hpp                               |  312 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_misc.hpp                              |  703 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_mvnrnd.hpp                            |  117 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_n_unique.hpp                          |  141 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_nonzeros.hpp                          |   42 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_norm.hpp                              |  442 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_normalise.hpp                         |  123 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_normcdf.hpp                           |  220 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_normpdf.hpp                           |  228 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_numel.hpp                             |   92 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_omit.hpp                              |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_ones.hpp                              |  167 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_orth_null.hpp                         |   95 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_pinv.hpp                              |  126 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_polyfit.hpp                           |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_polyval.hpp                           |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_powext.hpp                            |  199 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_powmat.hpp                            |  113 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_princomp.hpp                          |  166 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_prod.hpp                              |   82 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_qr.hpp                                |  149 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_quantile.hpp                          |   57 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_qz.hpp                                |   60 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_rande.hpp                             |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randg.hpp                             |  276 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randi.hpp                             |  301 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randn.hpp                             |  410 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randperm.hpp                          |  182 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_randu.hpp                             |  402 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_range.hpp                             |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_rank.hpp                              |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_regspace.hpp                          |  319 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_repelem.hpp                           |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_repmat.hpp                            |   88 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_reshape.hpp                           |  197 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_resize.hpp                            |  158 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_reverse.hpp                           |  102 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_roots.hpp                             |   63 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_schur.hpp                             |  107 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_shuffle.hpp                           |   88 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_size.hpp                              |  347 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_solve.hpp                             |  263 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sort.hpp                              |  166 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sort_index.hpp                        |  117 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_speye.hpp                             |   91 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_spones.hpp                            |   40 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sprandn.hpp                           |  132 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sprandu.hpp                           |  132 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_spsolve.hpp                           |  227 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sqrtmat.hpp                           |  137 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_stddev.hpp                            |  155 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_strans.hpp                            |  105 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sum.hpp                               |  159 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_svd.hpp                               |  266 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_svds.hpp                              |  435 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_sylvester.hpp                         |  154 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_symmat.hpp                            |  142 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_toeplitz.hpp                          |   59 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trace.hpp                             |  751 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trans.hpp                             |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trapz.hpp                             |   53 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trig.hpp                              |  517 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trimat.hpp                            |  150 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trimat_ind.hpp                        |  143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trunc_exp.hpp                         |   99 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_trunc_log.hpp                         |  106 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_unique.hpp                            |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_var.hpp                               |  158 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_vecnorm.hpp                           |  456 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_vectorise.hpp                         |  111 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_wishrnd.hpp                           |  213 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/fn_zeros.hpp                             |  209 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_affmul_bones.hpp                    |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_affmul_meat.hpp                     |  685 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_atan2_bones.hpp                     |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_atan2_meat.hpp                      |  250 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_conv_bones.hpp                      |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_conv_meat.hpp                       |  462 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cor_bones.hpp                       |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cor_meat.hpp                        |   76 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cov_bones.hpp                       |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cov_meat.hpp                        |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cross_bones.hpp                     |   35 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_cross_meat.hpp                      |   84 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_hist_bones.hpp                      |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_hist_meat.hpp                       |  308 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_histc_bones.hpp                     |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_histc_meat.hpp                      |  212 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_hypot_bones.hpp                     |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_hypot_meat.hpp                      |  187 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_intersect_bones.hpp                 |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_intersect_meat.hpp                  |  156 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_join_bones.hpp                      |  124 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_join_meat.hpp                       |  553 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_kron_bones.hpp                      |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_kron_meat.hpp                       |  175 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_max_bones.hpp                       |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_max_meat.hpp                        |  201 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_min_bones.hpp                       |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_min_meat.hpp                        |  201 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_mixed_bones.hpp                     |  136 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_mixed_meat.hpp                      |  716 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_mvnrnd_bones.hpp                    |   66 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_mvnrnd_meat.hpp                     |  212 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_polyfit_bones.hpp                   |   54 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_polyfit_meat.hpp                    |  204 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_polyval_bones.hpp                   |   40 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_polyval_meat.hpp                    |   97 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_powext_bones.hpp                    |   98 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_powext_meat.hpp                     |  775 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_quantile_bones.hpp                  |   67 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_quantile_meat.hpp                   |  303 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_relational_bones.hpp                |  190 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_relational_meat.hpp                 |  527 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_solve_bones.hpp                     |  299 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_solve_meat.hpp                      |  964 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_times_bones.hpp                     |  254 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_times_meat.hpp                      | 1583 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_times_misc_bones.hpp                |  122 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_times_misc_meat.hpp                 |  782 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_toeplitz_bones.hpp                  |   27 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_toeplitz_meat.hpp                   |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_trapz_bones.hpp                     |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/glue_trapz_meat.hpp                      |  231 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_diag_bones.hpp                       |  305 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_diag_meat.hpp                        | 3560 +-
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_full_bones.hpp                       |  289 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_full_meat.hpp                        | 3683 +-
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_misc_bones.hpp                       |  142 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/gmm_misc_meat.hpp                        |  203 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/hdf5_misc.hpp                            |  846 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/hdf5_name.hpp                            |  133 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/include_hdf5.hpp                         |   63 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/include_superlu.hpp                      |  633 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/injector_bones.hpp                       |   79 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/injector_meat.hpp                        |  424 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/memory.hpp                               |  301 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mp_misc.hpp                              |  101 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtGlueCube_bones.hpp                     |   37 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtGlueCube_meat.hpp                      |   51 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtGlue_bones.hpp                         |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtGlue_meat.hpp                          |   66 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtOpCube_bones.hpp                       |   66 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtOpCube_meat.hpp                        |  135 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtOp_bones.hpp                           |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtOp_meat.hpp                            |  138 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpGlue_bones.hpp                       |   43 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpGlue_meat.hpp                        |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpOp_bones.hpp                         |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpOp_meat.hpp                          |  102 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpReduceOp_bones.hpp                   |   67 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mtSpReduceOp_meat.hpp                    |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_gemm.hpp                             |  732 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_gemm_mixed.hpp                       |  378 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_gemv.hpp                             |  731 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_herk.hpp                             |  667 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/mul_syrk.hpp                             |  647 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_DenseGenMatProd_bones.hpp         |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_DenseGenMatProd_meat.hpp          |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_DoubleShiftQR_bones.hpp           |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_DoubleShiftQR_meat.hpp            |  322 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_EigsSelect.hpp                    |   57 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_GenEigsSolver_bones.hpp           |  118 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_GenEigsSolver_meat.hpp            |  418 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SortEigenvalue.hpp                |  235 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SparseGenMatProd_bones.hpp        |   38 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SparseGenMatProd_meat.hpp         |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SparseGenRealShiftSolve_bones.hpp |   52 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SparseGenRealShiftSolve_meat.hpp  |  180 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SymEigsShiftSolver_bones.hpp      |   33 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SymEigsShiftSolver_meat.hpp       |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SymEigsSolver_bones.hpp           |  108 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_SymEigsSolver_meat.hpp            |  404 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_TridiagEigen_bones.hpp            |   38 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_TridiagEigen_meat.hpp             |  165 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_UpperHessenbergEigen_bones.hpp    |   56 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_UpperHessenbergEigen_meat.hpp     |  201 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_UpperHessenbergQR_bones.hpp       |   46 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_UpperHessenbergQR_meat.hpp        |  234 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/newarp_cx_attrib.hpp                     |   44 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_accu_bones.hpp                        |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_accu_meat.hpp                         |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_all_bones.hpp                         |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_all_meat.hpp                          |  605 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_any_bones.hpp                         |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_any_meat.hpp                          |  599 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_chi2rnd_bones.hpp                     |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_chi2rnd_meat.hpp                      |  189 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_chol_bones.hpp                        |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_chol_meat.hpp                         |   89 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_circshift_bones.hpp                   |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_circshift_meat.hpp                    |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_clamp_bones.hpp                       |  111 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_clamp_meat.hpp                        |  744 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_col_as_mat_bones.hpp                  |   25 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_col_as_mat_meat.hpp                   |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cond_bones.hpp                        |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cond_meat.hpp                         |  217 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cor_bones.hpp                         |   30 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cor_meat.hpp                          |  144 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cov_bones.hpp                         |   30 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cov_meat.hpp                          |  116 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cumprod_bones.hpp                     |   51 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cumprod_meat.hpp                      |  226 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cumsum_bones.hpp                      |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cumsum_meat.hpp                       |  226 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cx_scalar_bones.hpp                   |  221 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_cx_scalar_meat.hpp                    |  739 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_det_bones.hpp                         |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_det_meat.hpp                          |  223 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diagmat_bones.hpp                     |   72 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diagmat_meat.hpp                      |  944 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diagvec_bones.hpp                     |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diagvec_meat.hpp                      |  609 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diff_bones.hpp                        |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_diff_meat.hpp                         |  311 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_dot_bones.hpp                         |  167 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_dot_meat.hpp                          |  806 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_dotext_bones.hpp                      |   57 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_dotext_meat.hpp                       |  227 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_elem_bones.hpp                        |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_elem_meat.hpp                         |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_expmat_bones.hpp                      |   58 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_expmat_meat.hpp                       |  337 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_fft_bones.hpp                         |   67 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_fft_meat.hpp                          |  437 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_find_bones.hpp                        |  156 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_find_meat.hpp                         | 1025 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_find_unique_bones.hpp                 |   84 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_find_unique_meat.hpp                  |  162 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_flip_bones.hpp                        |   73 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_flip_meat.hpp                         |  395 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_hist_bones.hpp                        |   30 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_hist_meat.hpp                         |  167 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_htrans_bones.hpp                      |  161 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_htrans_meat.hpp                       |  499 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_index_max_bones.hpp                   |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_index_max_meat.hpp                    |  509 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_index_min_bones.hpp                   |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_index_min_meat.hpp                    |  509 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_inv_gen_bones.hpp                     |  223 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_inv_gen_meat.hpp                      |  574 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_inv_spd_bones.hpp                     |   92 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_inv_spd_meat.hpp                      |  472 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_log_det_bones.hpp                     |   60 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_log_det_meat.hpp                      |  303 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_logmat_bones.hpp                      |  112 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_logmat_meat.hpp                       |  763 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_max_bones.hpp                         |  177 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_max_meat.hpp                          | 1725 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_mean_bones.hpp                        |  148 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_mean_meat.hpp                         |  837 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_median_bones.hpp                      |   95 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_median_meat.hpp                       |  399 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_min_bones.hpp                         |  179 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_min_meat.hpp                          | 1725 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_nonzeros_bones.hpp                    |   35 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_nonzeros_meat.hpp                     |  127 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_norm2est_bones.hpp                    |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_norm2est_meat.hpp                     |  317 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_norm_bones.hpp                        |   92 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_norm_meat.hpp                         | 1366 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_normalise_bones.hpp                   |   48 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_normalise_meat.hpp                    |  166 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_omit_bones.hpp                        |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_omit_meat.hpp                         |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_orth_null_bones.hpp                   |   50 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_orth_null_meat.hpp                    |  245 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_pinv_bones.hpp                        |   74 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_pinv_meat.hpp                         |  461 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_powmat_bones.hpp                      |   61 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_powmat_meat.hpp                       |  386 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_princomp_bones.hpp                    |   89 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_princomp_meat.hpp                     |  366 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_prod_bones.hpp                        |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_prod_meat.hpp                         |  232 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_range_bones.hpp                       |   31 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_range_meat.hpp                        |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_rank_bones.hpp                        |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_rank_meat.hpp                         |  273 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_rcond_bones.hpp                       |   23 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_rcond_meat.hpp                        |  119 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_relational_bones.hpp                  |  181 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_relational_meat.hpp                   |  746 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_repelem_bones.hpp                     |   34 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_repelem_meat.hpp                      |  104 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_repmat_bones.hpp                      |   44 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_repmat_meat.hpp                       |  193 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_reshape_bones.hpp                     |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_reshape_meat.hpp                      |  360 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_resize_bones.hpp                      |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_resize_meat.hpp                       |  233 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_reverse_bones.hpp                     |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_reverse_meat.hpp                      |  210 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_roots_bones.hpp                       |   42 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_roots_meat.hpp                        |  170 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_row_as_mat_bones.hpp                  |   25 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_row_as_mat_meat.hpp                   |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_shuffle_bones.hpp                     |   47 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_shuffle_meat.hpp                      |  254 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sort_bones.hpp                        |   62 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sort_index_bones.hpp                  |  169 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sort_index_meat.hpp                   |  229 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sort_meat.hpp                         |  415 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_as_dense_bones.hpp                 |   25 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_as_dense_meat.hpp                  |   27 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_diagvec_bones.hpp                  |   28 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_diagvec_meat.hpp                   |   59 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_max_bones.hpp                      |   86 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_max_meat.hpp                       |  739 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_mean_bones.hpp                     |   80 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_mean_meat.hpp                      |  428 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_min_bones.hpp                      |   86 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_min_meat.hpp                       |  787 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_minus_bones.hpp                    |   69 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_minus_meat.hpp                     |  180 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_nonzeros_bones.hpp                 |   28 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_nonzeros_meat.hpp                  |  101 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_plus_bones.hpp                     |   41 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_plus_meat.hpp                      |   94 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_stddev_bones.hpp                   |   37 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_stddev_meat.hpp                    |  181 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_sum_bones.hpp                      |   28 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_sum_meat.hpp                       |  156 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_var_bones.hpp                      |   70 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_var_meat.hpp                       |  348 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_vecnorm_bones.hpp                  |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sp_vecnorm_meat.hpp                   |  233 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sqrtmat_bones.hpp                     |  108 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sqrtmat_meat.hpp                      |  673 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_stddev_bones.hpp                      |   55 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_stddev_meat.hpp                       |  211 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_strans_bones.hpp                      |  107 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_strans_meat.hpp                       |  636 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sum_bones.hpp                         |   97 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_sum_meat.hpp                          |  747 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_symmat_bones.hpp                      |   75 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_symmat_meat.hpp                       |  325 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_toeplitz_bones.hpp                    |   43 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_toeplitz_meat.hpp                     |  137 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_trimat_bones.hpp                      |   93 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_trimat_meat.hpp                       |  484 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_unique_bones.hpp                      |   91 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_unique_meat.hpp                       |  220 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_var_bones.hpp                         |   85 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_var_meat.hpp                          |  466 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_vecnorm_bones.hpp                     |   69 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_vecnorm_meat.hpp                      |  303 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_vectorise_bones.hpp                   |  116 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_vectorise_meat.hpp                    |  580 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_wishrnd_bones.hpp                     |   64 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/op_wishrnd_meat.hpp                      |  368 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_div.hpp                    |  217 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_minus.hpp                  |  234 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_plus.hpp                   |  234 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_relational.hpp             |  333 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_schur.hpp                  |  135 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_cube_times.hpp                  |  129 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_div.hpp                         |  440 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_minus.hpp                       |  570 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_ostream.hpp                     |  181 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_plus.hpp                        |  499 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_relational.hpp                  |  819 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_schur.hpp                       |  446 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/operator_times.hpp                       |  556 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/podarray_bones.hpp                       |   94 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/podarray_meat.hpp                        |  340 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/promote_type.hpp                         |  855 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/restrictors.hpp                          |  738 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/running_stat_bones.hpp                   |  146 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/running_stat_meat.hpp                    |  522 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/running_stat_vec_bones.hpp               |  242 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/running_stat_vec_meat.hpp                |  771 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/sp_auxlib_bones.hpp                      |  413 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/sp_auxlib_meat.hpp                       | 3902 +--
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/span.hpp                                 |   81 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spdiagview_bones.hpp                     |  135 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spdiagview_meat.hpp                      | 1297 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_join_bones.hpp                    |  109 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_join_meat.hpp                     |  438 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_kron_bones.hpp                    |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_kron_meat.hpp                     |  157 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_max_bones.hpp                     |   68 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_max_meat.hpp                      |  231 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_merge_bones.hpp                   |   36 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_merge_meat.hpp                    |  625 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_min_bones.hpp                     |   68 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_min_meat.hpp                      |  229 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_minus_bones.hpp                   |   80 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_minus_meat.hpp                    |  408 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_plus_bones.hpp                    |   70 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_plus_meat.hpp                     |  348 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_relational_bones.hpp              |  103 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_relational_meat.hpp               |  564 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_schur_bones.hpp                   |   86 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_schur_meat.hpp                    |  470 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_times_bones.hpp                   |   76 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spglue_times_meat.hpp                    |  386 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_accu_bones.hpp                      |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_accu_meat.hpp                       |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_circshift_bones.hpp                 |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_circshift_meat.hpp                  |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_diagmat_bones.hpp                   |   85 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_diagmat_meat.hpp                    |  566 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_htrans_bones.hpp                    |   49 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_htrans_meat.hpp                     |   87 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_misc_bones.hpp                      |  372 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_misc_meat.hpp                       |  766 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_norm_bones.hpp                      |   45 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_norm_meat.hpp                       |  170 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_normalise_bones.hpp                 |   28 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_normalise_meat.hpp                  |  147 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_omit_bones.hpp                      |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_omit_meat.hpp                       |only
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_relational_bones.hpp                |  163 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_relational_meat.hpp                 |  671 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_repmat_bones.hpp                    |   40 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_repmat_meat.hpp                     |  143 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_reverse_bones.hpp                   |   35 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_reverse_meat.hpp                    |  208 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_strans_bones.hpp                    |   53 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_strans_meat.hpp                     |  183 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_symmat_bones.hpp                    |   43 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_symmat_meat.hpp                     |  106 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_trimat_bones.hpp                    |   83 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_trimat_meat.hpp                     |  417 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_vectorise_bones.hpp                 |   66 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spop_vectorise_meat.hpp                  |  126 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spsolve_factoriser_bones.hpp             |   65 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/spsolve_factoriser_meat.hpp              |  349 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/strip.hpp                                |  234 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_bones.hpp                        | 1004 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_cube_bones.hpp                   |  351 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_cube_each_bones.hpp              |  240 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_cube_each_meat.hpp               | 1229 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_cube_meat.hpp                    | 3547 +-
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_cube_slices_bones.hpp            |  122 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_cube_slices_meat.hpp             |  670 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_each_bones.hpp                   |  255 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_each_meat.hpp                    | 1729 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_elem1_bones.hpp                  |  158 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_elem1_meat.hpp                   | 1316 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_elem2_bones.hpp                  |  165 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_elem2_meat.hpp                   | 1346 -
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_field_bones.hpp                  |  119 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_field_meat.hpp                   |  751 
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/subview_meat.hpp                         | 6534 +++--
 armadillo4r-0.7.0/armadillo4r/inst/include/armadillo4r/armadillo/sym_helper.hpp                           |  676 
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Package rspacer updated to version 0.3.1 with previous version 0.3.0 dated 2025-10-17

Title: 'RSpace' API Wrapper
Description: Wrapper for the 'RSpace' Electronic Lab Notebook (<https://www.researchspace.com/>) API. This packages provides convenience functions to browse, search, create, and edit your 'RSpace' documents. In addition, it enables filling 'RSpace' templates from R Markdown/Quarto templates or tabular data (e.g., 'Excel' files). This R package is not developed or endorsed by 'Research Space'.
Author: Gerhard Burger [aut, cre] , Hanneke Leegwater [aut] , Leiden University [cph, fnd]
Maintainer: Gerhard Burger <burger.ga@gmail.com>

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Package MIC updated to version 2.0.0 with previous version 1.2.0 dated 2025-10-12

Title: Analysis of Antimicrobial Minimum Inhibitory Concentration Data
Description: Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data. Validate the results of an MIC experiment by comparing observed MIC values to a gold standard assay, in line with standards from the International Organization for Standardization (2021) <https://www.iso.org/standard/79377.html>.
Author: Alessandro Gerada [aut, cre, cph]
Maintainer: Alessandro Gerada <alessandro.gerada@liverpool.ac.uk>

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Package ganttify updated to version 0.1.7 with previous version 0.1.1 dated 2025-10-28

Title: Create Interactive Gantt Charts with Work Breakdown Structure
Description: Create Primavera-style interactive Gantt charts with Work Breakdown Structure (WBS) hierarchy and activities. Features include color-coded WBS items, indented labels, scrollable views for large projects, dynamic date formatting, and the ability to dim past activities. Built on top of 'plotly' for interactive visualizations.
Author: Ahmed Aredah [aut, cre]
Maintainer: Ahmed Aredah <Ahmed.Aredah@gmail.com>

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Package superb updated to version 1.0.1 with previous version 1.0.0 dated 2025-08-18

Title: Summary Plots with Adjusted Error Bars
Description: Computes standard error and confidence interval of various descriptive statistics under various designs and sampling schemes. The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005) <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012) <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>, Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding <doi:10.20982/tqmp.15.1.p001> for specific references. The documentation is available at <https://dcousin3.github.io/superb/> .
Author: Denis Cousineau [aut, cre], Bradley Harding [ctb], Marc-Andre Goulet [ctb], Jesika Walker [art, pre]
Maintainer: Denis Cousineau <denis.cousineau@uottawa.ca>

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Package stan4bart updated to version 0.0-11 with previous version 0.0-10 dated 2024-09-04

Title: Bayesian Additive Regression Trees with Stan-Sampled Parametric Extensions
Description: Fits semiparametric linear and multilevel models with non-parametric additive Bayesian additive regression tree (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) components and Stan (Stan Development Team (2021) <https://mc-stan.org/>) sampled parametric ones. Multilevel models can be expressed using 'lme4' syntax (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Author: Vincent Dorie [aut, cre] , Ben Goodrich [ctb] , Jonah Gabry [ctb] , Imad Ali [ctb] , Sam Brilleman [ctb] , Paul-Christian Burkner [ctb] , Joshua Pritikin [ctb] , Andrew Gelman [ctb] , Bob Carpenter [ctb] , Matt Hoffman [ctb] , Daniel Lee [ctb] , Mich [...truncated...]
Maintainer: Vincent Dorie <vdorie@gmail.com>

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Package Anthropometry updated to version 1.21 with previous version 1.20 dated 2025-07-23

Title: Statistical Methods for Anthropometric Data
Description: Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) <doi:10.18637/jss.v077.i06>.
Author: Guillermo Vinue [aut, cre], Irene Epifanio [aut], Amelia Simo [aut], M. Victoria Ibanez [aut], Juan Domingo [aut], Guillermo Ayala [aut]
Maintainer: Guillermo Vinue <guillermo.vinue@uv.es>

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Package shinymrp updated to version 0.10.0 with previous version 0.9.1 dated 2025-11-11

Title: Interface for Multilevel Regression and Poststratification
Description: Dual interfaces, graphical and programmatic, designed for intuitive applications of Multilevel Regression and Poststratification (MRP). Users can apply the method to a variety of datasets, from electronic health records to sample survey data, through an end-to-end Bayesian data analysis workflow. The package provides robust tools for data cleaning, exploratory analysis, flexible model building, and insightful result visualization. For more details, see Si et al. (2020) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2020002/article/00003-eng.pdf?st=iF1_Fbrh> and Si (2025) <doi:10.1214/24-STS932>.
Author: Toan Tran [cre, aut, cph], Jonah Gabry [aut, cph], Yajuan Si [aut, cph]
Maintainer: Toan Tran <trannttoan97@gmail.com>

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Package rtpcr updated to version 2.0.4 with previous version 2.0.3 dated 2025-11-08

Title: qPCR Data Analysis
Description: Various methods are employed for statistical analysis and graphical presentation of real-time PCR (quantitative PCR or qPCR) data. 'rtpcr' handles amplification efficiency calculation, statistical analysis and graphical representation of real-time PCR data based on up to two reference genes. By accounting for amplification efficiency values, 'rtpcr' was developed using a general calculation method described by Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods. Based on the experimental conditions, the functions of the 'rtpcr' package use t-test (for experiments with a two-level factor), analysis of variance (ANOVA), analysis of covariance (ANCOVA) or analysis of repeated measure data to calculate the fold change (FC, Delta Delta Ct method) or relative expression (RE, Delta Ct method). The functions further provide standard errors and confidence intervals for means, a [...truncated...]
Author: Ghader Mirzaghaderi [aut, cre, cph]
Maintainer: Ghader Mirzaghaderi <gh.mirzaghaderi@uok.ac.ir>

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Package nanoarrow updated to version 0.7.0-2 with previous version 0.7.0-1 dated 2025-09-24

Title: Interface to the 'nanoarrow' 'C' Library
Description: Provides an 'R' interface to the 'nanoarrow' 'C' library and the 'Apache Arrow' application binary interface. Functions to import and export 'ArrowArray', 'ArrowSchema', and 'ArrowArrayStream' 'C' structures to and from 'R' objects are provided alongside helpers to facilitate zero-copy data transfer among 'R' bindings to libraries implementing the 'Arrow' 'C' data interface.
Author: Dewey Dunnington [aut, cre] , Apache Arrow [aut, cph], Apache Software Foundation [cph]
Maintainer: Dewey Dunnington <dewey@dunnington.ca>

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Package MixedPsy updated to version 1.3.0 with previous version 1.2.0 dated 2025-02-18

Title: Statistical Tools for the Analysis of Psychophysical Data
Description: Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets. For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012).
Author: Alessandro Moscatelli [aut, cre] , Priscilla Balestrucci [aut]
Maintainer: Alessandro Moscatelli <moskante@gmail.com>

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Package E2E updated to version 0.1.2 with previous version 0.1.1 dated 2025-11-28

Title: Ensemble Learning Framework for Diagnostic and Prognostic Modeling
Description: Provides a framework to build and evaluate diagnosis or prognosis models using stacking, voting, and bagging ensemble techniques with various base learners. The package also includes tools for visualization and interpretation of models. The development version of the package is available on 'GitHub' at <https://github.com/xiaojie0519/E2E>. The methods are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655> on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> on stacking.
Author: Shanjie Luan [aut, cre]
Maintainer: Shanjie Luan <Luan20050519@163.com>

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Package airGRdatasets updated to version 0.2.3 with previous version 0.2.2 dated 2025-11-18

Title: Hydro-Meteorological Catchments Datasets for the 'airGR' Packages
Description: Sample of hydro-meteorological datasets extracted from the 'CAMELS-FR' French database <doi:10.57745/WH7FJR>. It provides metadata and catchment-scale aggregated hydro-meteorological time series on a pool of French catchments for use by the 'airGR' packages.
Author: Olivier Delaigue [aut, cre] , Pierre Brigode [aut] , Guillaume Thirel [aut] , Benoît Genot [ctb], Guilherme Mendoza Guimaraes [ctb]
Maintainer: Olivier Delaigue <airGR@inrae.fr>

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Package tram updated to version 1.3-1 with previous version 1.3-0 dated 2025-11-18

Title: Transformation Models
Description: Formula-based user-interfaces to specific transformation models implemented in package 'mlt' (<DOI:10.32614/CRAN.package.mlt>, <DOI:10.32614/CRAN.package.mlt.docreg>). Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, <DOI:10.1093/biostatistics/kxac048>) and a tutorial explains applications in survival analysis (Siegfried et al., 2025, <DOI:10.48550/arXiv.2402.06428>). Multivariate conditional transformation models (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023, <DOI:10.108 [...truncated...]
Author: Torsten Hothorn [aut, cre] , Luisa Barbanti [ctb] , Sandra Siegfried [aut] , Lucas Kook [aut] , Susanne Dandl [ctb] , Brian Ripley [ctb], Bill Venables [ctb], Douglas M. Bates [ctb], Nadja Klein [ctb]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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Package singleCellHaystack updated to version 1.0.3 with previous version 1.0.2 dated 2024-01-11

Title: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
Description: One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez [...truncated...]
Author: Alexis Vandenbon [aut, cre] , Diego Diez [aut]
Maintainer: Alexis Vandenbon <alexis.vandenbon@gmail.com>

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Package SDModels updated to version 2.0.0 with previous version 1.0.13 dated 2025-06-05

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

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Package qdap updated to version 2.4.6.1 with previous version 2.4.6 dated 2023-05-11

Title: Bridging the Gap Between Qualitative Data and Quantitative Analysis
Description: Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing.
Author: Tyler Rinker [aut, cre], Bryan Goodrich [ctb], Dason Kurkiewicz [ctb]
Maintainer: Tyler Rinker <tyler.rinker@gmail.com>

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Package polle updated to version 1.6.2 with previous version 1.6.1 dated 2025-12-01

Title: Policy Learning
Description: Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
Author: Andreas Nordland [aut, cre], Klaus Holst [aut]
Maintainer: Andreas Nordland <andreasnordland@gmail.com>

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Package istat readmission to version 1.1.1 with previous version 1.0 dated 2024-09-10

Title: Download and Manipulate Data from Istat
Description: Download data from ISTAT (Italian Institute of Statistics) database, both old and new provider (respectively, <http://dati.istat.it/> and <https://esploradati.istat.it/databrowser/>). Additional functions for manipulating data are provided. Moreover, a 'shiny' application called 'shinyIstat' can be used to search, download and filter datasets in an easier way.
Author: Elena Gradi [aut, cre], Alissa Lelli [aut], Daniela Ichim [aut]
Maintainer: Elena Gradi <elenaagradi@gmail.com>

This is a re-admission after prior archival of version 1.0 dated 2024-09-10

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Package crmPack updated to version 2.0.1 with previous version 2.0.0 dated 2025-11-29

Title: Object-Oriented Implementation of Dose Escalation Designs
Description: Implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.
Author: Daniel Sabanes Bove [aut, cre], Wai Yin Yeung [aut], Burak Kuersad Guenhan [aut], Giuseppe Palermo [aut], Thomas Jaki [aut], Jiawen Zhu [aut], Ziwei Liao [aut], Dimitris Kontos [aut], Marlene Schulte-Goebel [aut], Doug Kelkhoff [aut] , Oliver Boix [a [...truncated...]
Maintainer: Daniel Sabanes Bove <daniel.sabanes_bove@rconis.com>

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Package clinUtils updated to version 0.2.2 with previous version 0.2.1 dated 2025-09-03

Title: General Utility Functions for Analysis of Clinical Data
Description: Utility functions to facilitate the import, the reporting and analysis of clinical data. Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate the package functionalities.
Author: Laure Cougnaud [aut, cre], Michela Pasetto [aut], Arne De Roeck [rev] , Open Analytics [cph]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>

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Package Rnest updated to version 1.3 with previous version 1.2 dated 2025-06-19

Title: Next Eigenvalue Sufficiency Test
Description: Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.
Author: P.-O. Caron [aut, cre, cph]
Maintainer: P.-O. Caron <pocaron19@gmail.com>

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Package psre updated to version 0.3 with previous version 0.2 dated 2025-11-24

Title: Presenting Statistical Results Effectively
Description: Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800). Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay. Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book.
Author: Dave Armstrong [aut, cre], Robert Andersen [aut], Justin Esarey [cph], John Fox [cph], Michael Friendly [cph], Adrian Bowman [cph], Adelchi Azzalini [cph]
Maintainer: Dave Armstrong <davearmstrong.ps@gmail.com>

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Package cpp4r updated to version 0.4.0 with previous version 0.3.0 dated 2025-10-16

Title: Header-Only 'C++' and 'R' Interface
Description: Provides a header only, 'C++' interface to 'R' with enhancements over 'cpp11'. Enforces copy-on-write semantics consistent with 'R' behavior. Offers native support for ALTREP objects, 'UTF-8' string handling, modern 'C++11' features and idioms, and reduced memory requirements. Allows for vendoring, making it useful for restricted environments. Compared to 'cpp11', it adds support for converting 'C++' maps to 'R' lists, 'Roxygen' documentation directly in 'C++' code, proper handling of matrix attributes, support for nullable external pointers, bidirectional copy of complex number types, flexibility in type conversions, use of nullable pointers, and various performance optimizations.
Author: Mauricio Vargas Sepulveda [aut, cre] , Posit Software, PBC [aut]
Maintainer: Mauricio Vargas Sepulveda <m.vargas.sepulveda@gmail.com>

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Package survAH updated to version 1.2.0 with previous version 1.0.0 dated 2023-01-17

Title: Survival Data Analysis using Average Hazard
Description: Performs two-sample comparisons based on average hazard with survival weight (AHSW) or general censoring-free incidence rate (CFIR) proposed by Uno and Horiguchi (2023) <doi:10.1002/sim.9651>.
Author: Hajime Uno [aut] , Miki Horiguchi [aut, cre] , Zihan Qian [aut]
Maintainer: Miki Horiguchi <horiguchimiki@gmail.com>

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Package rmapshaper updated to version 0.6.0 with previous version 0.5.0 dated 2023-04-10

Title: Client for 'mapshaper' for 'Geospatial' Operations
Description: Edit and simplify 'geojson', 'Spatial', and 'sf' objects. This is wrapper around the 'mapshaper' 'JavaScript' library by Matthew Bloch <https://github.com/mbloch/mapshaper/> to perform topologically-aware polygon simplification, as well as other operations such as clipping, erasing, dissolving, and converting 'multi-part' to 'single-part' geometries.
Author: Andy Teucher [aut, cre] , Kenton Russell [aut] , Matthew Bloch [cph]
Maintainer: Andy Teucher <andy.teucher@gmail.com>

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Package orbital updated to version 0.4.0 with previous version 0.3.1 dated 2025-08-29

Title: Predict with 'tidymodels' Workflows in Databases
Description: Turn 'tidymodels' workflows into objects containing the sufficient sequential equations to perform predictions. These smaller objects allow for low dependency prediction locally or directly in databases.
Author: Emil Hvitfeldt [aut, cre], Posit Software, PBC [cph, fnd]
Maintainer: Emil Hvitfeldt <emil.hvitfeldt@posit.co>

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Package mini007 updated to version 0.2.2 with previous version 0.2.1 dated 2025-11-03

Title: Lightweight Framework for Orchestrating Multi-Agent Large Language Models
Description: Provides tools for creating agents with persistent state using R6 classes <https://cran.r-project.org/package=R6> and the 'ellmer' package <https://cran.r-project.org/package=ellmer>. Tracks prompts, messages, and agent metadata for reproducible, multi-turn large language model sessions.
Author: Mohamed El Fodil Ihaddaden [aut, cre]
Maintainer: Mohamed El Fodil Ihaddaden <ihaddaden.fodeil@gmail.com>

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Package csurvey updated to version 1.14 with previous version 1.12 dated 2025-05-23

Title: Constrained Regression for Survey Data
Description: Domain mean estimation with monotonicity or block monotone constraints. See Xu X, Meyer MC and Opsomer JD (2021)<doi:10.1016/j.jspi.2021.02.004> for more details.
Author: Xiyue Liao [aut, cre]
Maintainer: Xiyue Liao <xliao@sdsu.edu>

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Package christmas updated to version 1.4.1 with previous version 1.4.0 dated 2024-12-10

Title: Generation of Different Animated Christmas Cards
Description: Generation of different Christmas cards, most of them being animated. Most of the cards can be generated in three languages (English, Catalan and Spanish). The collection started in 2009.
Author: Jose Barrera-Gomez [aut, cre]
Maintainer: Jose Barrera-Gomez <jose.barrera@uab.cat>

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Package biodosetools updated to version 3.7.1.2 with previous version 3.7.1 dated 2025-10-22

Title: 'shiny' Application for Biological Dosimetry
Description: A tool to perform all different statistical tests and calculations needed by Biological dosimetry Laboratories. Detailed documentation is available in <https://biodosetools-team.github.io/documentation/>.
Author: Alfredo Hernandez [aut] , Anna Frances-Abellan [aut, cre] , David Endesfelder [aut], Pere Puig [aut]
Maintainer: Anna Frances-Abellan <anna.frances@uab.cat>

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Package SHAPBoost updated to version 1.0.1 with previous version 1.0.0 dated 2025-09-29

Title: The SHAPBoost Feature Selection Algorithm
Description: The implementation of SHAPBoost, a boosting-based feature selection technique that ranks features iteratively based on Shapley values.
Author: Oemer Tarik Oezyilmaz [aut, cre, cph] , Tamas Szili-Toeroek [aut, cph]
Maintainer: Oemer Tarik Oezyilmaz <o.t.ozyilmaz@umcg.nl>

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Package lpanda updated to version 0.2.1 with previous version 0.2.0 dated 2025-11-29

Title: Local Political Actor Network Diachronic Analysis Tools
Description: Provides functions to prepare, visualize, and analyse diachronic network data on local political actors, with a particular focus on the development of local party systems and identification of actor groups. Formalizes and automates a continuity diagram method that has been previously applied in research on Czech local politics, e.g. Bubenicek and Kubalek (2010, ISSN:1803-8220), Kubalek and Bubenicek (2012, ISSN:1803-8220), and Cmejrek, Bubenicek, and Copik (2010, ISBN:978-80-247-3061-5). The package also includes several example datasets derived from Czech municipal elections, compiled from official election results, field research, and previously published case studies on Czech local politics.
Author: Vaclav Bubenicek [aut, cre, cph] )
Maintainer: Vaclav Bubenicek <bubenicek@pef.czu.cz>

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Package htmltools updated to version 0.5.9 with previous version 0.5.8.1 dated 2024-04-04

Title: Tools for HTML
Description: Tools for HTML generation and output.
Author: Joe Cheng [aut], Carson Sievert [aut, cre] , Barret Schloerke [aut] , Winston Chang [aut] , Yihui Xie [aut], Jeff Allen [aut], Posit Software, PBC [cph, fnd]
Maintainer: Carson Sievert <carson@posit.co>

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Package broom updated to version 1.0.11 with previous version 1.0.10 dated 2025-09-13

Title: Convert Statistical Objects into Tidy Tibbles
Description: Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
Author: David Robinson [aut], Alex Hayes [aut] , Simon Couch [aut] , Emil Hvitfeldt [aut, cre] , Posit Software, PBC [cph, fnd] , Indrajeet Patil [ctb] , Derek Chiu [ctb], Matthieu Gomez [ctb], Boris Demeshev [ctb], Dieter Menne [ctb], Benjamin Nutter [ctb], [...truncated...]
Maintainer: Emil Hvitfeldt <emil.hvitfeldt@posit.co>

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Package MBMethPred updated to version 0.1.4.4 with previous version 0.1.4.3 dated 2025-06-17

Title: Medulloblastoma Subgroups Prediction
Description: Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, 'MBMethPred' can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. <doi: 10.3389/fgene.2023.1233657> for more details.
Author: Edris Sharif Rahmani [aut, ctb, cre] , Ankita Sunil Lawarde [aut, ctb] , Vijayachitra Modhukur [aut, ctb]
Maintainer: Edris Sharif Rahmani <rahmani.biotech@gmail.com>

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Package PubChemR updated to version 2.1.8 with previous version 2.1.7 dated 2025-11-05

Title: Interface to the 'PubChem' Database for Chemical Data Retrieval
Description: Provides an interface to the 'PubChem' database via the PUG REST <https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest> and PUG View <https://pubchem.ncbi.nlm.nih.gov/docs/pug-view> services. This package allows users to automatically access chemical and biological data from 'PubChem', including compounds, substances, assays, and various other data types. Functions are available to retrieve data in different formats, perform searches, and access detailed annotations.
Author: Selcuk Korkmaz [aut, cre] , Bilge Eren Yamasan [aut] , Dincer Goksuluk [aut]
Maintainer: Selcuk Korkmaz <selcukorkmaz@gmail.com>

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Package hackeRnews updated to version 0.2.2 with previous version 0.2.1 dated 2025-04-08

Title: Wrapper for the 'Official Hacker News' API
Description: Use the Official Hacker News API through R. Retrieve posts, articles and other items in form of convenient R objects.
Author: Ryszard Szymanski [aut, cre], Piotr Janus [aut], Zuzanna Magierska [aut], Rafal Muszynski [aut], Andrzej Nowikowski [aut]
Maintainer: Ryszard Szymanski <ryszard.szymanski@outlook.com>

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Package datanugget updated to version 1.4.0 with previous version 1.3.1 dated 2024-09-14

Title: Create, and Refine Data Nuggets
Description: Creating, and refining data nuggets. Data nuggets reduce a large dataset into a small collection of nuggets of data, each containing a center (location), weight (importance), and scale (variability) parameter. Data nugget centers are created by choosing observations in the dataset which are as equally spaced apart as possible. Data nugget weights are created by counting the number observations closest to a given data nugget center. We then say the data nugget 'contains' these observations and the data nugget center is recalculated as the mean of these observations. Data nugget scales are created by calculating the trace of the covariance matrix of the observations contained within a data nugget divided by the dimension of the dataset. Data nuggets are refined by 'splitting' data nuggets which have scales or shapes (defined as the ratio of the two largest eigenvalues of the covariance matrix of the observations contained within the data nugget) Reference paper: [1] Beavers, T. E., Cheng [...truncated...]
Author: Rituparna Dey [aut, cre], Yajie Duan [aut], Traymon Beavers [aut], Javier Cabrera [aut], Ge Cheng [aut], Kunting Qi [aut], Mariusz Lubomirski [aut]
Maintainer: Rituparna Dey <rituparnadey525@gmail.com>

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Package CohortSurvival updated to version 1.1.0 with previous version 1.0.3 dated 2025-08-18

Title: Estimate Survival from Common Data Model Cohorts
Description: Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts.
Author: Kim Lopez-Gueell [aut, cre] , Edward Burn [aut] , Marti Catala [aut] , Xintong Li [aut] , Danielle Newby [aut] , Nuria Mercade-Besora [aut]
Maintainer: Kim Lopez-Gueell <kim.lopez@spc.ox.ac.uk>

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Package catdata updated to version 1.2.5 with previous version 1.2.4 dated 2024-01-25

Title: Categorical Data
Description: This R-package contains examples from the book "Regression for Categorical Data", Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Author: Gunther Schauberger [aut, cre], Gerhard Tutz [aut]
Maintainer: Gunther Schauberger <gunther.schauberger@tum.de>

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Package ambient updated to version 1.0.3 with previous version 1.0.2 dated 2022-09-08

Title: A Generator of Multidimensional Noise
Description: Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The 'ambient' package provides an interface to the 'FastNoise' C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions.
Author: Thomas Lin Pedersen [cre, aut] , Jordan Peck [aut]
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>

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Package tryCatchLog updated to version 1.3.3 with previous version 1.3.1 dated 2021-10-25

Title: Advanced 'tryCatch()' and 'try()' Functions
Description: Advanced tryCatch() and try() functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).
Author: Juergen Altfeld [aut, cre, cph], Charles Epaillard [ctb], Brandon Bertelsen [ctb], Valerian Wrobel [ctb], Duccio Aiazzi [ctb]
Maintainer: Juergen Altfeld <jaltfeld@altfeld-im.de>

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Package trouBBlme4SolveR updated to version 0.1.4 with previous version 0.1.3 dated 2025-11-29

Title: Troubles Solver for 'lme4'
Description: The main function of the package aims to update 'lmer()'/'glmer()' models depending on their warnings, so trying to avoid convergence and singularity problems.
Author: Iago Gine-Vazquez [aut, cre]
Maintainer: Iago Gine-Vazquez <iago.gin-vaz@protonmail.com>

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Package nzilbb.vowels readmission to version 0.4.2 with previous version 0.4.1 dated 2025-05-06

Title: Vowel Covariation Tools
Description: Tools to support research on vowel covariation. Methods are provided to support Principal Component Analysis workflows (as in Brand et al. (2021) <doi:10.1016/j.wocn.2021.101096> and Wilson Black et al. (2023) <doi:10.1515/lingvan-2022-0086>).
Author: Joshua Wilson Black [aut, cre, cph] , James Brand [aut]
Maintainer: Joshua Wilson Black <joshua.black@canterbury.ac.nz>

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Package joyn updated to version 0.3.0 with previous version 0.2.4 dated 2024-12-13

Title: Tool for Diagnosis of Tables Joins and Complementary Join Features
Description: Tool for diagnosing table joins. It combines the speed of `collapse` and `data.table`, the flexibility of `dplyr`, and the diagnosis and features of the `merge` command in `Stata`.
Author: R.Andres Castaneda [aut, cre], Zander Prinsloo [aut], Rossana Tatulli [aut]
Maintainer: R.Andres Castaneda <acastanedaa@worldbank.org>

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Package funviewR updated to version 0.1.1 with previous version 0.1.0 dated 2025-11-09

Title: Visualize Function Call Dependencies in R Source Code
Description: Provides tools to analyze R source code and detect function definitions and their internal dependencies across multiple files. Creates interactive network visualizations using 'visNetwork' to display function call relationships, with detailed tooltips showing function arguments, return values, and documentation. Supports both individual files and directory-based analysis with automatic file detection. Useful for understanding code structure, identifying dependencies, and documenting R projects.
Author: Chathura Jayalath [aut, cre]
Maintainer: Chathura Jayalath <acj.chathura@gmail.com>

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Package ecoCopula updated to version 1.0.6 with previous version 1.0.2 dated 2022-03-01

Title: Graphical Modelling and Ordination using Copulas
Description: Creates 'graphs' of species associations (interactions) and ordination biplots from co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in Popovic, GC., Hui, FKC., Warton, DI., (2018) <doi:10.1016/j.jmva.2017.12.002>.
Author: Gordana Popovic [aut], David Warton [ctb, cre], Francis K.C. Hui [ctb], Michelle Lim [ctb]
Maintainer: David Warton <david.warton@unsw.edu.au>

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Package mllrnrs updated to version 0.0.7 with previous version 0.0.6 dated 2025-09-08

Title: R6-Based ML Learners for 'mlexperiments'
Description: Enhances 'mlexperiments' <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: 'glmnet' <https://CRAN.R-project.org/package=glmnet>, 'ranger' <https://CRAN.R-project.org/package=ranger>, 'xgboost' <https://CRAN.R-project.org/package=xgboost>, and 'lightgbm' <https://CRAN.R-project.org/package=lightgbm>. These can be used directly with the 'mlexperiments' R package.
Author: Lorenz A. Kapsner [cre, aut, cph]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner@gmail.com>

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Package VizTest updated to version 0.6 with previous version 0.5 dated 2025-08-29

Title: Optimal Confidence Intervals for Visual Testing
Description: Identifies the optimal confidence level to represent the results of a set of pairwise tests as suggested by Armstrong and Poirier (2025) <doi:10.1017/pan.2024.24>.
Author: Dave Armstrong [aut, cre] , William Poirier [aut]
Maintainer: Dave Armstrong <davearmstrong.ps@gmail.com>

Diff between VizTest versions 0.5 dated 2025-08-29 and 0.6 dated 2025-12-04

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Package VGAM updated to version 1.1-14 with previous version 1.1-13 dated 2025-02-12

Title: Vector Generalized Linear and Additive Models
Description: An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecolog [...truncated...]
Author: Thomas Yee [aut, cre] , Cleve Moler [ctb]
Maintainer: Thomas Yee <t.yee@auckland.ac.nz>

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Package VertexWiseR updated to version 1.4.4 with previous version 1.4.3 dated 2025-10-21

Title: Simplified Vertex-Wise Analyses of Whole-Brain and Hippocampal Surface
Description: Provides functions to run statistical analyses on surface-based neuroimaging data, computing measures including cortical thickness and surface area of the whole-brain and of the hippocampi. It can make use of 'FreeSurfer', 'fMRIprep', 'XCP-D', 'HCP' and 'CAT12' preprocessed datasets and 'HippUnfold' hippocampal segmentation outputs for a given sample by restructuring the data values into a single file. The single file can then be used by the package for analyses independently from its base dataset and without need for its access.
Author: Junhong Yu [aut] , Charly Billaud [aut, cre]
Maintainer: Charly Billaud <charly.billaud@ntu.edu.sg>

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Package veesa updated to version 0.1.7 with previous version 0.1.6 dated 2025-01-17

Title: Pipeline for Explainable Machine Learning with Functional Data
Description: Implements the Variable importance Explainable Elastic Shape Analysis pipeline for explainable machine learning with functional data inputs. Converts training and testing data functional inputs to elastic shape analysis principal components that account for vertical and/or horizontal variability. Computes feature importance to identify important principal components and visualizes variability captured by functional principal components. See Goode et al. (2025) <doi:10.48550/arXiv.2501.07602> for technical details about the methodology.
Author: Katherine Goode [cre, aut], J. Derek Tucker [aut], Sandia National Laboratories [cph, fnd]
Maintainer: Katherine Goode <kjgoode@sandia.gov>

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Package vaRiskScore updated to version 2.0.0 with previous version 1.1.0 dated 2023-05-31

Title: VA CVD Risk Score
Description: Estimates the predicted 10-year cardiovascular (CVD) risk score (in probability) for civilian women, women military service members and veterans by inputting patient profiles. The proposed women CVD risk score improves the accuracy of the existing American College of Cardiology/American Heart Association CVD risk assessment tool in predicting long‐term CVD risk for VA women, particularly in young and racial/ethnic minority women. See the reference: Jeon‐Slaughter, H., Chen, X., Tsai, S., Ramanan, B., & Ebrahimi, R. (2021) <doi:10.1161/JAHA.120.019217>.
Author: Xiaofei Chen [aut, cre], Haekyung Jeon-Slaughter [aut]
Maintainer: Xiaofei Chen <bill.nj08873@gmail.com>

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Package tsmp updated to version 0.4.16 with previous version 0.4.15 dated 2022-08-20

Title: Time Series with Matrix Profile
Description: A toolkit implementing the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Author: Francisco Bischoff [aut, cre] , Michael Yeh [res, ccp, ctb] , Diego Silva [res, ccp, ctb] , Yan Zhu [res, ccp, ctb] , Hoang Dau [res, ccp, ctb] , Michele Linardi [res, ccp, ctb]
Maintainer: Francisco Bischoff <fbischoff@med.up.pt>

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Package officer updated to version 0.7.2 with previous version 0.7.1 dated 2025-11-14

Title: Manipulation of Microsoft Word and PowerPoint Documents
Description: Access and manipulate 'Microsoft Word', 'RTF' and 'Microsoft PowerPoint' documents from R. The package focuses on tabular and graphical reporting from R; it also provides two functions that let users get document content into data objects. A set of functions lets add and remove images, tables and paragraphs of text in new or existing documents. The package does not require any installation of Microsoft products to be able to write Microsoft files.
Author: David Gohel [aut, cre], Stefan Moog [aut], Mark Heckmann [aut] , ArData [cph], Frank Hangler [ctb] , Liz Sander [ctb] , Anton Victorson [ctb] , Jon Calder [ctb] , John Harrold [ctb] , John Muschelli [ctb] , Bill Denney [ctb] , Nikolai Beck [ctb] , Gr [...truncated...]
Maintainer: David Gohel <david.gohel@ardata.fr>

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Package maplegend updated to version 0.4.0 with previous version 0.3.0 dated 2025-06-30

Title: Legends for Maps
Description: Create legends for maps and other graphics. Thematic maps need to be accompanied by legible legends to be fully comprehensible. This package offers a wide range of legends useful for cartography, some of which may also be useful for other types of graphics.
Author: Timothee Giraud [cre, aut]
Maintainer: Timothee Giraud <timothee.giraud@cnrs.fr>

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Package drhutools updated to version 1.0.1 with previous version 1.0.0 dated 2024-12-05

Title: Political Science Academic Research Gears
Description: Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, and set up color-blind palette, functions used in academic research of political psychology or political science in general.
Author: Yue Hu [aut, cre], Qian Qiu [ctb], Wen Deng [ctb]
Maintainer: Yue Hu <yuehu@tsinghua.edu.cn>

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Package DRDID updated to version 1.2.3 with previous version 1.2.2 dated 2025-05-30

Title: Doubly Robust Difference-in-Differences Estimators
Description: Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.
Author: Pedro H. C. Sant'Anna [aut, cre, cph], Jun Zhao [aut]
Maintainer: Pedro H. C. Sant'Anna <pedrohcgs@gmail.com>

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More information about DRDID at CRAN
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Package Compositional updated to version 8.0 with previous version 7.9 dated 2025-11-18

Title: Compositional Data Analysis
Description: Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451>. b) Tsagris M. (2014). "The k-NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. <doi:10.6339/JDS.201407_12(3).0008>. c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. <doi:10.48550/arXiv.15 [...truncated...]
Author: Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb], Christos Adam [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>

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More information about Compositional at CRAN
Permanent link

Package cardx updated to version 0.3.1 with previous version 0.3.0 dated 2025-08-27

Title: Extra Analysis Results Data Utilities
Description: Create extra Analysis Results Data (ARD) summary objects. The package supplements the simple ARD functions from the 'cards' package, exporting functions to put statistical results in the ARD format. These objects are used and re-used to construct summary tables, visualizations, and written reports.
Author: Daniel D. Sjoberg [aut, cre] , Abinaya Yogasekaram [aut], Emily de la Rua [aut], Malcolm Barrett [ctb] , F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

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Wed, 03 Dec 2025

Package rsynthbio updated to version 4.0.0 with previous version 3.0.2 dated 2025-10-29

Title: Synthesize Bio API Wrapper
Description: Access Synthesize Bio models from their API <https://app.synthesize.bio/> using this wrapper that provides a convenient interface to the Synthesize Bio API, allowing users to generate realistic gene expression data based on specified biological conditions. This package enables researchers to easily access AI-generated transcriptomic data for various modalities including bulk RNA-seq, single-cell RNA-seq, microarray data, and more.
Author: Synthesize Bio [aut, cre]
Maintainer: Synthesize Bio <candace@synthesize.bio>

Diff between rsynthbio versions 3.0.2 dated 2025-10-29 and 4.0.0 dated 2025-12-03

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More information about rsynthbio at CRAN
Permanent link

Package randotools updated to version 0.2.4 with previous version 0.2.3 dated 2025-11-07

Title: Create Randomization Lists
Description: Randomization lists are an integral component of randomized clinical trials. 'randotools' provides tools to easily create such lists.
Author: Alan G Haynes [aut, cre]
Maintainer: Alan G Haynes <alan.haynes@unibe.ch>

Diff between randotools versions 0.2.3 dated 2025-11-07 and 0.2.4 dated 2025-12-03

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More information about randotools at CRAN
Permanent link

Package inshiny updated to version 0.1.1 with previous version 0.1.0 dated 2025-09-09

Title: Compact Inline Widgets for 'shiny' Apps
Description: Provides a basic set of compact widgets for 'shiny' apps which occupy less space and can appear inline with surrounding text.
Author: Nick Davies [aut, cre]
Maintainer: Nick Davies <nicholas.davies@lshtm.ac.uk>

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More information about inshiny at CRAN
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Package binspp updated to version 0.2.3 with previous version 0.2.2 dated 2025-05-05

Title: Bayesian Inference for Neyman-Scott Point Processes
Description: The Bayesian MCMC estimation of parameters for Thomas-type cluster point process with various inhomogeneities. It allows for inhomogeneity in (i) distribution of parent points, (ii) mean number of points in a cluster, (iii) cluster spread. The package also allows for the Bayesian MCMC algorithm for the homogeneous generalized Thomas process. The cluster size is allowed to have a variance that is greater or less than the expected value (cluster sizes are over or under dispersed). Details are described in Dvořák, Remeš, Beránek & Mrkvička (2022) <arXiv: 10.48550/arXiv.2205.07946>.
Author: Mrkvicka Tomas [aut], Dvorak Jiri [aut], Beranek Ladislav [aut], Remes Radim [aut, cre], Park Jaewoo [ctb], Lee Sujeong [ctb]
Maintainer: Remes Radim <inrem@jcu.cz>

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Package teal.modules.general updated to version 0.6.0 with previous version 0.5.1 dated 2025-10-22

Title: General Modules for 'teal' Applications
Description: Prebuilt 'shiny' modules containing tools for viewing data, visualizing data, understanding missing and outlier values within your data and performing simple data analysis. This extends 'teal' framework that supports reproducible research and analysis.
Author: Dawid Kaledkowski [aut, cre], Pawel Rucki [aut], Mahmoud Hallal [aut], Ondrej Slama [ctb], Maciej Nasinski [aut], Konrad Pagacz [aut], Nikolas Burkoff [aut], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Dawid Kaledkowski <dawid.kaledkowski@roche.com>

Diff between teal.modules.general versions 0.5.1 dated 2025-10-22 and 0.6.0 dated 2025-12-03

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Package SpecHelpers updated to version 0.3.2 with previous version 0.3.1 dated 2025-03-06

Title: Spectroscopy Related Utilities
Description: Utility functions for spectroscopy. 1. Functions to simulate spectra for use in teaching or testing. 2. Functions to process files created by 'LoggerPro' and 'SpectraSuite' software.
Author: Bryan A. Hanson [aut, cre]
Maintainer: Bryan A. Hanson <hanson@depauw.edu>

Diff between SpecHelpers versions 0.3.1 dated 2025-03-06 and 0.3.2 dated 2025-12-03

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Package simest readmission to version 0.4-1-1 with previous version 0.4 dated 2017-04-25

Title: Constrained Single Index Model Estimation
Description: Estimation of function and index vector in single index model ('sim') with (and w/o) shape constraints including different smoothness conditions. See, e.g., Kuchibhotla and Patra (2020) <doi:10.3150/19-BEJ1183>.
Author: Arun Kumar Kuchibhotla [aut] , Rohit Kumar Patra [aut], Martin Maechler [ctb, cre]
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

This is a re-admission after prior archival of version 0.4 dated 2017-04-25

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Package radsafer updated to version 2.4.0 with previous version 2.3.0 dated 2023-07-12

Title: Radiation Safety
Description: Provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Wern [...truncated...]
Author: Mark Hogue [aut, cre]
Maintainer: Mark Hogue <mark.hogue.chp@gmail.com>

Diff between radsafer versions 2.3.0 dated 2023-07-12 and 2.4.0 dated 2025-12-03

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Package VGAMextra updated to version 0.0-9 with previous version 0.0-8 dated 2025-08-21

Title: Additions and Extensions of the 'VGAM' Package
Description: Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All fun [...truncated...]
Author: Victor Miranda [aut, cre, cph], Thomas Yee [ctb, ths, cph]
Maintainer: Victor Miranda <victor.miranda@aut.ac.nz>

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Permanent link

Package HQM updated to version 1.1 with previous version 1.0 dated 2025-02-01

Title: Superefficient Estimation of Future Conditional Hazards Based on Marker Information
Description: Provides univariate and indexed (multivariate) nonparametric smoothed kernel estimators for the future conditional hazard rate function when time-dependent covariates are present, a bandwidth selector for the estimator's implementation and pointwise and uniform confidence bands. Methods used in the package refer to Bagkavos, Isakson, Mammen, Nielsen and Proust-Lima (2025) <doi:10.1093/biomet/asaf008>.
Author: Dimitrios Bagkavos [aut, cre], Alex Isakson [ctb], Enno Mammen [ctb], Jens Nielsen [ctb], Cecile Proust-Lima [ctb]
Maintainer: Dimitrios Bagkavos <dimitrios.bagkavos@gmail.com>

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Package Rdistance updated to version 4.1.1 with previous version 4.0.5 dated 2025-04-10

Title: Density and Abundance from Distance-Sampling Surveys
Description: Distance-sampling (<doi:10.1007/978-3-319-19219-2>) is a field survey and analytical method that estimates density and abundance of survey targets (e.g., animals) when detection probability declines with observation distance. Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Analysis involves fitting smooth (parametric) curves to histograms of observation distances and using those functions to adjust density estimates for missed targets. Routines included here fit curves to observation distance histograms, estimate effective sampling area, density of targets in surveyed areas, and the abundance of targets in a surrounding study area. Confidence interval estimation uses built-in bootstrap resampling. Help files are extensive and have been vetted by multiple authors. Many tutorials are available on the package's website (URL below).
Author: Trent McDonald [cre, aut], Jason Carlisle [aut], Aidan McDonald [aut] , Ryan Nielson [ctb] , Ben Augustine [ctb] , James Griswald [ctb] , Patrick McKann [ctb] , Lacey Jeroue [ctb] , Hoffman Abigail [ctb] , Kleinsausser Michael [ctb] , Joel Reynolds [ [...truncated...]
Maintainer: Trent McDonald <trent@mcdonalddatasciences.com>

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More information about Rdistance at CRAN
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Package PRIMAL updated to version 1.0.3 with previous version 1.0.2 dated 2020-01-22

Title: Parametric Simplex Method for Sparse Learning
Description: Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.
Author: Zichong Li [aut, cre], Qianli Shen [aut]
Maintainer: Zichong Li <zichongli5@gmail.com>

Diff between PRIMAL versions 1.0.2 dated 2020-01-22 and 1.0.3 dated 2025-12-03

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Package sdstudio updated to version 0.1.4 with previous version 0.1.3 dated 2025-11-11

Title: Companion Application for the 'surveydown' Survey Platform
Description: Companion package that supports the 'surveydown' survey platform (<https://surveydown.org>). The default method for working with a 'surveydown' survey is to edit the plain text 'survey.qmd' and 'app.R' files. With 'sdstudio', you can create, preview and manage surveys with a 'shiny' application as a graphical user interface.
Author: Pingfan Hu [aut, cre, cph] , John Paul Helveston [aut, cph] , Bogdan Bunea [aut, cph]
Maintainer: Pingfan Hu <pingfan0727@gmail.com>

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Package Ryacas updated to version 1.1.6 with previous version 1.1.5 dated 2023-01-16

Title: R Interface to the 'Yacas' Computer Algebra System
Description: Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
Author: Mikkel Meyer Andersen [aut, cre, cph], Rob Goedman [aut, cph], Gabor Grothendieck [aut, cph], Soeren Hoejsgaard [aut, cph], Grzegorz Mazur [aut, cph], Ayal Pinkus [aut, cph], Nemanja Trifunovic [cph] )
Maintainer: Mikkel Meyer Andersen <mikl@math.aau.dk>

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Package RPESE updated to version 1.2.6 with previous version 1.2.5 dated 2022-09-08

Title: Estimates of Standard Errors for Risk and Performance Measures
Description: Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.
Author: Anthony Christidis [aut, cre], Xin Chen [aut]
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>

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Package panstarrs updated to version 0.2.3 with previous version 0.2.2 dated 2024-05-02

Title: Interface to the Pan-STARRS API
Description: An interface to the API for 'Pan-STARRS1', a data archive of the PS1 wide-field astronomical survey. The package allows access to the PS1 catalog and to the PS1 images. (see <https://outerspace.stsci.edu/display/PANSTARRS/> for more information). You can use it to plan astronomical observations, make guidance pictures, find magnitudes in five broadband filters (g, r, i, z, y) and more.
Author: Grigory Uskov [cre, aut]
Maintainer: Grigory Uskov <uskov.russia@gmail.com>

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Package nntmvn updated to version 1.3.0 with previous version 1.2.0 dated 2025-02-08

Title: Draw Samples of Truncated Multivariate Normal Distributions
Description: Draw samples from truncated multivariate normal distribution using the sequential nearest neighbor (SNN) method introduced in "Scalable Sampling of Truncated Multivariate Normals Using Sequential Nearest-Neighbor Approximation" <doi:10.48550/arXiv.2406.17307>.
Author: Jian Cao [aut, cre], Matthias Katzfuss [aut]
Maintainer: Jian Cao <jcao2416@gmail.com>

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Package mlr3 updated to version 1.3.0 with previous version 1.2.0 dated 2025-09-13

Title: Machine Learning in R - Next Generation
Description: Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.
Author: Michel Lang [aut] , Bernd Bischl [aut] , Jakob Richter [aut] , Patrick Schratz [aut] , Giuseppe Casalicchio [ctb] , Stefan Coors [ctb] , Quay Au [ctb] , Martin Binder [aut], Florian Pfisterer [aut] , Raphael Sonabend [aut] , Lennart Schneider [ctb] , [...truncated...]
Maintainer: Marc Becker <marcbecker@posteo.de>

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Package ICSKAT updated to version 0.3.0 with previous version 0.2.0 dated 2021-11-25

Title: Interval-Censored Sequence Kernel Association Test
Description: Implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide. The paper describing these methods is " Infer [...truncated...]
Author: Ryan Sun [aut, cre], Liang Zhu [aut]
Maintainer: Ryan Sun <ryansun.work@gmail.com>

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Package hdcuremodels updated to version 0.0.6 with previous version 0.0.5 dated 2025-07-31

Title: High-Dimensional Cure Models
Description: Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.
Author: Han Fu [aut], Kellie J. Archer [aut, cre] , Tung Lam Nguyen [rev] , Panagiotis Papastamoulis [rev]
Maintainer: Kellie J. Archer <archer.43@osu.edu>

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Package distcomp updated to version 1.3-4 with previous version 1.3-3 dated 2022-09-01

Title: Computations over Distributed Data without Aggregation
Description: Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using 'opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R 'survival' package. (That is, the underlying Cox model code is derived from that in the R 'survival' package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via 'shiny') for the implemented methods to help in designing and deploying the computations.
Author: Balasubramanian Narasimhan [aut, cre], Marina Bendersky [aut], Sam Gross [aut], Terry M. Therneau [ctb], Thomas Lumley [ctb]
Maintainer: Balasubramanian Narasimhan <naras@stat.Stanford.EDU>

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Package sspm readmission to version 1.1.0 with previous version 1.0.3 dated 2025-02-12

Title: Spatial Surplus Production Model Framework for Northern Shrimp Populations
Description: Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).
Author: Valentin Lucet [aut, cre, cph], Eric Pedersen [aut]
Maintainer: Valentin Lucet <valentin.lucet@gmail.com>

This is a re-admission after prior archival of version 1.0.3 dated 2025-02-12

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Package reconstructKM updated to version 0.4.0 with previous version 0.3.0 dated 2020-11-25

Title: Reconstruct Individual-Level Data from Published KM Plots
Description: Functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). The individual-level data can be used for re-analysis, meta-analysis, methodology development, etc. This package was used to generate the data for commentary such as Sun, Rich, & Wei (2018) <doi:10.1056/NEJMc1808567>. Please see the vignette for a quickstart guide.
Author: Ryan Sun [aut, cre]
Maintainer: Ryan Sun <ryansun.work@gmail.com>

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Package rcdf readmission to version 0.1.2 with previous version 0.1.1 dated 2025-10-12

Title: A Comprehensive Toolkit for Working with Encrypted Parquet Files
Description: Utilities for reading, writing, and managing RCDF files, including encryption and decryption support. It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) encryption.
Author: Bhas Abdulsamad [aut, cre, cph]
Maintainer: Bhas Abdulsamad <aeabdulsamad@gmail.com>

This is a re-admission after prior archival of version 0.1.1 dated 2025-10-12

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Package openesm updated to version 0.1.2 with previous version 0.1.1 dated 2025-11-02

Title: Access the Open Experience Sampling Method Database
Description: Provides programmatic access to the Open Experience Sampling Method ('openESM') database (<https://openesmdata.org>), a collection of harmonized experience sampling datasets. The package enables researchers to discover, download, and work with the datasets while ensuring proper citation and license compliance.
Author: Bjoern S. Siepe [aut, cre, cph] , Matthias Kloft [aut]
Maintainer: Bjoern S. Siepe <bjoernsiepe@gmail.com>

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Package MHD readmission to version 0.1.3 with previous version 0.1.2 dated 2024-01-24

Title: Metric Halfspace Depth
Description: Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean data. Implementing the method described in Dai and Lopez-Pintado (2022) <doi:10.1080/01621459.2021.2011298>.
Author: Xiongtao Dai [aut, cre]
Maintainer: Xiongtao Dai <xiongtao.dai@hotmail.com>

This is a re-admission after prior archival of version 0.1.2 dated 2024-01-24

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Package MetabolomicsBasics updated to version 1.4.7 with previous version 1.4.5 dated 2024-01-31

Title: Basic Functions to Investigate Metabolomics Data Matrices
Description: A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication <doi:10.1007/978-1-4939-7819-9_20>.
Author: Jan Lisec [aut, cre]
Maintainer: Jan Lisec <jan.lisec@bam.de>

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Package GitAI readmission to version 0.1.3 with previous version 0.1.2 dated 2025-08-22

Title: Extracts Knowledge from 'Git' Repositories
Description: Scan multiple 'Git' repositories, pull specified files content and process it with large language models. You can summarize the content in specific way, extract information and data, or find answers to your questions about the repositories. The output can be stored in vector database and used for semantic search or as a part of a RAG (Retrieval Augmented Generation) prompt.
Author: Kamil Wais [aut, cre], Krystian Igras [aut], Maciej Banas [aut]
Maintainer: Kamil Wais <kamil.wais@gmail.com>

This is a re-admission after prior archival of version 0.1.2 dated 2025-08-22

Diff between GitAI versions 0.1.2 dated 2025-08-22 and 0.1.3 dated 2025-12-03

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Package DIDmultiplegtDYN updated to version 2.2.0 with previous version 2.1.2 dated 2025-01-22

Title: Estimation in Difference-in-Difference Designs with Multiple Groups and Periods
Description: Estimation of heterogeneity-robust difference-in-differences estimators, with a binary, discrete, or continuous treatment, in designs where past treatments may affect the current outcome.
Author: Anzony Quispe [aut, cre], Diego Ciccia [aut], Felix Knau [aut], Melitine Malezieux [aut], Doulo Sow [aut], Clement de Chaisemartin [aut]
Maintainer: Anzony Quispe <anzony.quispe@gmail.com>

Diff between DIDmultiplegtDYN versions 2.1.2 dated 2025-01-22 and 2.2.0 dated 2025-12-03

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Package rdav updated to version 0.3.0 with previous version 0.2.2 dated 2025-11-13

Title: Simple 'WebDAV' Client
Description: A simple 'WebDAV' client that provides functions to fetch and send files or folders to servers using the 'WebDAV' protocol (see 'RFC' 4918 <https://www.rfc-editor.org/rfc/rfc4918>). Only a subset of the protocol is implemented (e.g. file locks are not yet supported).
Author: Gunther Krauss [aut, cre]
Maintainer: Gunther Krauss <guntherkrauss@uni-bonn.de>

Diff between rdav versions 0.2.2 dated 2025-11-13 and 0.3.0 dated 2025-12-03

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Package matrixCorr updated to version 0.7.2 with previous version 0.6.2 dated 2025-11-15

Title: Collection of Correlation and Association Estimators
Description: Compute correlation and other association matrices from small to high-dimensional datasets with relative simple functions and sensible defaults. Includes options for shrinkage and robustness to improve results in noisy or high-dimensional settings (p >= n), plus convenient print/plot methods for inspection. Implemented with optimised C++ backends using BLAS/OpenMP and memory-aware symmetric updates. Works with base matrices and data frames, returning standard R objects via a consistent S3 interface. Useful across genomics, agriculture, and machine-learning workflows. Supports Pearson, Spearman, Kendall, distance correlation, partial correlation, and robust biweight mid-correlation; Bland–Altman analyses and Lin's concordance correlation coefficient (including repeated-measures extensions). Methods based on Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>; Schäfer and Strimmer (2005) <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>.
Author: Thiago de Paula Oliveira [aut, cre]
Maintainer: Thiago de Paula Oliveira <thiago.paula.oliveira@gmail.com>

Diff between matrixCorr versions 0.6.2 dated 2025-11-15 and 0.7.2 dated 2025-12-03

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Package REBayes updated to version 2.59 with previous version 2.56 dated 2024-06-22

Title: Empirical Bayes Estimation and Inference
Description: Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, <DOI:10.18637/jss.v082.i08>.
Author: Roger Koenker [aut, cre], Jiaying Gu [ctb], Ivan Mizera [ctb]
Maintainer: Roger Koenker <rkoenker@uiuc.edu>

Diff between REBayes versions 2.56 dated 2024-06-22 and 2.59 dated 2025-12-03

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Package pyinit updated to version 1.1.4 with previous version 1.1.3 dated 2022-04-26

Title: Pena-Yohai Initial Estimator for Robust S-Regression
Description: Deterministic Pena-Yohai initial estimator for robust S estimators of regression. The procedure is described in detail in Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
Author: David Kepplinger [aut, cre], Matias Salibian-Barrera [aut], Gabriela Cohen Freue [aut]
Maintainer: David Kepplinger <david.kepplinger@gmail.com>

Diff between pyinit versions 1.1.3 dated 2022-04-26 and 1.1.4 dated 2025-12-03

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