Mon, 22 May 2023

Package glmm.hp updated to version 0.1-0 with previous version 0.0-9 dated 2023-04-12

Title: Hierarchical Partitioning of Marginal R2 for Generalized Mixed-Effect Models
Description: Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.
Author: Jiangshan Lai [aut, cre] , Kim Nimon [aut]
Maintainer: Jiangshan Lai <lai@njfu.edu.cn>

Diff between glmm.hp versions 0.0-9 dated 2023-04-12 and 0.1-0 dated 2023-05-22

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Package WienR updated to version 0.3-10 with previous version 0.3-9 dated 2023-04-23

Title: Derivatives of the First-Passage Time Density and Cumulative Distribution Function, and Random Sampling from the (Truncated) First-Passage Time Distribution
Description: First, we provide functions to calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF) with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0, the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable. Since the PDF, CDF, and their derivatives are represented as infinite series, we give the user the option to control the approximation errors with the argument 'precision'. For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) <https://github.com/stevengj/cubature>. Numerical integration is required whenever sv, sw, and [...truncated...]
Author: Raphael Hartmann [aut, cre], Karl C. Klauer [cph, aut, ctb, ths], Steven G. Johnson [ctb], Jean M. Linhart [ctb], Brian Gough [ctb], Gerard Jungman [ctb], Rudolf Schuerer [ctb], Przemyslaw Sliwa [ctb], Jason H. Stover [ctb]
Maintainer: Raphael Hartmann <raphael.hartmann@protonmail.com>

Diff between WienR versions 0.3-9 dated 2023-04-23 and 0.3-10 dated 2023-05-22

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Package enmSdmX updated to version 1.0.6 with previous version 1.0.4 dated 2023-04-19

Title: Species Distribution Modeling and Ecological Niche Modeling
Description: Implements species distribution modeling and ecological niche modeling, including: bias correction, spatial cross-validation, model evaluation, raster interpolation, biotic "velocity" (speed and direction of movement of a "mass" represented by a raster), interpolating across a time series of rasters, and use of spatially imprecise records. The heart of the package is a set of "training" functions which automatically optimize model complexity based number of available occurrences. These algorithms include MaxEnt, MaxNet, boosted regression trees/gradient boosting machines, generalized additive models, generalized linear models, natural splines, and random forests. To enhance interoperability with other modeling packages, no new classes are created. The package works with 'PROJ6' geodetic objects and coordinate reference systems.
Author: Adam B. Smith [cre, aut]
Maintainer: Adam B. Smith <adam.smith@mobot.org>

Diff between enmSdmX versions 1.0.4 dated 2023-04-19 and 1.0.6 dated 2023-05-22

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Package eBsc updated to version 4.17 with previous version 4.16 dated 2023-04-26

Title: "Empirical Bayes Smoothing Splines with Correlated Errors"
Description: Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021) <arXiv:1812.06948> for details.
Author: Francisco Rosales, Tatyana Krivobokova, Paulo Serra.
Maintainer: Francisco Rosales <francisco.rosales-marticorena@protonmail.com>

Diff between eBsc versions 4.16 dated 2023-04-26 and 4.17 dated 2023-05-22

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Package crew updated to version 0.2.1 with previous version 0.1.1 dated 2023-04-21

Title: A Distributed Worker Launcher Framework
Description: In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'NNG'-powered 'mirai' R package by Gao (2023) <https://CRAN.R-project.org/package=mirai> is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The 'crew' package extends 'mirai' with a unifying interface for third-party worker launchers. Inspiration also comes from packages. 'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>, 'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>), and 'batchtools' by Lang, Bischel, and Surmann (2017) <doi:10.21105/joss.00135>.
Author: William Michael Landau [aut, cre] , Daniel Woodie [ctb], Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau@gmail.com>

Diff between crew versions 0.1.1 dated 2023-04-21 and 0.2.1 dated 2023-05-22

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More information about crew at CRAN
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Package soundgen updated to version 2.6.0 with previous version 2.5.3 dated 2022-12-11

Title: Sound Synthesis and Acoustic Analysis
Description: Performs parametric synthesis of sounds with harmonic and noise components such as animal vocalizations or human voice. Also offers tools for audio manipulation and acoustic analysis, including pitch tracking, spectral analysis, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for synthesizing and annotating audio, manually correcting pitch contours, and measuring formant frequencies. Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.
Author: Andrey Anikin [aut, cre]
Maintainer: Andrey Anikin <rty.anik@rambler.ru>

Diff between soundgen versions 2.5.3 dated 2022-12-11 and 2.6.0 dated 2023-05-22

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Package rsatscan updated to version 1.0.4 with previous version 1.0.3 dated 2023-04-21

Title: Tools, Classes, and Methods for Interfacing with 'SaTScan' Stand-Alone Software
Description: 'SaTScan'(TM) <https://www.satscan.org> is software for finding regions in Time, Space, or Time-Space that have excess risk, based on scan statistics, and uses Monte Carlo hypothesis testing to generate P-values for these regions. The 'rsatscan' package provides functions for writing R data frames in 'SaTScan'-readable formats, for setting 'SaTScan' parameters, for running 'SaTScan' in the OS, and for reading the files that 'SaTScan' creates.
Author: Ken Kleinman [aut], Scott Hostovich [cre], Amer Moosa [ctb]
Maintainer: Scott Hostovich <HostovichS@imsweb.com>

Diff between rsatscan versions 1.0.3 dated 2023-04-21 and 1.0.4 dated 2023-05-22

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Package Require updated to version 0.3.1 with previous version 0.3.0 dated 2023-03-14

Title: Installing and Loading R Packages for Reproducible Workflows
Description: A single key function, 'Require' that makes rerun-tolerant versions of 'install.packages' and `require` for CRAN packages, packages no longer on CRAN (i.e., archived), specific versions of packages, and GitHub packages. This approach is developed to create reproducible workflows that are flexible and fast enough to use while in development stages, while able to build snapshots once a stable package collection is found. As with other functions in a reproducible workflow, this package emphasizes functions that return the same result whether it is the first or subsequent times running the function, with subsequent times being sufficiently fast that they can be run every time without undue waiting burden on the user or developer.
Author: Eliot J B McIntire [aut, cre] , Alex M Chubaty [ctb] , Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada [cph]
Maintainer: Eliot J B McIntire <eliot.mcintire@canada.ca>

Diff between Require versions 0.3.0 dated 2023-03-14 and 0.3.1 dated 2023-05-22

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Package matsbyname updated to version 0.6.3 with previous version 0.6.1 dated 2023-04-25

Title: An Implementation of Matrix Mathematics
Description: An implementation of matrix mathematics wherein operations are performed "by name."
Author: Matthew Heun [aut, cre]
Maintainer: Matthew Heun <matthew.heun@me.com>

Diff between matsbyname versions 0.6.1 dated 2023-04-25 and 0.6.3 dated 2023-05-22

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Package tram updated to version 0.8-3 with previous version 0.8-2 dated 2023-03-28

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

Diff between tram versions 0.8-2 dated 2023-03-28 and 0.8-3 dated 2023-05-22

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Package ggdag updated to version 0.2.9 with previous version 0.2.8 dated 2023-03-17

Title: Analyze and Create Elegant Directed Acyclic Graphs
Description: Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses the 'DAGitty' web tool (<http://dagitty.net>) for creating and analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.
Author: Malcolm Barrett [aut, cre]
Maintainer: Malcolm Barrett <malcolmbarrett@gmail.com>

Diff between ggdag versions 0.2.8 dated 2023-03-17 and 0.2.9 dated 2023-05-22

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 NEWS.md                                                                                       |    4 
 R/adjustment_sets.R                                                                           |    5 
 R/instrumental.R                                                                              |   37 ++
 R/utils.R                                                                                     |    2 
 README.md                                                                                     |    2 
 inst/WORDLIST                                                                                 |    1 
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 inst/doc/intro-to-ggdag.html                                                                  |  115 ++++-----
 man/control_for.Rd                                                                            |    6 
 man/geom_dag_text.Rd                                                                          |    2 
 man/ggdag-package.Rd                                                                          |    6 
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 tests/testthat/test-instrumental.R                                                            |   28 ++
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New package sewage with initial version 0.2.5
Package: sewage
Title: A Light-Weight Data Pipelining Tool
Version: 0.2.5
Description: Provides a simple interface to developing complex data pipelines which can be executed in a single call. 'sewage' makes it easy to test, debug, and share data pipelines through it's interface and visualizations.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/mwhalen18/sewage, https://mwhalen18.github.io/sewage/
BugReports: https://github.com/mwhalen18/sewage/issues
Suggests: testthat (>= 3.0.0), dplyr
Imports: DiagrammeR, glue, cli
NeedsCompilation: no
Packaged: 2023-05-22 11:35:55 UTC; matthewwhalen
Author: Matthew Whalen [aut, cre, cph]
Maintainer: Matthew Whalen <matthew.whalen18@gmail.com>
Repository: CRAN
Date/Publication: 2023-05-22 18:10:02 UTC

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New package MultiGroupSequential with initial version 1.0.0
Package: MultiGroupSequential
Version: 1.0.0
Date: 2023-05-20
Title: Group-Sequential Procedures with Multiple Hypotheses
Description: It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The 'MultiGroupSequential' package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) <doi:10.1080/19466315.2023.2191989> and the graphical procedure proposed by Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>. Luo and Quan (2013) describes three procedure and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel proced [...truncated...]
Depends: R (>= 4.0.0)
Imports: stats, mvtnorm, OpenMx, hommel
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2023-05-22 10:51:16 UTC; marvels
Author: Xiaodong Luo [aut, cre], Hui Quan [ctb], Sanofi [cph]
Maintainer: Xiaodong Luo <Xiaodong.Luo@sanofi.com>
Repository: CRAN
Date/Publication: 2023-05-22 18:10:06 UTC

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Package growthmodels updated to version 1.3.1 with previous version 1.2.0 dated 2013-11-23

Title: Nonlinear Growth Models
Description: A compilation of nonlinear growth models.
Author: Daniel Rodriguez
Maintainer: Daniel Rodriguez <daniel.rodriguez.perez@gmail.com>

Diff between growthmodels versions 1.2.0 dated 2013-11-23 and 1.3.1 dated 2023-05-22

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New package forestat with initial version 1.0.0
Package: forestat
Title: Evaluating the Quality of Natural Forest
Version: 1.0.0
Maintainer: Yuanyuan Han <jackhanyuan@foxmail.com>
Repository: CRAN
Description: Including tree height classification, tree height model establishment, sectional area growth and stock growth modeling, and calculation of actual and potential forest productivity.
License: GPL (>= 3)
Encoding: UTF-8
Language: en-US
LazyData: true
Depends: R (>= 2.10)
Imports: dplyr, ggplot2, graphics, nlme, stats
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-05-22 14:52:40 UTC; jackhanyuan
Author: Liyong Fu [aut], Zhong Wang [aut], Yuanyuan Han [aut, cre]
Date/Publication: 2023-05-22 18:20:02 UTC

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New package crossvalidationCP with initial version 1.1
Package: crossvalidationCP
Title: Cross-Validation for Change-Point Regression
Version: 1.1
Depends: R (>= 3.3.0)
Imports: changepoint (>= 2.0), fpopw(>= 1.1), wbs (>= 1.4), stats
Suggests: testthat (>= 2.0.0)
Description: Implements the cross-validation methodology from Pein and Shah (2021) <arXiv:2112.03220>. Can be customised by providing different cross-validation criteria, estimators for the change-point locations and local parameters, and freely chosen folds. Pre-implemented estimators and criteria are available. It also includes our own implementation of the COPPS procedure <doi:10.1214/19-AOS1814>.
License: GPL-3
NeedsCompilation: no
Packaged: 2023-05-22 16:07:52 UTC; pein
Author: Pein Florian [aut, cre]
Maintainer: Pein Florian <f.pein@lancaster.ac.uk>
Repository: CRAN
Date/Publication: 2023-05-22 18:30:02 UTC

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New package autoScorecard with initial version 0.1.0
Package: autoScorecard
Title: Fully Automatic Generation of Scorecards
Version: 0.1.0
Maintainer: Tai-Sen Zheng <jc3802201@gmail.com>
Description: Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. Including equal frequency binning, equidistant binning, K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, fully automatic generation of scorecards, etc. This package is designed to make scorecard development easier and faster. References include: 1. <http://shichen.name/posts/>. 2. Dong-feng Li(Peking University),Class PPT. 3. <https://zhuanlan.zhihu.com/p/389710022>. 4. <https://www.zhangshengrong.com/p/281oqR9JNw/>.
License: AGPL-3
Encoding: UTF-8
Imports: infotheo, ROCR, rpart, discretization, stats, graphics, corrplot
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2023-05-22 15:17:01 UTC; admin
Author: Tai-Sen Zheng [aut, cre]
Repository: CRAN
Date/Publication: 2023-05-22 18:30:05 UTC

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New package appeears with initial version 1.0
Package: appeears
Title: Interface to 'AppEARS' NASA Web Services
Version: 1.0
Description: Programmatic interface to the NASA Application for Extracting and Exploring Analysis Ready Samples services (AppEEARS; <https://appeears.earthdatacloud.nasa.gov/>). The package provides easy access to analysis ready earth observation data in R.
URL: https://github.com/bluegreen-labs/appeears
BugReports: https://github.com/bluegreen-labs/appeears/issues
Depends: R (>= 4.0)
Imports: httr, keyring, memoise, getPass, R6, sf, jsonlite, geojsonio, rstudioapi
License: AGPL-3
ByteCompile: true
Suggests: rmarkdown, covr, testthat, terra, ncdf4, knitr, rlang, dplyr, ggplot2, patchwork
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-05-22 12:30:53 UTC; khufkens
Author: Koen Hufkens [aut, cre] , Elio Campitelli [ctb] , BlueGreen Labs [cph, fnd]
Maintainer: Koen Hufkens <koen.hufkens@gmail.com>
Repository: CRAN
Date/Publication: 2023-05-22 18:20:05 UTC

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Package adw updated to version 0.3.1 with previous version 0.3.0 dated 2023-04-09

Title: Angular Distance Weighting Interpolation
Description: The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius.
Author: Panfeng Zhang [aut, cre] , Guoyu Ren [ctb], Yun Qin [ctb], Chenchen Ren [ctb], Xiang Zheng [ctb]
Maintainer: Panfeng Zhang <zhangpanfeng@cug.edu.cn>

Diff between adw versions 0.3.0 dated 2023-04-09 and 0.3.1 dated 2023-05-22

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Package scribe updated to version 0.2.0 with previous version 0.1.0 dated 2023-03-13

Title: Command Argument Parsing
Description: A base dependency solution with basic argument parsing for use with 'Rscript'.
Author: Jordan Mark Barbone [aut, cph, cre]
Maintainer: Jordan Mark Barbone <jmbarbone@gmail.com>

Diff between scribe versions 0.1.0 dated 2023-03-13 and 0.2.0 dated 2023-05-22

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Package oosse updated to version 1.0.5 with previous version 1.0.2 dated 2023-04-10

Title: Out-of-Sample R² with Standard Error Estimation
Description: Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.
Author: Stijn Hawinkel [cre, aut]
Maintainer: Stijn Hawinkel <stijn.hawinkel@psb.ugent.be>

Diff between oosse versions 1.0.2 dated 2023-04-10 and 1.0.5 dated 2023-05-22

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Package mlt updated to version 1.4-7 with previous version 1.4-6 dated 2023-04-14

Title: Most Likely Transformations
Description: Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>.
Author: Torsten Hothorn [aut, cre]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between mlt versions 1.4-6 dated 2023-04-14 and 1.4-7 dated 2023-05-22

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Package fuj updated to version 0.1.3 with previous version 0.1.2 dated 2023-03-06

Title: Functions and Utilities for Jordan
Description: Provides core functions and utilities for packages and other code developed by Jordan Mark Barbone.
Author: Jordan Mark Barbone [aut, cph, cre]
Maintainer: Jordan Mark Barbone <jmbarbone@gmail.com>

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Package onmaRg updated to version 0.2.1 with previous version 0.2.0 dated 2022-12-23

Title: Import Public Health Ontario's Ontario Marginalization Index
Description: The Ontario Marginalization Index is a socioeconomic model that is built on Statistics Canada census data. The model consists of four dimensions: Residential Instability, Material Deprivation, Dependency and Ethnic Concentration. Each of these dimensions are imported for a variety of geographic levels (DA, CD, etc.) for both the 2011 and 2016 administrations of the census (2021 pending). These data sets contribute to community analysis of equity with respect to Ontario's Anti-Racism Act. The Ontario Marginalization Index data is retrieved from the Public Health Ontario website: <https://www.publichealthontario.ca/en/data-and-analysis/health-equity/ontario-marginalization-index>. The shapefile data is retrieved from the Statistics Canada website: <https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-eng.cfm>.
Author: William Conley [aut, cre]
Maintainer: William Conley <william@cconley.ca>

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Package cNORM updated to version 3.0.3 with previous version 3.0.2 dated 2022-06-12

Title: Continuous Norming
Description: Conventional methods for producing standard scores or percentiles in psychometrics or biometrics are often plagued with 'jumps' or 'gaps' (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores. The continuous norming method introduced by A. Lenhard et al. (2016, <doi:10.1177/1073191116656437>; 2019, <doi:10.1371/journal.pone.0222279>; 2021 <doi: 10.1177/0013164420928457>) estimates percentile development (e. g. over age) and generates continuous test norm scores on the basis of the raw data from standardization samples, without requiring assumptions about the distribution of the raw data: Norm scores are directly established from raw data by modeling the latter ones as a function of both percentile scores and an explanatory variable (e.g., age). The method minimizes bias arising from sampling and measurement error, while handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost all of the varia [...truncated...]
Author: Alexandra Lenhard [aut] , Wolfgang Lenhard [cre, aut] , Sebastian Gary [aut], WPS publisher [fnd]
Maintainer: Wolfgang Lenhard <wolfgang.lenhard@uni-wuerzburg.de>

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Package vivainsights updated to version 0.3.1 with previous version 0.3.0 dated 2023-03-30

Title: Analyze and Visualize Data from 'Microsoft Viva Insights'
Description: Provides a versatile range of functions, including exploratory data analysis, time-series analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.
Author: Martin Chan [aut, cre], Carlos Morales [aut]
Maintainer: Martin Chan <martin.chan@microsoft.com>

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Package igraph updated to version 1.4.3 with previous version 1.4.2 dated 2023-04-07

Title: Network Analysis and Visualization
Description: Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.
Author: Gabor Csardi [aut], Tamas Nepusz [aut] , Vincent Traag [aut], Szabolcs Horvat [aut], Fabio Zanini [aut], Daniel Noom [aut], Kirill Mueller [aut, cre], Maelle Salmon [ctb], Chan Zuckerberg Initiative [fnd]
Maintainer: Kirill Mueller <kirill@cynkra.com>

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Package abn updated to version 2.7-5 with previous version 2.7-3 dated 2023-01-25

Title: Modelling Multivariate Data with Additive Bayesian Networks
Description: Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modeling; they generalises the usual multivariable regression, GLM, to multiple dependent variables. 'abn' provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data. The additive formulation of these models is equivalent to multivariate generalized linear modeling (including mixed models with iid random effects). The usual term to describe this model selection process is structure discovery. The core functionality is concerned with model selection - determi [...truncated...]
Author: Matteo Delucchi [cre] , Reinhard Furrer [aut] , Gilles Kratzer [aut] , Fraser Iain Lewis [aut] , Marta Pittavino [ctr] , Kalina Cherneva [ctr]
Maintainer: Matteo Delucchi <matteo.delucchi@math.uzh.ch>

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Package PUMP updated to version 1.0.2 with previous version 1.0.1 dated 2022-09-17

Title: Power Under Multiplicity Project
Description: Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <arXiv:2112.15273>.
Author: Kristin Porter [aut], Luke Miratrix [aut, cre] , Kristen Hunter [aut] , Zarni Htet [aut], MDRC [cph], Institute of Education Sciences [fnd]
Maintainer: Luke Miratrix <luke_miratrix@gse.harvard.edu>

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Package spant updated to version 2.12.0 with previous version 2.11.0 dated 2023-04-22

Title: MR Spectroscopy Analysis Tools
Description: Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.
Author: Martin Wilson [cre, aut] , Yong Wang [ctb], John Muschelli [ctb]
Maintainer: Martin Wilson <martin@pipegrep.co.uk>

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Package BiCausality updated to version 0.1.3 with previous version 0.1.2 dated 2022-08-19

Title: Binary Causality Inference Framework
Description: A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.
Author: Chainarong Amornbunchornvej [aut, cre]
Maintainer: Chainarong Amornbunchornvej <grandca@gmail.com>

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Package spaMM updated to version 4.3.0 with previous version 4.2.1 dated 2023-03-03

Title: Mixed-Effect Models, with or without Spatial Random Effects
Description: Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
Author: Francois Rousset [aut, cre, cph] , Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [aut]
Maintainer: Francois Rousset <francois.rousset@umontpellier.fr>

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Package mlr3pipelines updated to version 0.5.0-1 with previous version 0.5.0 dated 2023-05-22

Title: Preprocessing Operators and Pipelines for 'mlr3'
Description: Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.
Author: Martin Binder [aut, cre], Florian Pfisterer [aut] , Lennart Schneider [aut] , Bernd Bischl [aut] , Michel Lang [aut] , Sebastian Fischer [ctb] , Susanne Dandl [aut]
Maintainer: Martin Binder <mlr.developer@mb706.com>

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Package lwgeom updated to version 0.2-13 with previous version 0.2-11 dated 2023-01-14

Title: Bindings to Selected 'liblwgeom' Functions for Simple Features
Description: Access to selected functions found in 'liblwgeom' <https://github.com/postgis/postgis/tree/master/liblwgeom>, the light-weight geometry library used by 'PostGIS' <http://postgis.net/>.
Author: Edzer Pebesma [aut, cre] , Colin Rundel [ctb], Andy Teucher [ctb], liblwgeom developers [cph]
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

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Package csdata updated to version 2023.5.22 with previous version 2023.4.21 dated 2023-04-21

Title: Structural Data for Norway
Description: Datasets relating to population in municipalities, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2020.
Author: Richard Aubrey White [aut, cre] , Chi Zhang [aut]
Maintainer: Richard Aubrey White <hello@rwhite.no>

Diff between csdata versions 2023.4.21 dated 2023-04-21 and 2023.5.22 dated 2023-05-22

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New package PEtests with initial version 0.1.0
Package: PEtests
Title: Power-Enhanced (PE) Tests for High-Dimensional Data
Version: 0.1.0
Description: Two-sample power-enhanced mean tests, covariance tests, and simultaneous tests on mean vectors and covariance matrices for high-dimensional data. Methods of these PE tests are presented in Yu, Li, and Xue (2022) <doi:10.1080/01621459.2022.2126781>; Yu, Li, Xue, and Li (2022) <doi:10.1080/01621459.2022.2061354>.
Author: Xiufan Yu [aut, cre], Danning Li [aut], Lingzhou Xue [aut], Runze Li [aut]
Maintainer: Xiufan Yu <xiufan.yu@nd.edu>
Imports: stats
Encoding: UTF-8
License: GPL (>= 3)
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2023-05-21 16:15:17 UTC; xyu24
Repository: CRAN
Date/Publication: 2023-05-22 08:40:02 UTC

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New package intradayModel with initial version 0.0.1
Package: intradayModel
Title: Modeling and Forecasting Financial Intraday Signals
Version: 0.0.1
Date: 2023-05-20
Description: Models, analyzes, and forecasts financial intraday signals. This package currently supports a univariate state-space model for intraday trading volume provided by Chen (2016) <doi:10.2139/ssrn.3101695>.
Maintainer: Daniel P. Palomar <daniel.p.palomar@gmail.com>
URL: https://github.com/convexfi/intradayModel, https://www.danielppalomar.com, https://dx.doi.org/10.2139/ssrn.3101695
BugReports: https://github.com/convexfi/intradayModel/issues
License: Apache License (== 2.0)
Encoding: UTF-8
Depends: R (>= 2.10)
Imports: ggplot2, magrittr, patchwork, reshape2, scales, xts, zoo, utils
Suggests: knitr, rmarkdown, R.rsp, testthat (>= 3.0.0), cleanrmd, devtools
VignetteBuilder: knitr, rmarkdown, R.rsp
LazyData: true
NeedsCompilation: no
Packaged: 2023-05-20 13:51:08 UTC; 12928
Author: Shengjie Xiu [aut], Yifan Yu [aut], Daniel P. Palomar [cre, aut, cph]
Repository: CRAN
Date/Publication: 2023-05-22 08:30:02 UTC

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New package ggblend with initial version 0.1.0
Package: ggblend
Title: Blending and Compositing Algebra for 'ggplot2'
Version: 0.1.0
Description: Algebra of operations for blending, copying, adjusting, and compositing layers in 'ggplot2'. Supports copying and adjusting the aesthetics or parameters of an existing layer, partitioning a layer into multiple pieces for re-composition, applying affine transformations to layers, and combining layers (or partitions of layers) using blend modes (including commutative blend modes, like multiply and darken). Blend mode support is particularly useful for creating plots with overlapping groups where the layer drawing order does not change the output; see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.
License: MIT + file LICENSE
Language: en-US
Depends: R (>= 4.2)
Imports: methods, grid, ggplot2 (>= 3.4.0), rlang
Suggests: covr, testthat (>= 3.0.0), fontquiver, showtext, sysfonts, ggnewscale
BugReports: https://github.com/mjskay/ggblend/issues/new
URL: https://mjskay.github.io/ggblend/, https://github.com/mjskay/ggblend/
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2023-05-21 04:21:44 UTC; matth
Author: Matthew Kay [aut, cre, cph]
Maintainer: Matthew Kay <mjskay@northwestern.edu>
Repository: CRAN
Date/Publication: 2023-05-22 08:30:05 UTC

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Package BrainCon updated to version 0.3.0 with previous version 0.2.0 dated 2022-04-21

Title: Inference the Partial Correlations Based on Time Series Data
Description: A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using the methods of Qiu and Zhou. (2021)<DOI: 10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI: 10.1198/016214506000000339>. It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations.
Author: Yunhaonan Yang [aut, cre], Peng Wu [aut], Xin Gai [aut], Yumou Qiu [aut], Xiaohua Zhou [aut]
Maintainer: Yunhaonan Yang <haonan_yy@pku.edu.cn>

Diff between BrainCon versions 0.2.0 dated 2022-04-21 and 0.3.0 dated 2023-05-22

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Package mlr3pipelines updated to version 0.5.0 with previous version 0.4.3 dated 2023-03-23

Title: Preprocessing Operators and Pipelines for 'mlr3'
Description: Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.
Author: Martin Binder [aut, cre], Florian Pfisterer [aut] , Lennart Schneider [aut] , Bernd Bischl [aut] , Michel Lang [aut] , Sebastian Fischer [ctb] , Susanne Dandl [aut]
Maintainer: Martin Binder <mlr.developer@mb706.com>

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Package future.tests updated to version 0.7.0 with previous version 0.6.0 dated 2023-03-11

Title: Test Suite for 'Future API' Backends
Description: Backends implementing the 'Future' API, as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API. The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines.
Author: Henrik Bengtsson [aut, cre, cph], The R Consortium [fnd] grant in 2017)
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between future.tests versions 0.6.0 dated 2023-03-11 and 0.7.0 dated 2023-05-22

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Package DImodels updated to version 1.2.1 with previous version 1.2 dated 2022-11-25

Title: Diversity-Interactions (DI) Models
Description: The 'DImodels' package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) <doi:10.1890/08-1684.1>. Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function).
Author: Rafael de Andrade Moral [aut, cre], John Connolly [aut], Rishabh Vishwakarma [ctb], Caroline Brophy [aut]
Maintainer: Rafael de Andrade Moral <rafael.deandrademoral@mu.ie>

Diff between DImodels versions 1.2 dated 2022-11-25 and 1.2.1 dated 2023-05-22

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Package PINMA updated to version 1.1-2 with previous version 1.1-1 dated 2023-03-26

Title: Improved Methods for Constructing Prediction Intervals for Network Meta-Analysis
Description: Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.
Author: Hisashi Noma [aut, cre]
Maintainer: Hisashi Noma <noma@ism.ac.jp>

Diff between PINMA versions 1.1-1 dated 2023-03-26 and 1.1-2 dated 2023-05-22

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Package boutliers updated to version 1.1-2 with previous version 1.1-1 dated 2020-11-04

Title: Outlier Detection and Influence Diagnostics for Meta-Analysis
Description: Computational tools for outlier detection and influence diagnostics of meta-analysis. Bootstrap distributions of the influence statistics are calculated, and the thresholds to determine outliers are explicitly provided.
Author: Hisashi Noma [aut, cre], Masahiko Gosho [aut]
Maintainer: Hisashi Noma <noma@ism.ac.jp>

Diff between boutliers versions 1.1-1 dated 2020-11-04 and 1.1-2 dated 2023-05-22

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