Mon, 22 Jul 2024

Package DiSCos updated to version 0.1.1 with previous version 0.1.0 dated 2024-05-13

Title: Distributional Synthetic Controls Estimation
Description: The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.
Author: David Van Dijcke [aut, cre] , Florian Gunsilius [aut] , Siyun He [aut]
Maintainer: David Van Dijcke <dvdijcke@umich.edu>

Diff between DiSCos versions 0.1.0 dated 2024-05-13 and 0.1.1 dated 2024-07-22

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New package trud with initial version 0.1.0
Package: trud
Title: Query the 'NHS TRUD API'
Version: 0.1.0
Description: A convenient R interface to the 'National Health Service (NHS) Technology Reference Update Distribution (TRUD) API'. Retrieve available releases for items that you are subscribed to and download these with ease. For more information on the API, see <https://isd.digital.nhs.uk/trud/users/guest/filters/0/api>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: cli, dplyr, httr2, magrittr, purrr, rlang, rvest, stringr, tibble, tidyselect
URL: https://rmgpanw.github.io/trud/, https://github.com/rmgpanw/trud
BugReports: https://github.com/rmgpanw/trud/issues
Suggests: testthat (>= 3.0.0), withr
NeedsCompilation: no
Packaged: 2024-07-21 14:30:14 UTC; alasdair
Author: Alasdair Warwick [aut, cre, cph] , Robert Luben [aut] , Abraham Olvera-Barrios [aut] , Chuin Ying Ung [aut]
Maintainer: Alasdair Warwick <alasdair.warwick.19@ucl.ac.uk>
Repository: CRAN
Date/Publication: 2024-07-23 00:20:02 UTC

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New package tenm with initial version 0.5.1
Package: tenm
Version: 0.5.1
Title: Temporal Ecological Niche Models
Description: Implements methods and functions to calibrate time-specific niche models (multi-temporal calibration), letting users execute a strict calibration and selection process of niche models based on ellipsoids, as well as functions to project the potential distribution in the present and in global change scenarios.The 'tenm' package has functions to recover information that may be lost or overlooked while applying a data curation protocol. This curation involves preserving occurrences that may appear spatially redundant (occurring in the same pixel) but originate from different time periods. A novel aspect of this package is that it might reconstruct the fundamental niche more accurately than mono-calibrated approaches. The theoretical background of the package can be found in Peterson et al. (2011)<doi:10.5860/CHOICE.49-6266>.
License: GPL-3
URL: https://luismurao.github.io/tenm/
BugReports: https://github.com/luismurao/tenm/issues
Imports: MASS, terra (> 1.7.5), sf, purrr, dplyr, stringr, rgl (> 1.2), future, tidyr, furrr, lubridate, methods
Encoding: UTF-8
Depends: R (>= 4.1)
LazyData: true
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-07-21 15:50:20 UTC; luis.osorio
Author: Luis Osorio-Olvera [aut, cre] , Miguel Hernandez [aut] , Rusby G. Contreras-Diaz [aut] , Xavier Chiappa-Carrara [aut] , Fernanda Rosales-Ramos [aut] , Mariana Munguia-Carrara [aut] , Oliver Lopez-Corona [aut] , Townsend Peterson [ctb] , Jorge Soberon [...truncated...]
Maintainer: Luis Osorio-Olvera <luismurao@gmail.com>
Repository: CRAN
Date/Publication: 2024-07-23 00:30:01 UTC

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New package StuteTest with initial version 1.0.2
Package: StuteTest
Title: Stute (1997) Linearity Test
Version: 1.0.2
Maintainer: Diego Ciccia <diego.ciccia@kellogg.northwestern.edu>
Description: Non-parametric test, originally proposed by Stute (1997) <https://www.jstor.org/stable/2242560>, that the expectation of a dependent variable Y given an independent variable D is linear in D.
License: MIT + file LICENSE
VignetteBuilder: R.rsp
Imports: Rcpp (>= 1.0.12), dplyr, plm, rnames, stats
LinkingTo: Rcpp, RcppArmadillo
Author: Diego Ciccia [aut, cre], Felix Knau [aut], Doulo Sow [aut], Clement de Chaisemartin [aut], Xavier D'Haultfoeuille [aut]
Encoding: UTF-8
Suggests: R.rsp, testthat (>= 3.0.0)
NeedsCompilation: yes
Packaged: 2024-07-21 11:15:23 UTC; 39380
Repository: CRAN
Date/Publication: 2024-07-23 00:20:13 UTC

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New package pastboon with initial version 0.1.0
Package: pastboon
Title: Simulation of Parameterized Stochastic Boolean Networks
Version: 0.1.0
Description: Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: BNp (Boolean network with perturbations), described by Trairatphisan et al. (2013) <doi:10.1186/1478-811X-11-46>, SDDS (stochastic discrete dynamical systems), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and PEW (Boolean network with probabilistic edge weights), presented by Deritei et al. (2022) <doi:10.1371/journal.pcbi.1010536>. This package includes source code derived from the 'BoolNet' package, which is licensed under the Artistic License 2.0.
Author: Mohammad Taheri-Ledari [aut, cre, cph] , Kaveh Kavousi [ctb] , Sayed-Amir Marashi [ctb] , Authors of BoolNet [ctb] , Troy D. Hanson [ctb]
Maintainer: Mohammad Taheri-Ledari <mo.taheri@ut.ac.ir>
Depends: R (>= 3.5.0)
Suggests: BoolNet
License: Artistic-2.0
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
BugReports: https://github.com/taherimo/pastboon/issues
Packaged: 2024-07-21 15:04:32 UTC; taheri
Repository: CRAN
Date/Publication: 2024-07-23 00:30:06 UTC

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New package bsvarSIGNs with initial version 1.0
Package: bsvarSIGNs
Title: Bayesian SVARs with Sign, Zero, and Narrative Restrictions
Version: 1.0
Date: 2024-07-19
Maintainer: Xiaolei Wang <adamwang15@gmail.com>
Description: Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions impos [...truncated...]
License: GPL (>= 3)
Imports: Rcpp (>= 1.0.12), RcppProgress, R6
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, bsvars
Depends: R (>= 2.10), RcppArmadillo, bsvars
Suggests: tinytest
URL: https://bsvars.github.io/bsvarSIGNs/
BugReports: https://github.com/bsvars/bsvarSIGNs/issues
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Packaged: 2024-07-21 12:17:36 UTC; xiaoleiwang
Author: Xiaolei Wang [aut, cre] , Tomasz Wozniak [aut]
Repository: CRAN
Date/Publication: 2024-07-23 00:20:05 UTC

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New package aka with initial version 0.1.0
Package: aka
Title: Define Aliases for R Expressions
Version: 0.1.0
Description: Create aliases for other R names or arbitrarily complex R expressions. Accessing the alias acts as-if the aliased expression were invoked instead, and continuously reflects the current value of that expression: updates to the original expression will be reflected in the alias; and updates to the alias will automatically be reflected in the original expression.
URL: https://klmr.me/aka/, https://github.com/klmr/aka
BugReports: https://github.com/klmr/aka/issues
Suggests: testthat
License: MIT + file LICENSE
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-07-21 14:16:09 UTC; konrad
Author: Konrad Rudolph [cre, aut]
Maintainer: Konrad Rudolph <konrad.rudolph@gmail.com>
Repository: CRAN
Date/Publication: 2024-07-23 00:20:09 UTC

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Package wiesbaden updated to version 1.2.10 with previous version 1.2.9 dated 2023-02-17

Title: Access Databases from the Federal Statistical Office of Germany
Description: Retrieve and import data from different databases of the Federal Statistical Office of Germany (DESTATIS) using their SOAP XML web service <https://www-genesis.destatis.de/>.
Author: Moritz Marbach [aut, cre]
Maintainer: Moritz Marbach <m.marbach@ucl.ac.uk>

Diff between wiesbaden versions 1.2.9 dated 2023-02-17 and 1.2.10 dated 2024-07-22

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Package websocket updated to version 1.4.2 with previous version 1.4.1 dated 2021-08-18

Title: 'WebSocket' Client Library
Description: Provides a 'WebSocket' client interface for R. 'WebSocket' is a protocol for low-overhead real-time communication: <https://en.wikipedia.org/wiki/WebSocket>.
Author: Winston Chang [aut, cre], Joe Cheng [aut], Alan Dipert [aut], Barbara Borges [aut], Posit, PBC [cph], Peter Thorson [ctb, cph] , Rene Nyffenegger [ctb, cph] , Micael Hildenborg [ctb, cph] , Aladdin Enterprises [cph] , Bjoern Hoehrmann [ctb, cph]
Maintainer: Winston Chang <winston@posit.co>

Diff between websocket versions 1.4.1 dated 2021-08-18 and 1.4.2 dated 2024-07-22

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Package r2sundials updated to version 6.5.0-5 with previous version 6.5.0-4 dated 2023-12-08

Title: Wrapper for 'SUNDIALS' Solving ODE and Sensitivity Problem
Description: Wrapper for widely used 'SUNDIALS' software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its 'CVODES' solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made 'R' friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in 'R' and in 'C++' ('RcppArmadillo' flavor). In case of 'C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case 'rmumps' package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as 'R' or 'C++' functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. 'SUNDIALS' library was published in Hindmarsh et al. (2005) <doi:1 [...truncated...]
Author: Serguei Sokol [cre, aut], Carol S. Woodward [ctb], Daniel R. Reynolds [ctb], Alan C. Hindmarsh [ctb], David J. Gardner [ctb], Cody J. Balos [ctb], Radu Serban [ctb], Scott D. Cohen [ctb], Peter N. Brown [ctb], George Byrne [ctb], Allan G. Taylor [ctb [...truncated...]
Maintainer: Serguei Sokol <sokol@insa-toulouse.fr>

Diff between r2sundials versions 6.5.0-4 dated 2023-12-08 and 6.5.0-5 dated 2024-07-22

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New package precommit with initial version 0.4.3
Package: precommit
Title: Pre-Commit Hooks
Version: 0.4.3
Author: Lorenz Walthert
Maintainer: Lorenz Walthert <lorenz.walthert@icloud.com>
Description: Useful git hooks for R building on top of the multi-language framework 'pre-commit' for hook management. This package provides git hooks for common tasks like formatting files with 'styler' or spell checking as well as wrapper functions to access the 'pre-commit' executable.
License: GPL-3
URL: https://lorenzwalthert.github.io/precommit/, https://github.com/lorenzwalthert/precommit
Imports: cli, fs, here, magrittr, purrr, R.cache, rlang, rprojroot, withr, yaml
Suggests: desc, docopt (>= 0.7.1), git2r, glue, knitr, lintr, pkgload, pkgdown, reticulate (>= 1.16), rmarkdown, roxygen2, rstudioapi, spelling, styler, testthat (>= 2.1.0), tibble, usethis (>= 2.0.0)
VignetteBuilder: knitr
Encoding: UTF-8
SystemRequirements: git
NeedsCompilation: no
Packaged: 2024-07-22 13:56:23 UTC; lorenz
Repository: CRAN
Date/Publication: 2024-07-22 22:20:05 UTC

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Package eimpute updated to version 0.2.4 with previous version 0.2.3 dated 2024-02-18

Title: Efficiently Impute Large Scale Incomplete Matrix
Description: Efficiently impute large scale matrix with missing values via its unbiased low-rank matrix approximation. Our main approach is Hard-Impute algorithm proposed in <https://www.jmlr.org/papers/v11/mazumder10a.html>, which achieves highly computational advantage by truncated singular-value decomposition.
Author: Zhe Gao [aut, cre], Jin Zhu [aut], Junxian Zhu [aut], Xueqin Wang [aut], Yixuan Qiu [cph], Gael Guennebaud [cph, ctb], Jitse Niesen [cph, ctb], Ray Gardner [ctb]
Maintainer: Zhe Gao <gaozh8@mail.ustc.edu.cn>

Diff between eimpute versions 0.2.3 dated 2024-02-18 and 0.2.4 dated 2024-07-22

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Package dafishr updated to version 1.0.1 with previous version 1.0.0 dated 2022-12-06

Title: Download, Wrangle, and Analyse Vessel Monitoring System Data
Description: Allows to download, clean and analyse raw Vessel Monitoring System, VMS, data from Mexican government. You can use the vms_download() function to download raw data, or you can use the sample_dataset provided within the package. You can follow the tutorial in the vignette available at <https://cbmc-gcmp.github.io/dafishr/index.html>.
Author: Fabio Favoretto [aut, cre] , Eduardo Leon Solorzano [ctb]
Maintainer: Fabio Favoretto <fabio@gocmarineprogram.org>

Diff between dafishr versions 1.0.0 dated 2022-12-06 and 1.0.1 dated 2024-07-22

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Package stacks updated to version 1.0.5 with previous version 1.0.4 dated 2024-03-21

Title: Tidy Model Stacking
Description: Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking.
Author: Simon Couch [aut, cre], Max Kuhn [aut], Posit Software, PBC [cph, fnd]
Maintainer: Simon Couch <simon.couch@posit.co>

Diff between stacks versions 1.0.4 dated 2024-03-21 and 1.0.5 dated 2024-07-22

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Package brea updated to version 0.3.1 with previous version 0.3.0 dated 2024-07-02

Title: Bayesian Recurrent Events Analysis
Description: Functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models.
Author: Adam J King
Maintainer: Adam J King <king@cpp.edu>

Diff between brea versions 0.3.0 dated 2024-07-02 and 0.3.1 dated 2024-07-22

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Package mtarm updated to version 0.1.2 with previous version 0.1.1 dated 2024-06-04

Title: Bayesian Estimation of Multivariate Threshold Autoregressive Models
Description: Estimation, inference and forecasting using the Bayesian approach for multivariate threshold autoregressive (TAR) models in which the distribution used to describe the noise process belongs to the class of Gaussian variance mixtures.
Author: Luis Hernando Vanegas [aut, cre], Sergio Alejandro Calderon [aut], Luz Marina Rondon [aut]
Maintainer: Luis Hernando Vanegas <lhvanegasp@unal.edu.co>

Diff between mtarm versions 0.1.1 dated 2024-06-04 and 0.1.2 dated 2024-07-22

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New package ssdGSA with initial version 0.1.0
Package: ssdGSA
Title: Single Sample Directional Gene Set Analysis
Version: 0.1.0
Description: A method that inherits the standard gene set variation analysis (GSVA) method and also provides the option to use summary statistics from any analysis (disease vs healthy, lesional side vs nonlesional side, etc..) input to define the direction of gene sets used for directional gene set score calculation for a given disease. Hanzelmann, S., Castelo, R., and Guinney, J. (2013) <doi:10.1186/1471-2105-14-7>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Imports: GSVA, dplyr, purrr, stringr, tibble, vctrs, clusterProfiler, stats, org.Hs.eg.db, tidyselect, utils
VignetteBuilder: knitr
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2024-07-19 17:51:55 UTC; QIANQ09
Author: Xingpeng Li [aut, cre], Qi Qian [aut]
Maintainer: Xingpeng Li <xingpeng.li@pfizer.com>
Repository: CRAN
Date/Publication: 2024-07-22 17:30:02 UTC

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Package twang updated to version 2.6.1 with previous version 2.6 dated 2023-12-05

Title: Toolkit for Weighting and Analysis of Nonequivalent Groups
Description: Provides functions for propensity score estimating and weighting, nonresponse weighting, and diagnosis of the weights.
Author: Matthew Cefalu, Greg Ridgeway, Dan McCaffrey, Andrew Morral, Beth Ann Griffin, and Lane Burgette
Maintainer: Lane Burgette <burgette@rand.org>

Diff between twang versions 2.6 dated 2023-12-05 and 2.6.1 dated 2024-07-22

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Package mlr3 updated to version 0.20.1 with previous version 0.20.0 dated 2024-06-28

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 wrMisc updated to version 1.15.1 with previous version 1.15.0.3 dated 2024-05-10

Title: Analyze Experimental High-Throughput (Omics) Data
Description: The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of larg [...truncated...]
Author: Wolfgang Raffelsberger [aut, cre]
Maintainer: Wolfgang Raffelsberger <w.raffelsberger@gmail.com>

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Package phoenics updated to version 0.3 with previous version 0.2 dated 2024-06-27

Title: Pathways Longitudinal and Differential Analysis in Metabolomics
Description: Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.
Author: Camille Guilmineau [aut, cre], Remi Servien [aut] , Nathalie Vialaneix [aut]
Maintainer: Camille Guilmineau <camille.guilmineau@inrae.fr>

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Package landscapemetrics updated to version 2.1.4 with previous version 2.1.3 dated 2024-06-26

Title: Landscape Metrics for Categorical Map Patterns
Description: Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.fragstats.org/>) and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.
Author: Maximilian H.K. Hesselbarth [aut, cre] , Marco Sciaini [aut] , Jakub Nowosad [aut] , Sebastian Hanss [aut] , Laura J. Graham [ctb] , Jeffrey Hollister [ctb] , Kimberly A. With [ctb] , Florian Prive [ctb] function), Project Nayuki [ctb] , Matt Strima [...truncated...]
Maintainer: Maximilian H.K. Hesselbarth <mhk.hesselbarth@gmail.com>

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Package mlr3fda updated to version 0.2.0 with previous version 0.1.2 dated 2024-05-30

Title: Extending 'mlr3' to Functional Data Analysis
Description: Extends the 'mlr3' ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the 'tf' package. The package provides 'PipeOps' for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional 'PCA'.
Author: Sebastian Fischer [aut, cre] , Maximilian Muecke [aut] , Fabian Scheipl [ctb] , Bernd Bischl [ctb]
Maintainer: Sebastian Fischer <sebf.fischer@gmail.com>

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Package sensemakr updated to version 0.1.6 with previous version 0.1.4 dated 2021-10-08

Title: Sensitivity Analysis Tools for Regression Models
Description: Implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.
Author: Carlos Cinelli [aut, cre], Jeremy Ferwerda [aut], Chad Hazlett [aut], Danielle Tsao [ctb], Aaron Rudkin [ctb], Grigorij Ljubownikow [ctb]
Maintainer: Carlos Cinelli <carloscinelli@hotmail.com>

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Package secretbase updated to version 1.0.1 with previous version 1.0.0 dated 2024-06-16

Title: Cryptographic Hash, Extendable-Output and Base64 Functions
Description: Fast and memory-efficient streaming hash functions and base64 encoding and decoding. Performs direct hashing of strings and raw vectors. Stream hashes files potentially larger than memory, as well as in-memory objects through R's serialization mechanism. Implementations include the SHA-256, SHA-3 and 'Keccak' cryptographic hash functions, SHAKE256 extendable-output function (XOF), and 'SipHash' pseudo-random function.
Author: Charlie Gao [aut, cre] , Hibiki AI Limited [cph]
Maintainer: Charlie Gao <charlie.gao@shikokuchuo.net>

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Package pm3 updated to version 0.2.0 with previous version 0.1.9 dated 2023-06-17

Title: Propensity Score Matching for Unordered 3-Group Data
Description: You can use this program for 3 sets of categorical data for propensity score matching. Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. The matching will make the differences in the baseline data smaller. This method was described by Alvaro Fuentes (2022) <doi:10.1080/00273171.2021.1925521>.
Author: Qiang LIU [aut, cre]
Maintainer: Qiang LIU <dege857@163.com>

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Package ggdag updated to version 0.2.13 with previous version 0.2.12 dated 2024-03-08

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 (<https://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>

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Package easystats updated to version 0.7.3 with previous version 0.7.2 dated 2024-06-06

Title: Framework for Easy Statistical Modeling, Visualization, and Reporting
Description: A meta-package that installs and loads a set of packages from 'easystats' ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, visualization, and reporting. Additionally, it provides articles targeted at instructors for teaching 'easystats', and a dashboard targeted at new R users for easily conducting statistical analysis by accessing summary results, model fit indices, and visualizations with minimal programming.
Author: Daniel Luedecke [aut, cre] , Dominique Makowski [aut] , Mattan S. Ben-Shachar [aut] , Indrajeet Patil [aut] , Brenton M. Wiernik [aut] , Etienne Bacher [aut] , Remi Theriault [aut]
Maintainer: Daniel Luedecke <d.luedecke@uke.de>

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Package GeneNMF updated to version 0.6.0 with previous version 0.4.0 dated 2024-02-29

Title: Non-Negative Matrix Factorization for Single-Cell Omics
Description: A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures.
Author: Massimo Andreatta [aut, cre] , Santiago Carmona [aut]
Maintainer: Massimo Andreatta <massimo.andreatta@unil.ch>

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Package manynet updated to version 1.0.2 with previous version 1.0.1 dated 2024-07-17

Title: Many Ways to Make, Modify, Map, Mark, and Measure Myriad Networks
Description: Many tools for making, modifying, mapping, marking, measuring, and motifs and memberships of many different types of networks. All functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, and on one-mode, two-mode (bipartite), and sometimes three-mode networks. The package includes functions for importing and exporting, creating and generating networks, modifying networks and node and tie attributes, and describing and visualizing networks with sensible defaults.
Author: James Hollway [cre, aut, ctb] , Henrique Sposito [ctb]
Maintainer: James Hollway <james.hollway@graduateinstitute.ch>

Diff between manynet versions 1.0.1 dated 2024-07-17 and 1.0.2 dated 2024-07-22

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Package dsem updated to version 1.3.0 with previous version 1.2.1 dated 2024-04-02

Title: Fit Dynamic Structural Equation Models
Description: Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms."
Author: James Thorson [aut, cre]
Maintainer: James Thorson <James.Thorson@noaa.gov>

Diff between dsem versions 1.2.1 dated 2024-04-02 and 1.3.0 dated 2024-07-22

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Package UKB.COVID19 updated to version 0.1.5 with previous version 0.1.4 dated 2024-01-24

Title: UK Biobank COVID-19 Data Processing and Risk Factor Association Tests
Description: Process UK Biobank COVID-19 test result data for susceptibility, severity and mortality analyses, perform potential non-genetic COVID-19 risk factor and co-morbidity association tests. Wang et al. (2021) <doi:10.5281/zenodo.5174381>.
Author: Longfei Wang [aut, cre]
Maintainer: Longfei Wang <wang.lo@wehi.edu.au>

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Package SEMgraph updated to version 1.2.2 with previous version 1.2.1 dated 2024-02-06

Title: Network Analysis and Causal Inference Through Structural Equation Modeling
Description: Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions to import, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework proposed in Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.
Author: Mario Grassi [aut], Fernando Palluzzi [aut], Barbara Tarantino [aut, cre]
Maintainer: Barbara Tarantino <barbara.tarantino01@universitadipavia.it>

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Package insee (with last version 1.1.5) was removed from CRAN

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

2022-09-17 1.1.5
2022-03-10 1.1.4
2022-01-19 1.1.3
2021-10-06 1.1.2
2021-09-16 1.1.1
2021-06-04 1.1.0

Permanent link
Package rayvertex updated to version 0.11.4 with previous version 0.10.4 dated 2023-12-16

Title: 3D Software Rasterizer
Description: Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion.
Author: Tyler Morgan-Wall [aut, cph, cre] , Syoyo Fujita [ctb, cph], Vilya Harvey [ctb, cph], G-Truc Creation [ctb, cph], Sean Barrett [ctb, cph]
Maintainer: Tyler Morgan-Wall <tylermw@gmail.com>

Diff between rayvertex versions 0.10.4 dated 2023-12-16 and 0.11.4 dated 2024-07-22

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Package HDRFA updated to version 0.1.5 with previous version 0.1.4 dated 2023-11-07

Title: High-Dimensional Robust Factor Analysis
Description: Factor models have been widely applied in areas such as economics and finance, and the well-known heavy-tailedness of macroeconomic/financial data should be taken into account when conducting factor analysis. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's L2 norm, which turns out to do Principal Component Analysis (PCA) on the weighted sample covariance matrix and thereby named as Huber PCA. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm. In this package we also provide the code for traditional PCA, the Robust Two Step (RTS) method by He et al. (2022) and the Quantile Factor Analysis (QFA) method by Chen et al. (2021) and He et al. (2023).
Author: Yong He [aut], Lingxiao Li [aut], Dong Liu [aut, cre], Wenxin Zhou [aut]
Maintainer: Dong Liu <liudong_stat@163.com>

Diff between HDRFA versions 0.1.4 dated 2023-11-07 and 0.1.5 dated 2024-07-22

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