Wed, 14 Feb 2024

Package rnrfa updated to version 2.1.0.5 with previous version 2.1.0 dated 2022-09-08

Title: UK National River Flow Archive Data from R
Description: Utility functions to retrieve data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>). The package contains R wrappers to the UK NRFA data temporary-API. There are functions to retrieve stations falling in a bounding box, to generate a map and extracting time series and general information. The package is fully described in Vitolo et al (2016) "rnrfa: An R package to Retrieve, Filter and Visualize Data from the UK National River Flow Archive" <https://journal.r-project.org/archive/2016/RJ-2016-036/RJ-2016-036.pdf>.
Author: Ilaria Prosdocimi [ctb, cre] , Claudia Vitolo [aut] , Matthew Fry [ctb] , Wouter Buytaert [ctb] , Michael Spencer [ctb] , Tobias Gauster [ctb]
Maintainer: Ilaria Prosdocimi <prosdocimi.ilaria@gmail.com>

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Package plfm updated to version 2.2.6 with previous version 2.2.5 dated 2022-03-30

Title: Probabilistic Latent Feature Analysis
Description: Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.
Author: Michel Meulders [aut, cre], Philippe De Bruecker [ctb]
Maintainer: Michel Meulders <michel.meulders@kuleuven.be>

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Package codebookr updated to version 0.1.7 with previous version 0.1.6 dated 2023-03-16

Title: Create Codebooks from Data Frames
Description: Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame.
Author: Brad Cannell [aut, cre, cph]
Maintainer: Brad Cannell <brad.cannell@gmail.com>

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Package unusualprofile updated to version 0.1.4 with previous version 0.1.2 dated 2022-12-13

Title: Calculates Conditional Mahalanobis Distances
Description: Calculates a Mahalanobis distance for every row of a set of outcome variables (Mahalanobis, 1936 <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis distance is calculated using a conditional covariance matrix (i.e., a covariance matrix of the outcome variables after controlling for a set of predictors). Plotting the output of the cond_maha() function can help identify which elements of a profile are unusual after controlling for the predictors.
Author: W. Joel Schneider [aut, cre] , Feng Ji [aut]
Maintainer: W. Joel Schneider <w.joel.schneider@gmail.com>

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Package eSDM updated to version 0.4.1 with previous version 0.4.0 dated 2023-10-24

Title: Ensemble Tool for Predictions from Species Distribution Models
Description: A tool which allows users to create and evaluate ensembles of species distribution model (SDM) predictions. Functionality is offered through R functions or a GUI (R Shiny app). This tool can assist users in identifying spatial uncertainties and making informed conservation and management decisions. The package is further described in Woodman et al (2019) <doi:10.1111/2041-210X.13283>.
Author: Sam Woodman [aut, cre]
Maintainer: Sam Woodman <sam.woodman@noaa.gov>

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Package parallelly updated to version 1.37.0 with previous version 1.36.0 dated 2023-05-26

Title: Enhancing the 'parallel' Package
Description: Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package. For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package clidamonger updated to version 1.1.0 with previous version 1.0.0 dated 2023-02-09

Title: Monthly Climate Data for Germany, Usable for Heating and Cooling Calculations
Description: This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation).
Author: Tobias Loga [aut] , Guillaume Behem [aut], Jens Calisti [cre]
Maintainer: Jens Calisti <jens@tangled.de>

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Package Tplyr updated to version 1.2.0 with previous version 1.1.0 dated 2023-01-10

Title: A Traceability Focused Grammar of Clinical Data Summary
Description: A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.
Author: Eli Miller [aut] , Mike Stackhouse [aut, cre] , Ashley Tarasiewicz [aut], Nathan Kosiba [ctb] , Sadchla Mascary [ctb], Andrew Bates [ctb], Shiyu Chen [ctb], Oleksii Mikryukov [ctb], Atorus Research LLC [cph]
Maintainer: Mike Stackhouse <mike.stackhouse@atorusresearch.com>

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Package rstac updated to version 1.0.0 with previous version 0.9.2-5 dated 2024-02-11

Title: Client Library for SpatioTemporal Asset Catalog
Description: Provides functions to access, search and download spacetime earth observation data via SpatioTemporal Asset Catalog (STAC). This package supports the version 1.0.0 (and older) of the STAC specification (<https://github.com/radiantearth/stac-spec>). For further details see Simoes et al. (2021) <doi:10.1109/IGARSS47720.2021.9553518>.
Author: Rolf Simoes [aut], Felipe Carvalho [aut, cre], Brazil Data Cube Team [aut], National Institute for Space Research [cph]
Maintainer: Felipe Carvalho <lipecaso@gmail.com>

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New package nnR with initial version 0.1.0
Package: nnR
Title: Neural Networks Made Algebraic
Version: 0.1.0
Maintainer: Shakil Rafi <sarafi@uark.edu>
Description: Do algebraic operations on neural networks. We seek here to implement in R, operations on neural networks and their resulting approximations. Our operations derive their descriptions mainly from Rafi S., Padgett, J.L., and Nakarmi, U. (2024), "Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials", <doi:10.48550/arXiv.2402.01058>, Grohs P., Hornung, F., Jentzen, A. et al. (2023), "Space-time error estimates for deep neural network approximations for differential equations", <doi:10.1007/s10444-022-09970-2>, Jentzen A., Kuckuck B., von Wurstemberger, P. (2023), "Mathematical Introduction to Deep Learning Methods, Implementations, and Theory" <doi:10.48550/arXiv.2310.20360>. Our implementation is meant mainly as a pedagogical tool, and proof of concept. Faster implementations with deeper vectorizations may be made in future versions.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.1.0)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
URL: https://github.com/2shakilrafi/nnR/
BugReports: https://github.com/2shakilrafi/nnR/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-02-13 20:41:38 UTC; shakilrafi
Author: Shakil Rafi [aut, cre] , Joshua Lee Padgett [aut] , Ukash Nakarmi [ctb]
Repository: CRAN
Date/Publication: 2024-02-14 21:30:02 UTC

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Package kalmanfilter updated to version 2.1.0 with previous version 2.0.2 dated 2023-09-25

Title: Kalman Filter
Description: 'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. There is also a function to handle time varying parameters. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
Author: Alex Hubbard [aut, cre]
Maintainer: Alex Hubbard <hubbard.alex@gmail.com>

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Package ibm updated to version 0.3.0 with previous version 0.1.0 dated 2016-06-13

Title: Individual Based Models in R
Description: Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) and methods to create new ones, particularly for population dynamics models (reproduction, mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed.
Author: Ricardo Oliveros-Ramos [aut, cre]
Maintainer: Ricardo Oliveros-Ramos <ricardo.oliveros@gmail.com>

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Package hdm updated to version 0.3.2 with previous version 0.3.1 dated 2019-01-18

Title: High-Dimensional Metrics
Description: Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.
Author: Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut], Philipp Bach [ctb]
Maintainer: Martin Spindler <martin.spindler@gmx.de>

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New package genetic.algo.optimizeR with initial version 0.2.5
Package: genetic.algo.optimizeR
Title: Genetic Algorithm Optimization
Version: 0.2.5
Description: Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package is for learning purposes and allows users to optimize various functions or parameters by mimicking biological evolution processes such as selection, crossover, and mutation. Ideal for tasks like machine learning parameter tuning, mathematical function optimization, and solving combinatorial problems.
License: MIT + file LICENSE
URL: https://danymukesha.github.io/genetic.algo.optimizeR/, https://github.com/danymukesha/genetic.algo.optimizeR
BugReports: https://github.com/danymukesha/genetic.algo.optimizeR/issues
Imports: dplyr, ggplot2, magrittr, rsconnect, stats, stringr, tinytex, biocViews
Suggests: BiocStyle, knitr, learnr, rmarkdown, spelling, testthat (>= 3.0.0)
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
NeedsCompilation: no
Packaged: 2024-02-13 19:21:37 UTC; dany.mukesha
Author: Dany Mukesha [aut, cre]
Maintainer: Dany Mukesha <danymukesha@gmail.com>
Repository: CRAN
Date/Publication: 2024-02-14 21:30:09 UTC

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New package circlesplot with initial version 1.0.0
Package: circlesplot
Title: Visualize Proportions with Circles in a Plot
Version: 1.0.0
Description: Method for visualizing proportions between objects of different sizes. The proportions are drawn as circles with different diameters, which makes them ideal for visualizing proportions between planets.
License: MIT + file LICENSE
Imports: plotrix
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), viridis
URL: https://github.com/BenSt099/circlesplot, https://benst099.github.io/circlesplot/
BugReports: https://github.com/BenSt099/circlesplot/issues
Encoding: UTF-8
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-02-13 20:02:55 UTC; PC
Author: BenSt099 [aut, cre, cph]
Maintainer: BenSt099 <benst099@proton.me>
Repository: CRAN
Date/Publication: 2024-02-14 21:30:12 UTC

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Package calibrar updated to version 0.9.0 with previous version 0.2.0 dated 2016-02-17

Title: Automated Parameter Estimation for Complex Models
Description: General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See <https://roliveros-ramos.github.io/calibrar/> for more details.
Author: Ricardo Oliveros-Ramos [aut, cre]
Maintainer: Ricardo Oliveros-Ramos <ricardo.oliveros@gmail.com>

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Package refund updated to version 0.1-35 with previous version 0.1-34 dated 2023-12-07

Title: Regression with Functional Data
Description: Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.
Author: Jeff Goldsmith [aut], Fabian Scheipl [aut], Lei Huang [aut], Julia Wrobel [aut, cre], Chongzhi Di [aut], Jonathan Gellar [aut], Jaroslaw Harezlak [aut], Mathew W. McLean [aut], Bruce Swihart [aut], Luo Xiao [aut], Ciprian Crainiceanu [aut], Philip T. [...truncated...]
Maintainer: Julia Wrobel <julia.wrobel@emory.edu>

Diff between refund versions 0.1-34 dated 2023-12-07 and 0.1-35 dated 2024-02-14

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New package trajmsm with initial version 0.1.0
Package: trajmsm
Title: Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories
Version: 0.1.0
Maintainer: Awa Diop <awa.diop.2@ulaval.ca>
Description: Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.
License: GPL (>= 3)
Encoding: UTF-8
Imports: stats, class, e1071, flexmix, geepack, ggplot2, survival, sandwich, utils
URL: https://github.com/awamaeva/R-package-trajmsm
BugReports: https://github.com/awamaeva/R-package-trajmsm/issues
NeedsCompilation: no
Packaged: 2024-02-13 17:02:59 UTC; YaHawa
Author: Awa Diop [aut, cre], Denis Talbot [aut]
Repository: CRAN
Date/Publication: 2024-02-14 17:30:02 UTC

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Package shar updated to version 2.3 with previous version 2.2.1 dated 2024-01-09

Title: Species-Habitat Associations
Description: Analyse species-habitat associations in R. Therefore, information about the location of the species (as a point pattern) is needed together with environmental conditions (as a categorical raster). To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.
Author: Maximilian H.K. Hesselbarth [aut, cre] , Marco Sciaini [aut] , Chris Wudel [aut] , Zeke Marshall [ctb] , Thomas Etherington [ctb] , Janosch Heinermann [ctb]
Maintainer: Maximilian H.K. Hesselbarth <mhk.hesselbarth@gmail.com>

Diff between shar versions 2.2.1 dated 2024-01-09 and 2.3 dated 2024-02-14

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Package pool updated to version 1.0.3 with previous version 1.0.2 dated 2024-01-18

Title: Object Pooling
Description: Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are 'DBI' connections.
Author: Joe Cheng [aut], Barbara Borges [aut], Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd]
Maintainer: Hadley Wickham <hadley@posit.co>

Diff between pool versions 1.0.2 dated 2024-01-18 and 1.0.3 dated 2024-02-14

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Package pomp updated to version 5.6 with previous version 5.5 dated 2023-12-02

Title: Statistical Inference for Partially Observed Markov Processes
Description: Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Author: Aaron A. King [aut, cre] , Edward L. Ionides [aut] , Carles Breto [aut] , Stephen P. Ellner [ctb] , Matthew J. Ferrari [ctb], Sebastian Funk [ctb] , Steven G. Johnson [ctb], Bruce E. Kendall [ctb] , Michael Lavine [ctb], Dao Nguyen [ctb] , Eamon B. O [...truncated...]
Maintainer: Aaron A. King <kingaa@umich.edu>

Diff between pomp versions 5.5 dated 2023-12-02 and 5.6 dated 2024-02-14

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New package multe with initial version 1.0.0
Package: multe
Title: Multiple Treatment Effects Regression
Version: 1.0.0
Description: Implements contamination bias diagnostics and alternative estimators for regressions with multiple treatments. The implementation is based on Goldsmith-Pinkham, Hull, and Kolesár (2022) <arXiv:2106.05024>.
Depends: R (>= 4.3.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: stats, nnet
Suggests: spelling, knitr, formatR, rmarkdown, testthat (>= 3.0.0)
Language: en-US
URL: https://github.com/kolesarm/multe
BugReports: https://github.com/kolesarm/multe/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-02-13 18:22:24 UTC; kolesarm
Author: Michal Kolesar [aut, cre] , Paul Goldsmith-Pinkham [ctb], Peter Hull [ctb]
Maintainer: Michal Kolesar <kolesarmi@googlemail.com>
Repository: CRAN
Date/Publication: 2024-02-14 17:30:12 UTC

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New package GSNA with initial version 0.1.4.1
Package: GSNA
Title: Gene Set Networking Analysis Package
Version: 0.1.4.1
Description: Create networks of gene sets, infer clusters of functionally-related gene sets based on similarity statistics, and visualize the results. This package simplifies and accelerates interpretation of pathways analysis data sets. It is designed to work in tandem with standard pathways analysis methods, such as the 'GSEA' program (Gene Set Enrichment Analysis), CERNO (Coincident Extreme Ranks in Numerical Observations, implemented in the 'tmod' package) and others. Inputs to 'GSNA' are the outputs of pathways analysis methods: a list of gene sets (or "modules"), pathways or GO-terms with associated p-values. Since pathways analysis methods may be used to analyze many different types of data including transcriptomic, epigenetic, and high-throughput screen data sets, the 'GSNA' pipeline is applicable to these data as well. The use of 'GSNA' has been described in the following papers: Collins DR, Urbach JM, Racenet ZJ, Arshad U, Power KA, Newman RM, et al. (2021) <doi:10.1016/j.immuni.2021.0 [...truncated...]
Language: en-US
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: circlize, DT, dendextend, dplyr, ggplot2, graphics, grDevices, igraph, Matrix, methods, psych, raster, stringr, stringi, stats, tibble, tidyr, tmod, utils, withr, Rcpp
Depends: R (>= 3.5.0)
Suggests: gplots, GEOquery, knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2024-02-13 03:13:41 UTC; jonathan
Author: Jonathan M Urbach [aut, cre]
Maintainer: Jonathan M Urbach <jurbach@mgh.harvard.edu>
Repository: CRAN
Date/Publication: 2024-02-14 17:30:15 UTC

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Package FuzzyClass updated to version 0.1.6 with previous version 0.1.5 dated 2023-08-17

Title: Fuzzy and Non-Fuzzy Classifiers
Description: It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) <doi:10.1007/s40815-020-00936-4>.
Author: Jodavid Ferreira [aut, cre] , Ronei Moraes [ctb] , Arthur Ricardo [ctb]
Maintainer: Jodavid Ferreira <jodavid@protonmail.com>

Diff between FuzzyClass versions 0.1.5 dated 2023-08-17 and 0.1.6 dated 2024-02-14

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Package funData updated to version 1.3-9 with previous version 1.3-8 dated 2021-10-17

Title: An S4 Class for Functional Data
Description: S4 classes for univariate and multivariate functional data with utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description of the package functionalities and its interplay with the MFPCA package for multivariate functional principal component analysis <https://CRAN.R-project.org/package=MFPCA>.
Author: Clara Happ-Kurz [aut, cre]
Maintainer: Clara Happ-Kurz <chk_R@gmx.de>

Diff between funData versions 1.3-8 dated 2021-10-17 and 1.3-9 dated 2024-02-14

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Package bvhar updated to version 2.0.0 with previous version 1.2.0 dated 2024-01-09

Title: Bayesian Vector Heterogeneous Autoregressive Modeling
Description: Tools to research Bayesian Vector heterogeneous autoregressive (VHAR) model, referring to Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.
Author: Young Geun Kim [aut, cre, cph] , Changryong Baek [ctb]
Maintainer: Young Geun Kim <ygeunkimstat@gmail.com>

Diff between bvhar versions 1.2.0 dated 2024-01-09 and 2.0.0 dated 2024-02-14

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Package wrProteo updated to version 1.11.0.1 with previous version 1.10.1 dated 2023-08-18

Title: Proteomics Data Analysis Functions
Description: Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium <doi:10.1093/nar/gky1049>) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 <doi:10.1038/nprot.2016.136>), Dia-NN (Demichev et al 2020 <doi:10.1038/s41592-019-0638-x>), Fragpipe(da Veiga et al 2020 <doi:10.1038/s41592-020-0912-y>), MassChroq (Valot et al 2011 <doi:10.1002/pmic.201100120>), OpenMS (Strauss et al 2021 <doi:10.1038/nmeth.3959>), ProteomeDiscoverer (Orsburn 2021 <doi:10.3390/proteomes9010015>), Proline (Bouyssie et al 2020 <doi:10.1093/bioinformatics/btaa118>), AlphaPept (prepri [...truncated...]
Author: Wolfgang Raffelsberger [aut, cre]
Maintainer: Wolfgang Raffelsberger <w.raffelsberger@gmail.com>

Diff between wrProteo versions 1.10.1 dated 2023-08-18 and 1.11.0.1 dated 2024-02-14

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Package clustNet updated to version 1.2.0 with previous version 1.1.0 dated 2023-09-27

Title: Network-Based Clustering
Description: Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
Author: Fritz Bayer [aut, cre, cph], Jack Kuipers [ctb]
Maintainer: Fritz Bayer <frbayer@ethz.ch>

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Package see updated to version 0.8.2 with previous version 0.8.1 dated 2023-11-03

Title: Model Visualisation Toolbox for 'easystats' and 'ggplot2'
Description: Provides plotting utilities supporting packages in the 'easystats' ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for 'ggplot2'. Color scales are based on <https://materialui.co/colors>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.
Author: Daniel Luedecke [aut, ctb] , Dominique Makowski [aut, inv] , Indrajeet Patil [aut, cre] , Mattan S. Ben-Shachar [aut, ctb] , Brenton M. Wiernik [aut, ctb] , Philip Waggoner [aut, ctb] , Jeffrey R. Stevens [ctb] , Matthew Smith [rev] , Jakob Bossek [re [...truncated...]
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package hbamr updated to version 2.2.0 with previous version 2.1.2 dated 2024-02-07

Title: Hierarchical Bayesian Aldrich-McKelvey Scaling via 'Stan'
Description: Perform hierarchical Bayesian Aldrich-McKelvey scaling using Hamiltonian Monte Carlo via 'Stan'. Aldrich-McKelvey ('AM') scaling is a method for estimating the ideological positions of survey respondents and political actors on a common scale using positional survey data. The hierarchical versions of the Bayesian 'AM' model included in this package outperform other versions both in terms of yielding meaningful posterior distributions for respondent positions and in terms of recovering true respondent positions in simulations. The package contains functions for preparing data, fitting models, extracting estimates, plotting key results, and comparing models using cross-validation. The original version of the default model is described in Bølstad (2024) <doi:10.1017/pan.2023.18>.
Author: Joergen Boelstad [aut, cre]
Maintainer: Joergen Boelstad <jorgen.bolstad@stv.uio.no>

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Package stagedtrees updated to version 2.3.0 with previous version 2.2.1 dated 2022-04-28

Title: Staged Event Trees
Description: Creates and fits staged event tree probability models, which are probabilistic graphical models capable of representing asymmetric conditional independence statements for categorical variables. Includes functions to create, plot and fit staged event trees from data, as well as many efficient structure learning algorithms. References: Carli F, Leonelli M, Riccomagno E, Varando G (2022). <doi: 10.18637/jss.v102.i06>. Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604). Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) <arXiv:1705.09457>. Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>. Barclay L. M., Hutton J. L. and Smith J. Q. (2013) <doi:10.1016/j.ijar.2013.05.006>. Smith J. Q. and Anderson P. E. (2008) <doi:10.1016/j.artint.2007.05.004>.
Author: Gherardo Varando [aut, cre] , Federico Carli [aut], Manuele Leonelli [aut] , Eva Riccomagno [aut]
Maintainer: Gherardo Varando <gherardo.varando@gmail.com>

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Package ggnetwork updated to version 0.5.13 with previous version 0.5.12 dated 2023-03-06

Title: Geometries to Plot Networks with 'ggplot2'
Description: Geometries to plot network objects with 'ggplot2'.
Author: Francois Briatte [aut, cre] , Michal Bojanowski [ctb] , Mickael Canouil [ctb] , Zachary Charlop-Powers [ctb] , Jacob C. Fisher [ctb] , Kipp Johnson [ctb] , Tyler Rinker [ctb]
Maintainer: Francois Briatte <f.briatte@gmail.com>

Diff between ggnetwork versions 0.5.12 dated 2023-03-06 and 0.5.13 dated 2024-02-14

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New package discourseGT with initial version 1.2.0
Package: discourseGT
Title: Analyze Group Patterns using Graph Theory in Educational Settings
Version: 1.2.0
Description: Analyzes group patterns using discourse analysis data with graph theory mathematics. Takes the order of which individuals talk and converts it to a network edge and weight list. Returns the density, centrality, centralization, and subgroup information for each group. Based on the analytical framework laid out in Chai et al. (2019) <doi:10.1187/cbe.18-11-0222>.
License: MIT + file LICENSE
Depends: R (>= 3.5.0)
Imports: dplyr, GGally, ggplot2, ggrepel, graphics, igraph, network, stats, utils
Suggests: BiocManager, formatR, ggpubr, knitr, markdown, R.rsp, rmarkdown, rticles, sna, testthat (>= 3.0.0)
VignetteBuilder: knitr, R.rsp
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2023-07-14 21:52:53 UTC; brutestep
Author: Albert Chai [aut], Andrew Lee [aut], Joshua Le [aut], Katherine Ly [ctb], Kevin Banh [ctb], Priya Pahal [ctb], Jitarth Sheth [aut], Qi Cui [aut, cre], Stanley Lo [aut]
Maintainer: Qi Cui <q1cui@ucsd.edu>
Repository: CRAN
Date/Publication: 2023-07-19 07:20:02 UTC

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Package validate updated to version 1.1.5 with previous version 1.1.3 dated 2023-03-28

Title: Data Validation Infrastructure
Description: Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6 and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.
Author: Mark van der Loo [cre, aut] , Edwin de Jonge [aut] , Paul Hsieh [ctb]
Maintainer: Mark van der Loo <mark.vanderloo@gmail.com>

Diff between validate versions 1.1.3 dated 2023-03-28 and 1.1.5 dated 2024-02-14

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Package Trading updated to version 3.0 with previous version 2.5 dated 2022-08-26

Title: CCR, Advanced Correlation & Beta Estimates, Betting Strategies
Description: Contains performance analysis metrics of track records including entropy-based correlation and dynamic beta based on the Kalman filter. The normalized sample entropy method has been implemented which produces accurate entropy estimation even on smaller datasets while for the dynamic beta calculation the Kalman filter methodology has been utilized. On a separate stream, trades from the five major assets classes and also functionality to use pricing curves, rating tables, CSAs and add-on tables. The implementation follows an object oriented logic whereby each trade inherits from more abstract classes while also the curves/tables are objects. Furthermore, odds calculators and P&L back-testing functionality has been implemented for the most widely used betting/trading strategies including martingale, DAlembert, Labouchere and Fibonacci. Back-testing has also been included for the EuroMillions and EuroJackpot lotteries. Furthermore, some basic functionality about climate risk has been i [...truncated...]
Author: Tasos Grivas
Maintainer: Tasos Grivas <info@openriskcalculator.com>

Diff between Trading versions 2.5 dated 2022-08-26 and 3.0 dated 2024-02-14

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Package survival updated to version 3.5-8 with previous version 3.5-7 dated 2023-08-14

Title: Survival Analysis
Description: Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.
Author: Terry M Therneau [aut, cre], Thomas Lumley [ctb, trl] , Atkinson Elizabeth [ctb], Crowson Cynthia [ctb]
Maintainer: Terry M Therneau <therneau.terry@mayo.edu>

Diff between survival versions 3.5-7 dated 2023-08-14 and 3.5-8 dated 2024-02-14

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Package prqlr updated to version 0.8.0 with previous version 0.7.0 dated 2024-01-08

Title: R Bindings for the 'prqlc' Rust Library
Description: Provides a function to convert 'PRQL' strings to 'SQL' strings. Combined with other R functions that take 'SQL' as an argument, 'PRQL' can be used on R.
Author: Tatsuya Shima [aut, cre], Authors of the dependency Rust crates [aut]
Maintainer: Tatsuya Shima <ts1s1andn@gmail.com>

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Package sra updated to version 0.1.4.1 with previous version 0.1.4 dated 2023-03-08

Title: Selection Response Analysis
Description: Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R fun [...truncated...]
Author: Arnaud Le Rouzic
Maintainer: Arnaud Le Rouzic <arnaud.le-rouzic@universite-paris-saclay.fr>

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Package FuzzyPovertyR updated to version 2.0.1 with previous version 2.0.0 dated 2024-02-12

Title: Estimation of Fuzzy Poverty Measures
Description: Estimates fuzzy measures of poverty and deprivation. It also estimates the sampling variance of these measures using bootstrap or jackknife repeated replications.
Author: Federico Crescenzi [aut, cre] , Lorenzo Mori [aut] , Gianni Betti [ctb]
Maintainer: Federico Crescenzi <federico.crescenzi@unitus.it>

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Package discourseGT (with last version 1.2.0) was removed from CRAN

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

2023-07-19 1.2.0
2022-10-27 1.1.8
2021-11-03 1.1.7
2021-06-18 1.1.6
2021-04-02 1.1.5
2021-02-17 1.1.4
2021-01-18 1.1.3
2020-12-08 1.1.2
2020-10-29 1.1.1
2020-09-25 1.1.0
2020-02-17 1.0.0

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Package parlitools (with last version 0.4.1) was removed from CRAN

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

2020-01-12 0.4.1
2020-01-11 0.4.0
2019-09-15 0.3.4
2019-05-30 0.3.3
2019-03-22 0.3.1
2019-01-07 0.3.0
2017-09-23 0.2.1
2017-08-10 0.2.0
2017-07-15 0.1.0

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Package waved updated to version 1.3 with previous version 1.2 dated 2018-05-29

Title: Wavelet Deconvolution
Description: Makes available code necessary to reproduce figures and tables in papers on the WaveD method for wavelet deconvolution of noisy signals as presented in The WaveD Transform in R, Journal of Statistical Software Volume 21, No. 3, 2007.
Author: Marc Raimondo <marcr@maths.usyd.edu.au> and Michael Stewart <michael.stewart@sydney.edu.au>
Maintainer: Michael Stewart <michael.stewart@sydney.edu.au>

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Package quantmod updated to version 0.4.26 with previous version 0.4.25 dated 2023-08-22

Title: Quantitative Financial Modelling Framework
Description: Specify, build, trade, and analyse quantitative financial trading strategies.
Author: Jeffrey A. Ryan [aut, cph], Joshua M. Ulrich [cre, aut], Ethan B. Smith [ctb], Wouter Thielen [ctb], Paul Teetor [ctb], Steve Bronder [ctb]
Maintainer: Joshua M. Ulrich <josh.m.ulrich@gmail.com>

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Package jsmodule updated to version 1.5.0 with previous version 1.4.4 dated 2023-12-06

Title: 'RStudio' Addins and 'Shiny' Modules for Medical Research
Description: 'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
Author: Jinseob Kim [aut, cre] , Zarathu [cph, fnd], Hyunki Lee [aut], Changwoo Lim [aut], Jinhwan Kim [aut], Yoonkyoung Jeon [aut]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>

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Package CodelistGenerator updated to version 2.2.2 with previous version 2.2.1 dated 2024-02-07

Title: Identify Relevant Clinical Codes and Evaluate Their Use
Description: Generate a candidate code list for the Observational Medical Outcomes Partnership (OMOP) common data model based on string matching. For a given search strategy, a candidate code list will be returned.
Author: Edward Burn [aut, cre] , Marti Catala [ctb]
Maintainer: Edward Burn <edward.burn@ndorms.ox.ac.uk>

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Package brulee updated to version 0.3.0 with previous version 0.2.0 dated 2022-09-19

Title: High-Level Modeling Functions with 'torch'
Description: Provides high-level modeling functions to define and train models using the 'torch' R package. Models include linear, logistic, and multinomial regression as well as multilayer perceptrons.
Author: Max Kuhn [aut, cre] , Daniel Falbel [aut], Posit Software, PBC [cph, fnd]
Maintainer: Max Kuhn <max@posit.co>

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Package vaccine updated to version 1.2.0 with previous version 1.1.0 dated 2023-12-04

Title: Statistical Tools for Immune Correlates Analysis of Vaccine Clinical Trial Data
Description: Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023) <doi:10.48550/arXiv.2208.06465>.
Author: Avi Kenny [aut, cre, cph]
Maintainer: Avi Kenny <avi.kenny@gmail.com>

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Package msma updated to version 3.1 with previous version 3.0 dated 2023-08-25

Title: Multiblock Sparse Multivariable Analysis
Description: Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.
Author: Atsushi Kawaguchi
Maintainer: Atsushi Kawaguchi <kawa_a24@yahoo.co.jp>

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