Fri, 15 Mar 2024

Package BTSPAS updated to version 2024.4.1 with previous version 2021.11.2 dated 2021-10-25

Title: Bayesian Time-Stratified Population Analysis
Description: Provides advanced Bayesian methods to estimate abundance and run-timing from temporally-stratified Petersen mark-recapture experiments. Methods include hierarchical modelling of the capture probabilities and spline smoothing of the daily run size. Theory described in Bonner and Schwarz (2011) <doi:10.1111/j.1541-0420.2011.01599.x>.
Author: Carl J Schwarz <cschwarz.stat.sfu.ca@gmail.com> and Simon J Bonner <sbonner6@uwo.ca>
Maintainer: Carl J Schwarz <cschwarz.stat.sfu.ca@gmail.com>

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Package teal.reporter updated to version 0.3.1 with previous version 0.3.0 dated 2024-02-16

Title: Reporting Tools for 'shiny' Modules
Description: Prebuilt 'shiny' modules containing tools for the generation of 'rmarkdown' reports, supporting reproducible research and analysis.
Author: Dawid Kaledkowski [aut, cre] , Kartikeya Kirar [aut] , Marcin Kosinski [aut], Maciej Nasinski [aut], Konrad Pagacz [aut], Mahmoud Hallal [aut], Chendi Liao [rev], Dony Unardi [rev], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Dawid Kaledkowski <dawid.kaledkowski@roche.com>

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Package SpatialDDLS updated to version 1.0.1 with previous version 1.0.0 dated 2023-12-06

Title: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks
Description: Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.
Author: Diego Mananes [aut, cre] , Carlos Torroja [aut] , Fatima Sanchez-Cabo [aut]
Maintainer: Diego Mananes <dmananesc@cnic.es>

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Package sass updated to version 0.4.9 with previous version 0.4.8 dated 2023-12-05

Title: Syntactically Awesome Style Sheets ('Sass')
Description: An 'SCSS' compiler, powered by the 'LibSass' library. With this, R developers can use variables, inheritance, and functions to generate dynamic style sheets. The package uses the 'Sass CSS' extension language, which is stable, powerful, and CSS compatible.
Author: Joe Cheng [aut], Timothy Mastny [aut], Richard Iannone [aut] , Barret Schloerke [aut] , Carson Sievert [aut, cre] , Christophe Dervieux [ctb] , RStudio [cph, fnd], Sass Open Source Foundation [ctb, cph] , Greter Marcel [ctb, cph] , Mifsud Michael [ct [...truncated...]
Maintainer: Carson Sievert <carson@rstudio.com>

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Package KSgeneral updated to version 1.1.3 with previous version 1.1.2 dated 2023-07-10

Title: Computing P-Values of the K-S Test for (Dis)Continuous Null Distribution
Description: Computes a p-value of the one-sample two-sided (or one-sided, as a special case) Kolmogorov-Smirnov (KS) statistic, for any fixed critical level, and an arbitrary, possibly large sample size for a pre-specified purely discrete, mixed or continuous cumulative distribution function (cdf) under the null hypothesis. If a data sample is supplied, 'KSgeneral' computes the p-value corresponding to the value of the KS test statistic computed based on the user provided data sample. The package 'KSgeneral' implements a novel, accurate and efficient method named Exact-KS-FFT, expressing the p-value as a double-boundary non-crossing probability for a homogeneous Poisson process, which is then efficiently computed using Fast Fourier Transform (FFT). The package can also be used to compute and plot the complementary cdf of the KS statistic which is known to depend on the hypothesized distribution when the latter is discontinuous (i.e. purely discrete or mixed). To cite this package in publication us [...truncated...]
Author: Dimitrina S. Dimitrova <D.Dimitrova@city.ac.uk>, Yun Jia <yunjia2019@gmail.com>, Vladimir K. Kaishev <Vladimir.Kaishev.1@city.ac.uk> and Senren Tan <raymondtsrtsr@outlook.com>
Maintainer: Dimitrina S. Dimitrova <D.Dimitrova@city.ac.uk>

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Package insight updated to version 0.19.9 with previous version 0.19.8 dated 2024-01-31

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

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Package emuR updated to version 2.5.0 with previous version 2.4.2 dated 2023-11-03

Title: Main Package of the EMU Speech Database Management System
Description: Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/> for more details.
Author: Markus Jochim [aut, cre] , Raphael Winkelmann [aut], Klaus Jaensch [aut, ctb], Steve Cassidy [aut, ctb], Jonathan Harrington [aut, ctb]
Maintainer: Markus Jochim <markusjochim@phonetik.uni-muenchen.de>

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Package AMAPVox updated to version 2.2.1 with previous version 1.0.1 dated 2023-06-22

Title: LiDAR Data Voxelisation
Description: Read, manipulate and write voxel spaces. Voxel spaces are read from text-based output files of the 'AMAPVox' software. 'AMAPVox' is a LiDAR point cloud voxelisation software that aims at estimating leaf area through several theoretical/numerical approaches. See more in the article Vincent et al. (2017) <doi:10.23708/1AJNMP> and the technical note Vincent et al. (2021) <doi:10.23708/1AJNMP>.
Author: Gregoire Vincent [aut], Julien Heurtebize [aut], Philippe Verley [aut, cre]
Maintainer: Philippe Verley <philippe.verley@ird.fr>

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Package visR updated to version 0.4.1 with previous version 0.4.0 dated 2023-11-20

Title: Clinical Graphs and Tables Adhering to Graphical Principles
Description: To enable fit-for-purpose, reusable clinical and medical research focused visualizations and tables with sensible defaults and based on graphical principles as described in: "Vandemeulebroecke et al. (2018)" <doi:10.1002/pst.1912>, "Vandemeulebroecke et al. (2019)" <doi:10.1002/psp4.12455>, and "Morris et al. (2019)" <doi:10.1136/bmjopen-2019-030215>.
Author: Mark Baillie [aut, cre, cph], Diego Saldana [aut], Charlotta Fruechtenicht [aut], Marc Vandemeulebroecke [aut], Thanos Siadimas [aut], Pawel Kawski [aut], Steven Haesendonckx [aut], James Black [aut], Pelagia Alexandra Papadopoulou [aut], Tim Treis [ [...truncated...]
Maintainer: Mark Baillie <bailliem@gmail.com>

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Package frailtypack updated to version 3.6.0 with previous version 3.5.1 dated 2023-11-25

Title: Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints
Description: The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested fra [...truncated...]
Author: Virginie Rondeau [aut, cre] , Juan R. Gonzalez [aut], Yassin Mazroui [aut], Audrey Mauguen [aut], Amadou Diakite [aut], Alexandre Laurent [aut], Myriam Lopez [aut], Agnieszka Krol [aut], Casimir L. Sofeu [aut], Julien Dumerc [aut], Denis Rustand [aut [...truncated...]
Maintainer: Virginie Rondeau <virginie.rondeau@u-bordeaux.fr>

Diff between frailtypack versions 3.5.1 dated 2023-11-25 and 3.6.0 dated 2024-03-15

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Package folders updated to version 0.1.0 with previous version 0.0.6 dated 2023-03-14

Title: Standardized Folder Names
Description: Supports the use of standardized folder names.
Author: Brian High [aut, cre], University of Washington [cph, fnd]
Maintainer: Brian High <bhigh@live.com>

Diff between folders versions 0.0.6 dated 2023-03-14 and 0.1.0 dated 2024-03-15

 DESCRIPTION                           |    6 -
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 README.md                             |  166 +++++++++++++++++++++++-----------
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 man/get_folders.Rd                    |   20 +---
 tests/testthat/test-cleanup_folders.R |  111 +++++++++++++++++++---
 tests/testthat/test-create_folders.R  |    9 +
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Package aifeducation updated to version 0.3.2 with previous version 0.3.1 dated 2024-02-18

Title: Artificial Intelligence for Education
Description: In social and educational settings, the use of Artificial Intelligence (AI) is a challenging task. Relevant data is often only available in handwritten forms, or the use of data is restricted by privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences, data is often unbalanced in terms of frequencies. To support educators as well as educational and social researchers in using the potentials of AI for their work, this package provides a unified interface for neural nets in 'keras', 'tensorflow', and 'pytorch' to deal with natural language problems. In addition, the package ships with a shiny app, providing a graphical user interface. This allows the usage of AI for people without skills in writing python/R scripts. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Lee (2013) <https://www.researchgate.net/publication/280581078_Pseudo-Label_The_Simple_and_Efficie [...truncated...]
Author: Berding Florian [aut, cre] , Pargmann Julia [ctb], Riebenbauer Elisabeth [ctb], Rebmann Karin [ctb], Slopinski Andreas [ctb]
Maintainer: Berding Florian <florian.berding@uni-hamburg.de>

Diff between aifeducation versions 0.3.1 dated 2024-02-18 and 0.3.2 dated 2024-03-15

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More information about aifeducation at CRAN
Permanent link

Package PCMBase updated to version 1.2.14 with previous version 1.2.13 dated 2022-11-18

Title: Simulation and Likelihood Calculation of Phylogenetic Comparative Models
Description: Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above model [...truncated...]
Author: Venelin Mitov [aut, cre, cph] , Krzysztof Bartoszek [ctb], Georgios Asimomitis [ctb], Tanja Stadler [ths]
Maintainer: Venelin Mitov <vmitov@gmail.com>

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Package dinamic updated to version 1.0.1 with previous version 1.0 dated 2011-11-09

Title: A Method to Analyze Recurrent DNA Copy Number Aberrations in Tumors
Description: In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. This package implements the DiNAMIC procedure for assessing the statistical significance of recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
Author: Vonn Walter [aut, cre] , Andrew B. Nobel [aut], Fred A. Wright [aut]
Maintainer: Vonn Walter <vwalter1@pennstatehealth.psu.edu>

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Package tfrmt updated to version 0.1.1 with previous version 0.1.0 dated 2023-10-17

Title: Applies Display Metadata to Analysis Results Datasets
Description: Creates a framework to store and apply display metadata to Analysis Results Datasets (ARDs). The use of 'tfrmt' allows users to define table format and styling without the data, and later apply the format to the data.
Author: Becca Krouse [aut, cre], Christina Fillmore [aut] , GlaxoSmithKline Research & Development Limited [cph, fnd], Atorus Research LLC [cph, fnd], Ellis Hughes [aut] , Karima Ahmad [aut] , Shannon Haughton [aut]
Maintainer: Becca Krouse <becca.z.krouse@gsk.com>

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Package moodlequizR updated to version 2.0.2 with previous version 1.0.3 dated 2023-09-12

Title: Easily Create Fully Randomized 'Moodle' Test Questions
Description: Routines to generate fully randomized 'moodle' quizzes. It also contains 12 examples and a 'shiny' app.
Author: Wolfgang Rolke [aut, cre]
Maintainer: Wolfgang Rolke <wolfgang.rolke@upr.edu>

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Package manynet updated to version 0.4.4 with previous version 0.4.1 dated 2024-01-24

Title: Many Ways to Make, Modify, Mark, and Map Myriad Networks
Description: A set of tools for making, modifying, marking, and mapping 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>

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Package flashr updated to version 0.1.2 with previous version 0.1.1 dated 2023-03-28

Title: Create Flashcards of Terms and Definitions
Description: Provides functions for creating flashcard decks of terms and definitions. This package creates HTML slides using 'revealjs' that can be viewed in the 'RStudio' viewer or a web browser. Users can create flashcards from either existing built-in decks or create their own from CSV files or vectors of function names.
Author: Jeffrey R. Stevens [aut, cre, cph]
Maintainer: Jeffrey R. Stevens <jeffrey.r.stevens@protonmail.com>

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Package rirods updated to version 0.2.0 with previous version 0.1.2 dated 2023-11-02

Title: R Client for 'iRODS'
Description: The open sourced data management software 'Integrated Rule-Oriented Data System' ('iRODS') offers solutions for the whole data life cycle (<https://irods.org/>). The loosely constructed and highly configurable architecture of 'iRODS' frees the user from strict formatting constraints and single-vendor solutions. This package provides an interface to the 'iRODS' HTTP API, allowing you to manage your data and metadata in 'iRODS' with R. Storage of annotated files and R objects in 'iRODS' ensures findability, accessibility, interoperability, and reusability of data.
Author: Martin Schobben [aut, cre, cph] , Mariana Montes [aut], Terrell Russell [ctb], Christine Staiger [ctb], Ton Smeele [ctb], Alan King [ctb]
Maintainer: Martin Schobben <schobbenmartin@gmail.com>

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Package mlmpower updated to version 1.0.6 with previous version 1.0.5 dated 2024-01-29

Title: Power Analysis and Data Simulation for Multilevel Models
Description: A declarative language for specifying multilevel models, solving for population parameters based on specified variance-explained effect size measures, generating data, and conducting power analyses to determine sample size recommendations. The specification allows for any number of within-cluster effects, between-cluster effects, covariate effects at either level, and random coefficients. Moreover, the models do not assume orthogonal effects, and predictors can correlate at either level and accommodate models with multiple interaction effects.
Author: Brian T. Keller [aut, cre, cph]
Maintainer: Brian T. Keller <btkeller@missouri.edu>

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Package poppr updated to version 2.9.6 with previous version 2.9.5 dated 2024-01-15

Title: Genetic Analysis of Populations with Mixed Reproduction
Description: Population genetic analyses for hierarchical analysis of partially clonal populations built upon the architecture of the 'adegenet' package. Originally described in Kamvar, Tabima, and Grünwald (2014) <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) <doi:10.3389/fgene.2015.00208>.
Author: Zhian N. Kamvar [cre, aut] , Javier F. Tabima [aut] , Sydney E. Everhart [ctb, dtc] , Jonah C. Brooks [aut], Stacy A. Krueger-Hadfield [ctb] , Erik Sotka [ctb], Brian J. Knaus [ctb] , Patrick G. Meirmans [ctb] , Frederic D. Chevalier [ctb] , David Fo [...truncated...]
Maintainer: Zhian N. Kamvar <zkamvar@gmail.com>

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Package topr updated to version 2.0.1 with previous version 2.0.0 dated 2024-02-04

Title: Create Custom Plots for Viewing Genetic Association Results
Description: A collection of functions for visualizing,exploring and annotating genetic association results.Association results from multiple traits can be viewed simultaneously along with gene annotation, over the entire genome (Manhattan plot) or in the more detailed regional view.
Author: Thorhildur Juliusdottir [cph, aut, cre], Andri Stefansson [aut]
Maintainer: Thorhildur Juliusdottir <totajuliusd@gmail.com>

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Package qtl2ggplot updated to version 1.2.4 with previous version 1.2.2 dated 2023-03-11

Title: Data Visualization for QTL Experiments
Description: Functions to plot QTL (quantitative trait loci) analysis results and related diagnostics. Part of 'qtl2', an upgrade of the 'qtl' package to better handle high-dimensional data and complex cross designs.
Author: Brian S Yandell [aut, cre], Karl W Broman [aut]
Maintainer: Brian S Yandell <brian.yandell@wisc.edu>

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Package giscoR updated to version 0.4.1 with previous version 0.4.0 dated 2023-10-30

Title: Download Map Data from GISCO API - Eurostat
Description: Tools to download data from the GISCO (Geographic Information System of the Commission) Eurostat database <https://ec.europa.eu/eurostat/web/gisco>. Global and European map data available. This package is in no way officially related to or endorsed by Eurostat.
Author: Diego Hernangomez [aut, cre, cph] , EuroGeographics [cph] , Vincent Arel-Bundock [cph]
Maintainer: Diego Hernangomez <diego.hernangomezherrero@gmail.com>

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Package formatdown updated to version 0.1.3 with previous version 0.1.2 dated 2023-06-21

Title: Formatting Tools for 'rmarkdown' Documents
Description: Provides a small set of tools for formatting tasks when creating documents in R Markdown or Quarto Markdown. Convert the elements of a numerical vector to character strings in one of several forms: powers-of-ten notation in engineering or scientific form delimited for rendering as inline equations; integer or decimal notation delimited for equation rendering; numbers with measurement units (non-delimited) where units are selected to eliminate the need for powers-of-ten or scientific notation. Useful for rendering a numerical scalar in an inline R code chunk as well as formatting columns of data frames displayed in a table.
Author: Richard Layton [aut, cre]
Maintainer: Richard Layton <graphdoctor@gmail.com>

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Package saeRobust updated to version 0.5.0 with previous version 0.4.0 dated 2023-01-18

Title: Robust Small Area Estimation
Description: Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.
Author: Sebastian Warnholz [aut, cre]
Maintainer: Sebastian Warnholz <wahani@gmail.com>

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Package howManyImputations updated to version 0.2.5 with previous version 0.2.4 dated 2023-02-05

Title: Calculate How many Imputations are Needed for Multiple Imputation
Description: When performing multiple imputations, while 5-10 imputations are sufficient for obtaining point estimates, a larger number of imputations are needed for proper standard error estimates. This package allows you to calculate how many imputations are needed, following the work of von Hippel (2020) <doi:10.1177/0049124117747303>.
Author: Josh Errickson [aut, cre]
Maintainer: Josh Errickson <jerrick@umich.edu>

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Package finbif updated to version 0.9.5 with previous version 0.9.4 dated 2024-01-12

Title: Interface for the 'Finnish Biodiversity Information Facility' API
Description: A programmatic interface to the 'Finnish Biodiversity Information Facility' ('FinBIF') API (<https://api.laji.fi>). 'FinBIF' aggregates Finnish biodiversity data from multiple sources in a single open access portal for researchers, citizen scientists, industry and government. 'FinBIF' allows users of biodiversity information to find, access, combine and visualise data on Finnish plants, animals and microorganisms. The 'finbif' package makes the publicly available data in 'FinBIF' easily accessible to programmers. Biodiversity information is available on taxonomy and taxon occurrence. Occurrence data can be filtered by taxon, time, location and other variables. The data accessed are conveniently preformatted for subsequent analyses.
Author: Finnish Museum of Natural History - Luomus [cph], William K. Morris [aut, cre]
Maintainer: William K. Morris <willi@mmorris.email>

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Package fable updated to version 0.3.4 with previous version 0.3.3 dated 2023-03-22

Title: Forecasting Models for Tidy Time Series
Description: Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.
Author: Mitchell O'Hara-Wild [aut, cre], Rob Hyndman [aut], Earo Wang [aut], Gabriel Caceres [ctb] , Christoph Bergmeir [ctb] , Tim-Gunnar Hensel [ctb], Timothy Hyndman [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Package ripc updated to version 0.2.1 with previous version 0.2.0 dated 2023-09-06

Title: Download and Tidy IPC and CH Data
Description: Utilities to access Integrated Food Security Phase Classification (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are available for all of the 'IPC-CH' Public API (<https://docs.api.ipcinfo.org>) simplified and advanced endpoints to easily download the data in a clean and tidy format.
Author: Seth Caldwell [aut, cre, cph]
Maintainer: Seth Caldwell <caldwellst@gmail.com>

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Package EGAnet updated to version 2.0.5 with previous version 2.0.4 dated 2024-01-22

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

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Package bnlearn updated to version 4.9.3 with previous version 4.9.2 dated 2024-03-12

Title: Bayesian Network Structure Learning, Parameter Learning and Inference
Description: Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.
Author: Marco Scutari [aut, cre], Tomi Silander [ctb], Robert Ness [ctb]
Maintainer: Marco Scutari <scutari@bnlearn.com>

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Package BayesXsrc updated to version 3.0-5 with previous version 3.0-4 dated 2023-02-03

Title: Distribution of the 'BayesX' C++ Sources
Description: 'BayesX' performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the 'BayesX' command line tool for easy installation. A convenient R interface is provided in package R2BayesX.
Author: Nikolaus Umlauf [aut, cre] , Daniel Adler [aut], Thomas Kneib [aut], Stefan Lang [aut], Achim Zeileis [aut]
Maintainer: Nikolaus Umlauf <Nikolaus.Umlauf@uibk.ac.at>

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Package BayesMallows updated to version 2.1.1 with previous version 2.1.0 dated 2024-03-13

Title: Bayesian Preference Learning with the Mallows Rank Model
Description: An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>; Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>; Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank mo [...truncated...]
Author: Oystein Sorensen [aut, cre] , Waldir Leoncio [aut], Valeria Vitelli [aut] , Marta Crispino [aut], Qinghua Liu [aut], Cristina Mollica [aut], Luca Tardella [aut], Anja Stein [aut]
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>

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Package templates updated to version 0.4.0 with previous version 0.3.0 dated 2018-05-22

Title: A System for Working with Templates
Description: Provides tools to work with template code and text in R. It aims to provide a simple substitution mechanism for R-expressions inside these templates. Templates can be written in other languages like 'SQL', can simply be represented by characters in R, or can themselves be R-expressions or functions.
Author: Sebastian Warnholz [aut, cre]
Maintainer: Sebastian Warnholz <wahani@gmail.com>

Diff between templates versions 0.3.0 dated 2018-05-22 and 0.4.0 dated 2024-03-15

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 NAMESPACE                               |    4 
 NEWS                                    |    6 
 R/NAMESPACE.R                           |    6 
 R/templateUtilities.R                   |    6 
 build/vignette.rds                      |binary
 inst/doc/Template_Programming_in_R.R    |   14 
 inst/doc/Template_Programming_in_R.html |  455 ++++++++++++++++++++++++++------
 tests/testthat/test-utils.R             |   20 +
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Package s3fs updated to version 0.1.5 with previous version 0.1.4 dated 2023-10-23

Title: 'Amazon Web Service S3' File System
Description: Access 'Amazon Web Service Simple Storage Service' ('S3') <https://aws.amazon.com/s3/> as if it were a file system. Interface based on the R package 'fs'.
Author: Dyfan Jones [aut, cre]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

Diff between s3fs versions 0.1.4 dated 2023-10-23 and 0.1.5 dated 2024-03-15

 DESCRIPTION            |    6 +--
 MD5                    |    8 ++--
 NEWS.md                |    6 +++
 R/s3filesystem_class.R |   30 ++++++++--------
 R/utils.R              |   88 +++++++++++++++++++++----------------------------
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New package rlandfire with initial version 1.0.0
Package: rlandfire
Title: Interface to 'LANDFIRE Product Service' API
Version: 1.0.0
Maintainer: Mark Buckner <mab677@cornell.edu>
Description: Provides access to a suite of geospatial data layers for wildfire management, fuel modeling, ecology, natural resource management, climate, conservation, etc., via the 'LANDFIRE' (<https://www.landfire.gov/index.php>) Product Service ('LFPS') API.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/bcknr/rlandfire
BugReports: https://github.com/bcknr/rlandfire/issues
Imports: httr, sf, utils
Suggests: knitr, raster, rmarkdown, stars, terra, testthat (>= 3.0.0)
VignetteBuilder: knitr
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-03-14 20:56:57 UTC; mark
Author: Mark Buckner [aut, cre, cph]
Repository: CRAN
Date/Publication: 2024-03-15 11:20:02 UTC

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New package pensynth with initial version 0.3.0
Package: pensynth
Title: Penalized Synthetic Control Estimation
Version: 0.3.0
Description: Estimate penalized synthetic control models and perform hold-out validation to determine their penalty parameter. This method is based on the work by Abadie & L'Hour (2021) <doi:10.1080/01621459.2021.1971535>. Penalized synthetic controls smoothly interpolate between one-to-one matching and the synthetic control method.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: geometry, clarabel
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2024-03-15 08:54:50 UTC; erikj
Author: Erik-Jan van Kesteren [cre, aut] , Isaac Slaughter [ctb]
Maintainer: Erik-Jan van Kesteren <e.vankesteren1@uu.nl>
Repository: CRAN
Date/Publication: 2024-03-15 11:50:05 UTC

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Package NeuroDecodeR updated to version 0.2.0 with previous version 0.1.0 dated 2022-09-29

Title: Decode Information from Neural Activity
Description: Neural decoding is method of analyzing neural data that uses a pattern classifiers to predict experimental conditions based on neural activity. 'NeuroDecodeR' is a system of objects that makes it easy to run neural decoding analyses. For more information on neural decoding see Meyers & Kreiman (2011) <doi:10.7551/mitpress/8404.003.0024>.
Author: Ethan Meyers [aut, cre]
Maintainer: Ethan Meyers <ethan.meyers@gmail.com>

Diff between NeuroDecodeR versions 0.1.0 dated 2022-09-29 and 0.2.0 dated 2024-03-15

 NeuroDecodeR-0.1.0/NeuroDecodeR/vignettes/ZD_400bins_400sampled_start100_end500.Rda                                     |only
 NeuroDecodeR-0.1.0/NeuroDecodeR/vignettes/position_invariance_results                                                   |only
 NeuroDecodeR-0.1.0/NeuroDecodeR/vignettes/results                                                                       |only
 NeuroDecodeR-0.2.0/NeuroDecodeR/DESCRIPTION                                                                             |   13 
 NeuroDecodeR-0.2.0/NeuroDecodeR/MD5                                                                                     |  276 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/NAMESPACE                                                                               |  138 
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/NeuroDecodeR.R                                                                        |  120 
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/cl_max_correlation.R                                                                  |  300 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/cl_poisson_naive_bayes.R                                                              |  374 +-
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/cl_svm.R                                                                              |  412 +-
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/create_binned_data.R                                                                  |  566 +--
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/cv_standard.R                                                                         | 1046 +++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/ds_basic.R                                                                            |  950 +++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/ds_generalization.R                                                                   |  578 +--
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/fp_select_k_features.R                                                                |  332 +-
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/fp_zscore.R                                                                           |  212 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/generic_functions.R                                                                   |  486 +--
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/get_num_label_repetitions.R                                                           | 1052 +++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/log_and_save_decoding_results.R                                                       |  733 ++--
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/ndr_container.R                                                                       |  276 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/private_helper_functions.R                                                            |  526 +--
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/raster_data_functions.R                                                               | 1305 ++++----
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/rm_confusion_matrix.R                                                                 | 1100 +++----
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/rm_main_results.R                                                                     | 1566 +++++-----
 NeuroDecodeR-0.2.0/NeuroDecodeR/R/test_valid_NDR_objects.R                                                              | 1006 +++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/README.md                                                                               |  254 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/build/vignette.rds                                                                      |binary
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/NDR_object_specification.R                                                     |  172 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/NDR_object_specification.Rmd                                                   |  836 ++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/NDR_object_specification.html                                                  | 1459 ++++-----
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/data_formats.R                                                                 |   56 
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/data_formats.Rmd                                                               |  350 +-
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/data_formats.html                                                              | 1091 +++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/datasets.R                                                                     |   28 
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/datasets.Rmd                                                                   |  288 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/datasets.html                                                                  |  917 ++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/generalization_tutorial.R                                                      |  360 +-
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/generalization_tutorial.Rmd                                                    |  716 ++--
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/generalization_tutorial.html                                                   | 1447 ++++-----
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/introduction_tutorial.R                                                        |  254 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/introduction_tutorial.Rmd                                                      |  992 +++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/inst/doc/introduction_tutorial.html                                                     | 1461 ++++-----
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 NeuroDecodeR-0.2.0/NeuroDecodeR/man/NeuroDecodeR.Rd                                                                     |   82 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/aggregate_CV_split_results.Rd                                                       |   74 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/aggregate_CV_split_results.rm_confusion_matrix.Rd                                   |   74 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/aggregate_CV_split_results.rm_main_results.Rd                                       |   74 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/aggregate_resample_run_results.Rd                                                   |   52 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/aggregate_resample_run_results.rm_confusion_matrix.Rd                               |   52 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/aggregate_resample_run_results.rm_main_results.Rd                                   |   52 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/cl_max_correlation.Rd                                                               |  156 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/cl_poisson_naive_bayes.Rd                                                           |  182 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/cl_svm.Rd                                                                           |  154 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/convert_matlab_raster_data.Rd                                                       |  160 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/create_binned_data.Rd                                                               |  164 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/cv_standard.Rd                                                                      |  246 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/ds_basic.Rd                                                                         |  232 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/ds_generalization.Rd                                                                |  236 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/figures/README-TCD_plot-1.png                                                       |binary
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/figures/README-line_plot-1.png                                                      |binary
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/figures/README-pipe_example-1.png                                                   |binary
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/fp_select_k_features.Rd                                                             |  124 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/fp_zscore.Rd                                                                        |  102 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_data.Rd                                                                         |   70 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_data.ds_basic.Rd                                                                |   70 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_data.ds_generalization.Rd                                                       |   70 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_num_label_repetitions.Rd                                                        |  112 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_num_label_repetitions_each_site.Rd                                              |   68 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.Rd                                                                   |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.cl_max_correlation.Rd                                                |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.cl_poisson_naive_bayes.Rd                                            |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.cl_svm.Rd                                                            |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.cv_standard.Rd                                                       |   62 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.ds_basic.Rd                                                          |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.ds_generalization.Rd                                                 |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.fp_select_k_features.Rd                                              |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.fp_zscore.Rd                                                         |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.rm_confusion_matrix.Rd                                               |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_parameters.rm_main_results.Rd                                                   |   64 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_predictions.Rd                                                                  |   94 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_predictions.cl_max_correlation.Rd                                               |   94 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_predictions.cl_poisson_naive_bayes.Rd                                           |   94 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_predictions.cl_svm.Rd                                                           |   94 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/get_siteIDs_with_k_label_repetitions.Rd                                             |   86 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/log_check_results_already_exist.Rd                                                  |   44 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/log_load_results_from_params.Rd                                                     |   48 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/log_load_results_from_result_name.Rd                                                |   54 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/log_save_results.Rd                                                                 |   66 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/plot.label_repetition.Rd                                                            |   56 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/plot.raster_data.Rd                                                                 |   54 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/plot.rm_confusion_matrix.Rd                                                         |  120 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/plot.rm_main_results.Rd                                                             |   91 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/plot_main_results.Rd                                                                |  126 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/preprocess_data.Rd                                                                  |   86 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/preprocess_data.fp_select_k_features.Rd                                             |   86 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/preprocess_data.fp_zscore.Rd                                                        |   86 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/read_matlab_raster_data.Rd                                                          |  138 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/read_raster_data.Rd                                                                 |   94 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/rm_confusion_matrix.Rd                                                              |  132 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/rm_main_results.Rd                                                                  |  134 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/run_decoding.Rd                                                                     |   60 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/run_decoding.cv_standard.Rd                                                         |   58 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/test_valid_binned_format.Rd                                                         |   48 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/test_valid_ndr_object.Rd                                                            |   50 
 NeuroDecodeR-0.2.0/NeuroDecodeR/man/test_valid_raster_format.Rd                                                         |   87 
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat.R                                                                        |    8 
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/example_DECODING_RESULTS.Rda                                             |binary
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/generate_data_to_test_NDR.R                                              |  370 +-
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/test-bin_convert_data.R                                                  |  622 +--
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/test-classifiers.R                                                       |  344 +-
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/test-datasources.R                                                       |  849 ++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/test-feature_preprocessors.R                                             |  290 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/test-full_decoding.R                                                     |  152 
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/test-log_save_results.R                                                  |  310 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/test-ndr_container.R                                                     |   74 
 NeuroDecodeR-0.2.0/NeuroDecodeR/tests/testthat/test-result_metrics.R                                                    |  240 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/vignettes/NDR_object_specification.Rmd                                                  |  836 ++---
 NeuroDecodeR-0.2.0/NeuroDecodeR/vignettes/data_formats.Rmd                                                              |  350 +-
 NeuroDecodeR-0.2.0/NeuroDecodeR/vignettes/datasets.Rmd                                                                  |  288 -
 NeuroDecodeR-0.2.0/NeuroDecodeR/vignettes/generalization_tutorial.Rmd                                                   |  716 ++--
 NeuroDecodeR-0.2.0/NeuroDecodeR/vignettes/introduction_tutorial.Rmd                                                     |  992 +++---
 122 files changed, 18938 insertions(+), 18723 deletions(-)

More information about NeuroDecodeR at CRAN
Permanent link

New package dySEM with initial version 1.0.0
Package: dySEM
Title: Dyadic Structural Equation Modeling
Version: 1.0.0
Description: Scripting of structural equation models via 'lavaan' for Dyadic Data Analysis, and helper functions for supplemental calculations, tabling, and model visualization. Current models supported include Dyadic Confirmatory Factor Analysis, the Actor–Partner Interdependence Model (observed and latent), the Common Fate Model (observed and latent), Mutual Influence Model (latent), and the Bifactor Dyadic Model (latent).
License: GPL-3
Encoding: UTF-8
URL: https://github.com/jsakaluk/dySEM, https://jsakaluk.github.io/dySEM/
BugReports: https://github.com/jsakaluk/dySEM/issues
Imports: dplyr, gt, lavaan, lifecycle, magrittr, rlang, semPlot, stringr, tibble
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
LazyData: true
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-03-14 17:49:18 UTC; johnsakaluk
Author: John Sakaluk [aut, cre, cph] , Omar Camanto [aut], Robyn Kilshaw [ctb], Alexandra Fisher [ctb]
Maintainer: John Sakaluk <jksakaluk@gmail.com>
Repository: CRAN
Date/Publication: 2024-03-15 11:10:02 UTC

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Package databraryr updated to version 0.6.2 with previous version 0.6.1 dated 2024-02-22

Title: Interact with the 'Databrary.org' API
Description: 'Databrary.org' is a restricted access repository for research data, especially video and audio. This package provides commands to interact with the data stored on 'Databrary.org'.
Author: Rick O. Gilmore [aut, cre, cph], Jeffrey Spies [aut], National Science Foundation OAC-2032713 [fnd], National Institutes of Health R01HD094830 [fnd]
Maintainer: Rick O. Gilmore <rog1@psu.edu>

Diff between databraryr versions 0.6.1 dated 2024-02-22 and 0.6.2 dated 2024-03-15

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New package palette with initial version 0.0.2
Package: palette
Title: Color Scheme Helpers
Version: 0.0.2
Description: Hexadecimal codes are typically used to represent colors in R. Connecting these codes to their colors requires practice or memorization. 'palette' provides a 'vctrs' class for working with color palettes, including printing and plotting functions. The goal of the class is to place visual representations of color palettes directly on or, at least, next to their corresponding character representations. Palette extensions also are provided for data frames using 'pillar'.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/christopherkenny/palette, https://christophertkenny.com/palette/
BugReports: https://github.com/christopherkenny/palette/issues
Imports: cli, pillar, vctrs
Suggests: ggplot2, spelling, testthat (>= 3.0.0), vdiffr
Language: en-US
Depends: R (>= 3.5)
NeedsCompilation: no
Packaged: 2024-03-14 17:07:36 UTC; chris
Author: Christopher T. Kenny [aut, cre]
Maintainer: Christopher T. Kenny <christopherkenny@fas.harvard.edu>
Repository: CRAN
Date/Publication: 2024-03-15 10:40:02 UTC

More information about palette at CRAN
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Package FinePop updated to version 1.5.2 with previous version 1.5.1 dated 2018-10-26

Title: Fine-Scale Population Analysis
Description: Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files.
Author: Reiichiro Nakamichi, Hirohisa Kishino, Shuichi Kitada
Maintainer: Reiichiro Nakamichi <nakamichi_reiichiro33@fra.go.jp>

Diff between FinePop versions 1.5.1 dated 2018-10-26 and 1.5.2 dated 2024-03-15

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Package epos updated to version 1.1 with previous version 1.0 dated 2021-02-19

Title: Epilepsy Ontologies' Similarities
Description: Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>, and Mueller, Bernd et al. (2022) <doi:10.1186/s13326-021-00258-w>.
Author: Bernd Mueller
Maintainer: Bernd Mueller <bernd.mueller@zbmed.de>

Diff between epos versions 1.0 dated 2021-02-19 and 1.1 dated 2024-03-15

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 epos-1.1/epos/DESCRIPTION                                          |   14 
 epos-1.1/epos/MD5                                                  |   70 ++--
 epos-1.1/epos/NAMESPACE                                            |  119 ++++----
 epos-1.1/epos/R/data.R                                             |   35 +-
 epos-1.1/epos/R/doThePlotDrugVectors.R                             |   86 ++---
 epos-1.1/epos/R/drawVennDiagramms.R                                |   60 ++--
 epos-1.1/epos/R/main.R                                             |    1 
 epos-1.1/epos/R/writeNeuroTable.R                                  |   51 ++-
 epos-1.1/epos/README.md                                            |   15 -
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 epos-1.1/epos/man/calcEnrichment.Rd                                |   40 +-
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Package QCA updated to version 3.22 with previous version 3.21 dated 2023-09-18

Title: Qualitative Comparative Analysis
Description: An extensive set of functions to perform Qualitative Comparative Analysis: crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA') and fuzzy sets ('fsQCA'), using a GUI - graphical user interface. 'QCA' is a methodology that bridges the qualitative and quantitative divide in social science research. It uses a Boolean minimization algorithm, resulting in a minimal causal configuration associated with a given phenomenon.
Author: Adrian Dusa [aut, cre, cph] , Ciprian Paduraru [ctb] , jQuery Foundation [cph] , jQuery contributors [ctb, cph] , Vasil Dinkov [ctb, cph] , Dmitry Baranovskiy [ctb, cph] , Emmanuel Quentin [ctb, cph] , Jimmy Breck-McKye [ctb, cph] , Alrik Thiem [aut] [...truncated...]
Maintainer: Adrian Dusa <dusa.adrian@unibuc.ro>

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Package feasts updated to version 0.3.2 with previous version 0.3.1 dated 2023-03-22

Title: Feature Extraction and Statistics for Time Series
Description: Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.
Author: Mitchell O'Hara-Wild [aut, cre], Rob Hyndman [aut], Earo Wang [aut], Di Cook [ctb], Thiyanga Talagala [ctb] , Leanne Chhay [ctb]
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Package WRS2 updated to version 1.1-6 with previous version 1.1-5 dated 2023-10-30

Title: A Collection of Robust Statistical Methods
Description: A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Author: Patrick Mair [cre, aut], Rand Wilcox [aut], Indrajeet Patil [ctb]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

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Package eRm updated to version 1.0-6 with previous version 1.0-5 dated 2024-03-01

Title: Extended Rasch Modeling
Description: Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
Author: Patrick Mair [cre, aut], Thomas Rusch [aut], Reinhold Hatzinger [aut], Marco J. Maier [aut], Rudolf Debelak [ctb]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

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Package cfa updated to version 0.10-1 with previous version 0.10-0 dated 2017-05-01

Title: Configural Frequency Analysis (CFA)
Description: Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.
Author: Patrick Mair [aut, cre], Stefan Funke [aut], Joachim Harloff [ctb], Alexander von Eye [ctb]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

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Package misha (with last version 4.2.9) was removed from CRAN

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

2024-01-15 4.2.9
2023-12-05 4.2.8

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Package vcfppR (with last version 0.4.0) was removed from CRAN

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

2024-02-26 0.4.0
2024-01-14 0.3.8
2023-12-14 0.3.5

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Package inferCSN updated to version 1.0.1 with previous version 1.0.0 dated 2024-02-09

Title: Inferring Cell-Specific Gene Regulatory Network
Description: A method for inferring cell-specific gene regulatory network from single-cell sequencing data.
Author: Meng Xu [aut, cre]
Maintainer: Meng Xu <mengxu98@qq.com>

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Package framecleaner updated to version 0.2.1 with previous version 0.2.0 dated 2021-11-17

Title: Clean Data Frames
Description: Provides a friendly interface for modifying data frames with a sequence of piped commands built upon the 'tidyverse' Wickham et al., (2019) <doi:10.21105/joss.01686> . The majority of commands wrap 'dplyr' mutate statements in a convenient way to concisely solve common issues that arise when tidying small to medium data sets. Includes smart defaults and allows flexible selection of columns via 'tidyselect'.
Author: Harrison Tietze [aut, cre]
Maintainer: Harrison Tietze <Harrison4192@gmail.com>

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