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149 results for “r packages”
FIGURE 4 in GroupStruct: An R Package for Allometric Size Correction
FIGURE 4. Results of the ANOVA and posthoc Tukey test comparing the means of each trait across all pairwise species comparisons for Dataset 1. Green boxes represent p ≤ 0.05 (reject the null of equal means), while red boxes represent p ≥ 0.05 (failure to reject the null of equal means). All p-values have been adjusted to correct for multiple comparisons using the singlestep method.
FIGURE 2. Scatter plots for Dataset 2 in GroupStruct: An R Package for Allometric Size Correction
FIGURE 2. Scatter plots for Dataset 2 (Amolops) showing the regression slopes of each trait variate plotted against SVL. Data were log-transformed but not size-adjusted. The blue line represents the best-fit regression line. Within-species slopes are presented in Table 1. Points represent individual measurements.
FIGURE 1. Scatter plots for Dataset 1 in GroupStruct: An R Package for Allometric Size Correction
FIGURE 1. Scatter plots for Dataset 1 (Cyrtodactylus) showing the regression slopes of each trait variate plotted against SVL. Data were log-transformed but not size-adjusted. The blue line represents the best-fit regression line. Within-species slopes are presented in Table 1. Points represent individual measurements.
FIGURE 3 in GroupStruct: An R Package for Allometric Size Correction
FIGURE 3. PCA plots of the first two principal components for Dataset 1 and 2. Ellipses around points represent a 95% confidence interval. Points represent PCA scores calculated from individual measurements.
FIGURE 5 in GroupStruct: An R Package for Allometric Size Correction
FIGURE 5. Results of the ANOVA and posthoc Tukey test comparing the means of each trait across all pairwise species comparisons for Dataset 2. Green boxes represent p ≤ 0.05 (reject the null of equal means), while red boxes represent p ≥ 0.05 (failure to reject the null of equal means). All p-values have been adjusted to correct for multiple comparisons using the singlestep method.
Rendered HOTSSea model output for the pacea R package
<p>This files are for the pacea R package https://github.com/pbs-assess/pacea and are automatically downloaded to your local machine when running the function pacea::hotssea_all_variables() . See the hotssea vignette for details. </p> <p>The Hindcast of the Salish Sea (HOTSSea) is a physical ocean model that recreates conditions throughout the Salish Sea from 1980 to 2018, filling in the gaps in patchy measurements. See pacea for reference. </p>
R Scripts - Replication Package
<p><strong>R Scripts - Replication Package:</strong> Contains R scripts for the complete quantitative analysis:</p> <ul> <li>One script for the preliminary question</li> <li>Three scripts, one for each research question</li> <li>One script for the classification of reactors based on their number of commits.</li> </ul>
Example data for: FIESTA: A Forest Inventory Estimation and Analysis R package
<p class="MsoNormal">This dataset is for examples in the Ecography Software Note, FIESTA: A Forest Inventory Estimation and Analysis R package, by Frescino, Tracey S.; Moisen, Gretchen G.; Patterson, Paul, L.; Toney, Chris; White, Grayson W. The examples demonstrate how to generate estimates of forest attributes using three different <em>FIESTA</em> modules: Green Book (GB), Model-Assisted (MA), and Small Area (SA). Included in the dataset are: a geospatial vector shapefile (.shp) of the Middle Bear-Logan Watershed area of interest (AOI); an R sf object (.rds) defining an ecological extent encompassing the AOI, Ecomap Section M331D (Cleland et al. 2007) ; a SQLite database (.db) including FIA plot data downloaded from FIA's publicly available DataMart (<a href="https://apps.fs.usda.gov/fia/datamart/datamart.html">https://apps.fs.usda.gov/fia/datamart/datamart.html</a>) and subset to the M331D boundary; and five auxiliary spatially-explicit raster layers (.img) clipped to the M331D boundary.</p>
TA B L E 2 Identified R packages useful for taxonomic name harmonization. Square brackets indicate supplementary references in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices
TA B L E 2 Identified R packages useful for taxonomic name harmonization. Square brackets indicate supplementary references
R notebooks to reproduce all analyses from the manuscript "grandR: a comprehensive package for nucleotide conversion sequencing data analysis"
<p>This package contains all R notebooks to reproduce the analyses from our manuscript "grandR: a comprehensive package for nucleotide conversion sequencing data analysis".</p> <p>In the zip file you find</p> <ul> <li>several rds files in the data folder: They contain grandR objects of both simulated and real SLAM-seq data sets. You can delete them and create them again by either just "knitting" the notebooks (which will generate all data necessary for this notebook and save it into the data folder), or by executing the generateAllDataFiles.R script ("Rscript generateAllDataFiles.R"), which will generate all rds files that do not exist).</li> <li>several R notebooks (Rmd): "Knitting" them will generate all figures from the manuscript. Without the data files (rds), this will be slow!</li> <li>knit_all.bash: Execute to "knit" all notebooks</li> <li>clean.bash: Clear the output of "knitting" the notebooks</li> </ul> <p> </p>
Data from: samc: An R package for connectivity modeling with spatial absorbing Markov chains
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Example data for: FIESTA: A Forest Inventory Estimation and Analysis R package
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Data from: An R package for analyzing survival using continuous-time open capture-recapture models
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Data from: PolyPatEx: an R package for paternity exclusion in autopolyploids
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Data from: Multi-DICE: R package for comparative population genomic inference under hierarchical co-demographic models of independent single-population size changes
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Data from: SIDER: an R package for predicting trophic discrimination factors of consumers based on their ecology and phylogenetic relatedness
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phyloregion: R package for biogeographic regionalization and spatial conservation
<ol> <li>Biogeographical regionalization is the classification of regions in terms of their biotas and is key to understanding biodiversity patterns across the world. Previously, it was only possible to perform analysis of biogeographic regionalization on small datasets, often using tools that are difficult to replicate.</li> <li>Here, we present phyloregion, a package for the analysis of biogeographic regionalization and spatial conservation in the R computing environment, tailored for mega phylogenies and macroecological datasets of ever-increasing size and complexity.</li> <li>Compared to available packages, phyloregion is three to four orders of magnitude faster and memory efficient for cluster analysis, determining optimal number of clusters, evolutionary distinctiveness of regions, as well as analysis of more standard conservation measures of phylogenetic diversity, phylogenetic endemism, and evolutionary distinctiveness and global endangerment.</li> <li>A case study for zoogeographic regionalization of 9574 species of squamate reptiles (amphisbaenians, lizards, and snakes) across the globe, reveals their evolutionary affinities using visualization tools that allow rapid identification of patterns and underlying processes with user-friendly colours–for example–indicating the levels of differentiation of the taxa in different regions.</li> <li>Ultimately, phyloregion would facilitate rapid biogeographic analyses that accommodates the ongoing mass-production of species occurrence records and phylogenetic datasets at any scale and for any taxonomic group into completely reproducible R workflows.</li> </ol>
Data from: RClone: a package to identify MultiLocus Clonal Lineages and handle clonal datasets in R
Partially clonal species are common in the Tree of Life. And yet, population genetics models still mostly focus on the extremes: strictly sexual versus purely asexual reproduction. Here we present an R package built upon GenClone software including new functions and several improvements. The RClone package includes functions to handle clonal datasets, allowing (i) checking for dataset reliability to discriminate multi-locus genotypes (MLG), (ii) ascertainment of MLG and semi-automatic determination of clonal lineages (MLL), (iii) genotypic richness and evenness indices calculation based on MLGs or MLLs, and (iv) describing several spatial components of clonality. RClone allows the one shot analysis of multi-population datasets without size limitation, suitable for datasets now increasingly produced through Next Generation Sequencing. A major improvement compared to existing software is the ability to determine the threshold to cluster similar MLGs into MLLs, based on implemented simulations of sexual events. Several functions allow data importation, conversion and exportation with adegenet, Genetix or Arlequin. RClone is provided with two vignettes to handle analysis on one (RClone_quickmanual) or several populations (RClone_qmsevpops).
Data from: pavo: an R package for the analysis, visualization and organization of spectral data
1. Recent technical and methodological advances have led to a dramatic increase in the use of spectrometry to quantify reflectance properties of biological materials, as well as models to determine how these colours are perceived by animals, providing important insights into ecological and evolutionary aspects of animal visual communication. 2. Despite this growing interest, a unified cross-platform framework for analyzing and visualizing spectral data has not been available. We introduce pavo, an R package that facilitates the organization, visualization, and analysis of spectral data in a cohesive framework. pavo is highly flexible, allowing users to (a) organize and manipulate data from a variety of sources, (b) visualize data using R's state-of-the-art graphics capabilities, and (c) analyze data using spectral curve shape properties and visual system modeling for a broad range of taxa. 3. In this paper, we present a summary of the functions implemented in pavo and how they integrate in a workflow to explore and analyze spectral data. We also present an exact solution for the calculation of colour volume overlap in colourspace, thus expanding previously published methodologies. 4. As an example of pavo's capabilities, we compare the colour patterns of three African Glossy Starling species, two of which have diverged very recently. We demonstrate how both colour vision models and direct spectral measurement analysis can be used to describe colour attributes and differences between these species. Different approaches to visual models and several plotting capabilities exemplify the package's versatility and streamlined workflow. 5. pavo provides a cohesive environment for handling spectral data and addressing complex sensory ecology questions, while integrating with R's modular core for a broader and comprehensive analytical framework, automated management of spectral data, and reproducible workflows for colour analysis.
Data from: ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data
We present an r package, ggtree, which provides programmable visualization and annotation of phylogenetic trees. ggtree can read more tree file formats than other softwares, including newick, nexus, NHX, phylip and jplace formats, and support visualization of phylo, multiphylo, phylo4, phylo4d, obkdata and phyloseq tree objects defined in other r packages. It can also extract the tree/branch/node-specific and other data from the analysis outputs of beast, epa, hyphy, paml, phylodog, pplacer, r8s, raxml and revbayes software, and allows using these data to annotate the tree. The package allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion. A two-dimensional tree can be drawn by scaling the tree width based on an attribute of the nodes. A tree can be annotated with an associated numerical matrix (as a heat map), multiple sequence alignment, subplots or silhouette images. The package ggtree is released under the artistic-2.0 license. The source code and documents are freely available through bioconductor (http://www.bioconductor.org/packages/ggtree).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.