fusionImage: An R package for pan‐sharpening images in open source software

Aim: Large-scale datasets are becoming increasingly available for macroecological research from different disciplines. However, learning their specific extraction and analytical requirements can become prohibitively time-consuming for researchers. We argue that this issue can be tackled with the provision of methodological frameworks published in open-source software. We illustrate this solution with the invacost R package, an open-source software designed to query and analyse the global database on reported economic costs of invasive alien species, InvaCost. Innovations: First, the invacost package provides updates of this dynamic database directly in the analytical environment R. Second, it helps understand the nature of economic cost data for invasive species, their harmonisation process, and the inherent biases associated with such data. Third, it readily provides complementary methods to query and analyse the costs of invasive species at the global scale, all the while accounting for econometric statistical issues. Main conclusions: This tool will be useful for scientists working on invasive alien species, by (i) facilitating access and use to this multi-disciplinary data resource and (ii) providing a standard procedure which will facilitate reproducibility and comparability of studies, one of the major critics of this topic until now. We discuss how the development of this R package was designed as an enforcement of general recommendations for transparency, reproducibility and comparability of science in the era of big data in ecology.

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ggVennDiagram: An Intuitive, Easy-to-Use, and Highly Customizable R Package to Generate Venn Diagram

Frontiers in Genetics ◽

10.3389/fgene.2021.706907 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chun-Hui Gao ◽

Guangchuang Yu ◽

Peng Cai

Keyword(s):

Open Source ◽

Open Source Software ◽

Source Code ◽

R Package ◽

Venn Diagram ◽

Free Access ◽

High Quality ◽

Venn Diagrams ◽

Label Edge ◽

The Cost

Venn diagrams are widely used diagrams to show the set relationships in biomedical studies. In this study, we developed ggVennDiagram, an R package that could automatically generate high-quality Venn diagrams with two to seven sets. The ggVennDiagram is built based on ggplot2, and it integrates the advantages of existing packages, such as venn, RVenn, VennDiagram, and sf. Satisfactory results can be obtained with minimal configurations. Furthermore, we designed comprehensive objects to store the entire data of the Venn diagram, which allowed free access to both intersection values and Venn plot sub-elements, such as set label/edge and region label/filling. Therefore, high customization of every Venn plot sub-element can be fulfilled without increasing the cost of learning when the user is familiar with ggplot2 methods. To date, ggVennDiagram has been cited in more than 10 publications, and its source code repository has been starred by more than 140 GitHub users, suggesting a great potential in applications. The package is an open-source software released under the GPL-3 license, and it is freely available through CRAN (https://cran.r-project.org/package=ggVennDiagram).

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Introducing selfisher: open source software for statistical analyses of fishing gear selectivity

10.1101/2020.12.11.421362 ◽

2020 ◽

Author(s):

Mollie E Brooks ◽

Valentina Melli ◽

Esther Savina ◽

Juan Santos ◽

Russell Millar ◽

...

Keyword(s):

Open Source ◽

Statistical Methods ◽

Open Source Software ◽

Fixed Effects ◽

Mesh Size ◽

R Package ◽

Fishing Gear ◽

Comparison Study ◽

Gear Selectivity ◽

Fishing Gear Selectivity

Fishing gear is constantly being improved to select certain sizes and species while excluding others. Experiments are conducted to quantify the selectivity and the resulting data needs to be analyzed using specialized statistical methods in many cases. Here, we present a new estimation tool for analyzing this type of data: an R package named selfisher. It can be used for both active and passive gears, and can handle different trial designs. It allows fitting models containing multiple fixed effects (e.g. length, total catch weight, mesh size, water turbidity) and random effects (e.g. haul). A bootstrapping procedure is provided to account for between and within haul variability and overdispersion. We demonstrate its use via four case studies including (1) covered codend analyses of four gears, (2) a paired gear study with numerous potential covariates, (3) a catch comparison study of unpaired hauls of gillnets and (4) a catch comparison study of paired hauls using polynomials and splines. This free and open source software will make it easier to model fishing gear selectivity, teach the statistical methods, and make analyses more repeatable.

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modglm: An R package for testing, interpreting, and displaying interactions in generalized linear models of discrete data

10.31234/osf.io/6vmsa ◽

2021 ◽

Author(s):

Connor McCabe ◽

Max Andrew Halvorson ◽

Kevin Michael King ◽

Xiaolin Cao ◽

Dale Sim Kim

Keyword(s):

Open Source ◽

Generalized Linear Models ◽

Open Source Software ◽

Interaction Effect ◽

Software Package ◽

Linear Models ◽

R Package ◽

Interaction Effects ◽

Research Practice ◽

Nonlinear Scale

Many researchers hope to examine interaction effects using generalized linear models (GLMs) to predict outcomes on nonlinear scales. For instance, logistic and Poisson GLMs are used to estimate associations between predictors and outcomes in nonlinear probability and count scales, respectively. However, we (McCabe et al., 2021; Halvorson et al., in press) and others (Ai & Norton, 2003; Mize, 2019; Norton, Wang, & Ai, 2004) have shown that testing and interpreting interaction effects on these scales is not straightforward. GLMs require the application of partial derivatives and/or discrete differences to compute and probe interaction effects appropriately when models are interpreted on their nonlinear scale. Currently available open-source software does not provide methods of computing these interaction effects on probability and count scales, reflecting a central limitation in applying these methods in research practice. Here, we introduce `modglm`, an R-based software package that accompanies our manuscript providing recommendations for computing interaction effects in nonlinear probability and counts (McCabe et al., 2021). This software produces the interaction effect between two variables in generalized linear models of probabilities and counts and provides additional statistics and plotting utilities for evaluating and describing this effect.

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Solving boundary value problems in the open source software R: package bvpSolve

Opuscula Mathematica ◽

10.7494/opmath.2014.34.2.387 ◽

2014 ◽

Vol 34 (2) ◽

pp. 387 ◽

Cited By ~ 12

Author(s):

Francesca Mazzia ◽

Jeff R. Cash ◽

Karline Soetaert

Keyword(s):

Boundary Value Problems ◽

Open Source ◽

Open Source Software ◽

Boundary Value ◽

R Package

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AlphaPart - R implementation of the method for partitioning genetic trends

10.1101/2020.04.24.059071 ◽

2020 ◽

Author(s):

Jana Obšteter ◽

Justin Holl ◽

John M. Hickey ◽

Gregor Gorjanc

Keyword(s):

Gene Flow ◽

Open Source ◽

Open Source Software ◽

Genetic Gain ◽

R Package ◽

Genetic Trend ◽

Breeding Values ◽

Breeding Programmes ◽

Estimated Breeding Values ◽

Genetic Trends

AbstractBackgroundIn this paper we present the AlphaPart R package, an open-source software that implements a method for partitioning breeding values and genetic trends to identify sources of genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from averaged year of birth estimated breeding values of selection candidates. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated pig breeding example.ResultsThe package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by analyst defined paths, and a set of functions for handling data and results. The package is freely available from CRAN repository at http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by examining the genetic gain in a pig breeding example, in which the multiplier achieved higher breeding values than the nucleus for traits measured and selected in the multiplier. The partitioning analysis revealed that these higher values depended on the accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. For traits measured only in the nucleus, the multiplier achieved comparable or smaller genetic gain than the nucleus depending on the amount of gene flow.ConclusionsAlphaPart implements a method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to better understand or improve their breeding programmes.

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