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

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.

scholarly journals Integration of enabling methods for the automated flow preparation of piperazine-2-carboxamide

2014 ◽  
Vol 10 ◽  
pp. 641-652 ◽  
Author(s):  
Richard J Ingham ◽  
Claudio Battilocchio ◽  
Joel M Hawkins ◽  
Steven V Ley
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Package ◽  
Flow Chemistry ◽  
Raspberry Pi ◽  
Step Flow ◽  
Simultaneous Control ◽  
Open Source Software Package

Here we describe the use of a new open-source software package and a Raspberry Pi® computer for the simultaneous control of multiple flow chemistry devices and its application to a machine-assisted, multi-step flow preparation of pyrazine-2-carboxamide – a component of Rifater®, used in the treatment of tuberculosis – and its reduced derivative piperazine-2-carboxamide.

scholarly journals An open source software package for primality testing of numbers of the form p2^n+1, with no constraints on the relative sizes of p and 2^n

2018 ◽  
Author(s):  
Tejas R. Rao
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Package ◽  
Primality Testing ◽  
Fermat Number ◽  
Open Source Software Package ◽  
Sierpinski Numbers ◽  
Efficient Software

We develop an efficient software package to test for the primality of p2^n+1, p prime and p>2^n. This aids in the determination of large, non-Sierpinski numbers p, for prime p, and in cryptography. It furthermore uniquely allows for the computation of the smallest n such that p2^n+1 is prime when p is large. We compute primes of this form for the first one million primes p and find four primes of the form above 1000 digits. The software may also be used to test whether p2^n+1 divides a generalized fermat number base 3.

scholarly journals Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Hua Dean Fang ◽  
Chien-Yu Lin ◽  
Meng-Jung Shih ◽  
Hung-Ming Wang ◽  
Tsung-Ying Ho ◽  
...  
Keyword(s):  
Open Source ◽  
Texture Analysis ◽  
Open Source Software ◽  
Software Package ◽  
Tumor Heterogeneity ◽  
Locally Advanced ◽  
Image Texture ◽  
Pixel Intensity ◽  
Open Source Software Package ◽  
Molecular Images

Background. The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project.Methods. With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies.Results. In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outperformed SUVmeanfor outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmeanand TLG (0.6 and 0.52, resp.).Conclusions. CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use athttp://code.google.com/p/cgita.

scholarly journals Electron Microscopy (Big and Small) Data Analysis With the Open Source Software Package HyperSpy

2017 ◽  
Vol 23 (S1) ◽  
pp. 214-215 ◽  
Author(s):  
Francisco de la Pena ◽  
Tomas Ostasevicius ◽  
Vidar Tonaas Fauske ◽  
Pierre Burdet ◽  
Petras Jokubauskas ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Software Package ◽  
Small Data ◽  
Open Source Software Package

An enhanced version of SMMP—open-source software package for simulation of proteins

2006 ◽  
Vol 174 (5) ◽  
pp. 422-429 ◽  
Author(s):  
Frank Eisenmenger ◽  
Ulrich H.E. Hansmann ◽  
Shura Hayryan ◽  
Chin-Kun Hu
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Package ◽  
Open Source Software Package

scholarly journals Analysing global economic costs of invasive alien species with the invacost R package

2020 ◽  
Author(s):  
Boris Leroy ◽  
Andrew M Kramer ◽  
Anne-Charlotte Vaissière ◽  
Franck Courchamp ◽  
Christophe Diagne
Keyword(s):  
Invasive Species ◽  
Alien Species ◽  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Invasive Alien Species ◽  
Standard Procedure ◽  
R Package ◽  
Economic Costs ◽  
Global Database

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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