Search Results for "quantitative"
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Showing 15 open source projects for "quantitative"
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Croizat
A software package for quantitative analysis in Panbiogeography
Croizat is a free, user-friendly, cross-platform desktop software package which biologists can use to integrate and analyze spatial data on species or other taxa and to explore geographical patterns in diversity under a panbiogeographic and graph-theoretic approach. -
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Libro
An interactive program for statistical analysis of texts
A cross-platform text analysis program written in Python and Free Pascal/Lazarus which scans a whole text file (in plain text, HTML, EPUB, or ODT formats) and ranks all used words according to frequency, performing a quantitative analysis of the text using Shannon-Weaver information statistic and Zipf power law function. It counts words, sentences, chars, spaces, and syllables. Also computes readability indexes (Gunning-Fog, Coleman-Liau, Automated Readability Index (ARI), SMOG grade, Flesch–Kincaid grade level and Flesch Reading Ease). -
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DataPrep
Python-based data preprocessing tool
DataPrep v0.2 is a Tkinter-based GUI application/tool designed to assist users in data preprocessing, multicollinearity removal, and feature selection for a wide range of applications in Cheminformatics, Bioinformatics, Data Analysis, Feature Selection, Molecular Modeling, Machine Learning, and Quantitative-structure-property relationship (QSPR) studies. It includes functionality to load, process, and save datasets with support for different preprocessing & multicollinearity removal strategies with customizable parameter setting options. -
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Discursive Dilemma
Optimal Decision Rules for the Discursive Dilemma
Accompanying software for the paper "Optimal Decision Rules for the Discursive Dilemma", by A. Alabert, M. Farré and R. Montes (https://arxiv.org/abs/2210.13100). Computes a ranking of the best decision rules according to a quantitative criterion based on the minimisation of the combined probabilities of false positives and false negatives -
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Python4Proteomics Course
Python course for Proteomics analysis
Python course (in Spanish) for Proteomics analysis using basically Jupyter NoteBooks. For more information, you can have a look at the readme.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/p4p/code/ci/default/tree/readme.md -
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DEBay
Deconvolutes qPCR data to estimate cell-type-specific gene expression
...The user manual of DEBay: https://sourceforge.net/projects/debay/files/UserManual.pdf Sample data: https://sourceforge.net/projects/debay/files/Test_data/ Citation Information: Vimalathithan Devaraj, Biplab Bose. DEBay: A computational tool for deconvolution of quantitative PCR data for estimation of cell type-specific gene expression in a mixed population. Heliyon, 2020, 6(7), e04489. https://doi.org/10.1016/j.heliyon.2020.e04489 -
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A quantitative finance C++ library for modeling, pricing, trading, and risk management in real-life. A cross-platform free/open-source tool for derivatives and financial engineering.
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AstroTortilla
Astrophotography assistant
AstroTortilla is a wrapper around common astrophotography tools intended to automate basic tasks such as polar alignment and goto correction. Not in active development presently. -
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forest01
Automatic delineation of forest borderlines
Forest01 is a tool for the automatic mapping of forest based on Airborne Laser Scanner data. The forest mapping is performed by means of quantitative criteria typical of forest definitions, namely minimum threshold for the height of trees, canopy cover, forest area size and width. -
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pyQPCR is a GUI application written in python that deals with quantitative PCR (QPCR) raw data. Using quantification cycle values extracted from QPCR instruments, it uses a proven and universally applicable model to give finalized quantification resu
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GASiC
Genome Abundance Similarity Correction
One goal of sequencing based metagenomic analysis is the quantitative taxonomic assessment of microbial community compositions. However, the majority of approaches either quantify at low resolution (e.g. at phylum level) or have severe problems discerning highly similar species. Yet, accurate quantification on species level is desirable in applications such as metagenomic diagnostics or community comparison. -
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fitGCP
Fitting genome coverage distributions with mixture models
...Thus, biases such as fragmented or erroneous reference genomes often remain unaccounted for. Making this information accessible can improve the quality of sequencing experiments and quantitative analyses. fitGCP is a framework for fitting mixtures of probability distributions to genome coverage profiles. Besides commonly used distributions, fitGCP uses distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm. Please find the accompanying paper here: http://dx.doi.org/10.1093/bioinformatics/btt147 -
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FABS
Fluorescence and absorption spectroscopy made easy
...FABS was designed with students in mind, as a way to help high schoolers and undergraduates learn about spectroscopy without paying for expensive equipment and software. Accompanying this software is a hardware design, which can be used as a bare-bones spectrometer that will provide quantitative absorption and fluorescence emission spectra at an extremely low cost. It uses only materials that many people can find around their home. -
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colonyzer image analysis software
Image analysis estimating cell density in arrayed microbial cultures
Image analysis software for quantification of cell density in microbial cultures (e.g. S. cerevisiae) growing on solid agar plates. Specialises in detection of extremely low cell densities. Forms part of the Quantitative Fitness Analysis (QFA) workflow: http://research.ncl.ac.uk/qfa/ Suitable for high-throughput, genome-wide analysis of culture libraries when combined with the following qfa R package: http://qfa.r-forge.r-project.org/ This is the version presented, used and demonstrated in the following manuscripts: Lawless et al. 2010 http://dx.doi.org/10.1186/1471-2105-11-287 Addinall et al. 2011 http://dx.doi.org/10.1371/journal.pgen.1001362 Chang et al. 2011 http://dx.doi.org/10.1534/g3.111.000216 Banks et al. 2012 http://dx.doi.org/10.3791/4018 Development has been moved to github, where Colonyzer has undergone several recent improvements, particuarly making installation easier and analysis faster: https://github.com/CnrLwlss/Colonyzer -
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