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jamovi is an open source graphical user interface for the R programming language. [3] It is used in statistical research, especially as a tool for ANOVA (analysis of variance) and to understand statistical inference. [4] [5] It also can be used for linear regression, [6] mixed models and Bayesian models. [7]
graph-tool is a Python module for manipulation and statistical analysis of graphs (AKA networks).The core data structures and algorithms of graph-tool are implemented in C++, making extensive use of metaprogramming, based heavily on the Boost Graph Library. [1]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Unless automated tools make these corrections, beamline staff can be overwhelmed by data that can be collected far faster than corrections and reconstruction can be performed. [ 3 ] To address the needs for image correction and tomographic reconstruction in an instrument independent manner, the TomoPy code was developed, which is a ...
The Biopython project is an open-source collection of non-commercial Python tools for computational biology and bioinformatics, created by an international association of developers. [ 1 ] [ 4 ] [ 5 ] It contains classes to represent biological sequences and sequence annotations , and it is able to read and write to a variety of file formats.
ParaView is an open-source, multi-platform data analysis and visualization application. ParaView is known and used in many different communities to analyze and visualize scientific data sets. [ 2 ] It can be used to build visualizations to analyze data using qualitative and quantitative techniques.
They implemented and open-sourced the next version of Gradient Boosting library called CatBoost, which has support of categorical and text data, GPU training, model analysis, visualisation tools. CatBoost was open-sourced in July 2017 and is under active development in Yandex and the open-source community.