We recommend the finest software for performing statistical analysis. Open source goodness at its best.
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We recommend the finest software for performing statistical analysis. Open source goodness at its best.
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Great libraries and tools that are ideal for mathematicians working with Python.
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PyMC supports Bayesian statistical modelling with MCMC sampling, variational inference, posterior diagnostics and predictive simulation.
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git-heatmap creates a customisable visual calendar of local Git commit activity, helping developers inspect how activity varies over time.
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git-who analyses repository authorship across branches, directories, and revisions, with contribution rankings, history views, and filters.
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Onefetch displays Git repository details, code statistics, contributor information, and language breakdowns in a configurable terminal view.
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gdcalc combines financial, scientific, statistical and programmer calculations with algebraic and RPN entry and unit conversion.
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hercules analyses complete Git repository history, producing ownership, churn, burndown, contributor, and structural activity datasets.
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git-quick-stats reports repository contributions, commits, branches, reviewers, and activity trends through an interactive terminal menu.
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git-of-theseus plots how code survives and changes over time, using historical cohorts and contributor data from Git repository history.
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git-fame ranks Git repository contributors using lines and commit history, with path, date, file-extension, and whitespace filters.
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git-sizer measures Git object sizes, reference counts, directory structure, and repository characteristics that can affect performance.
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git-stats displays Git contribution calendars and activity statistics, including imported commits, author filters, and changes over time.
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plotnine builds layered statistical graphics using a grammar of graphics, with faceting, statistical transforms, scales and themes.
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Vega-Altair defines interactive statistical charts declaratively with Vega-Lite, composable views, transformations and selections.
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seaborn provides statistical visualisation for distributions, regression, categorical comparisons and faceted plots with matplotlib.
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gbutils is a command-line statistics and econometrics toolkit with data manipulation, regression, non-parametric tests and plotting.
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statsmodels provides statistical models, regression, time-series forecasting, hypothesis testing, econometrics and model diagnostics.
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patsy converts statistical formulas into design matrices with categorical contrasts, spline bases and reusable data transformations.
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jamovi provides spreadsheet-based statistical analysis, R syntax output, data transformations and additional methods through its modules.
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