Search Results for "statistical"
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Showing 99 open source projects for "statistical"
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1
ggstatsplot
Enhancing {ggplot2} plots with statistical analysis
...The central idea of {ggstatsplot} is simple: combine these two phases into one in the form of graphics with statistical details, which makes data exploration simpler and faster. Summary of statistical tests and effect sizes. -
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Mixed-effects models in Julia
A Julia package for fitting (statistical) mixed-effects models
This package defines linear mixed models (LinearMixedModel) and generalized linear mixed models (GeneralizedLinearMixedModel). Users can use the abstraction for statistical model API to build, fit (fit/fit!), and query the fitted models. A mixed-effects model is a statistical model for a response variable as a function of one or more covariates. For a categorical covariate the coefficients associated with the levels of the covariate are sometimes called effects, as in "the effect of using Treatment 1 versus the placebo". ... -
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RCall.jl
Call R from Julia
R is a language for statistical computing and graphics that has been around for a couple of decades and it has one of the most impressive collections of scientific and statistical packages of any environment. Recently, the Julia language has become an attractive alternative because it provides the remarkable performance of a low-level language without sacrificing the readability and ease of use of high-level languages. -
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G2
Interactive data-driven visualization grammar for statistical charts
G2 is a highly interactive data-driven visualization grammar for statistical charts. with a high level of usability and scalability. It provides a set of grammar, and takes users beyond a limited set of charts to an almost unlimited world of graphical forms. With G2, you can describe the visual appearance and interactive behavior of visualization just by one statement, and generate web-based views using Canvas or SVG. -
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plotly.js
JavaScript charting library behind Plotly and Dash
Plotly JavaScript Open Source Graphing Library. Built on top of d3.js and stack.gl, Plotly.js is a high-level, declarative charting library. plotly.js ships with over 40 chart types, including 3D charts, statistical graphs, and SVG maps. plotly.js is free and open source and you can view the source, report issues or contribute on GitHub. For plotly.js to build with Webpack you will need to install ify-loader@v1.1.0+ and add it to your webpack.config.json. This adds Browserify transform compatibility to Webpack which is necessary for some plotly.js dependencies. ... -
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collapse
Advanced and Fast Data Transformation in R
collapse is a high-performance R package designed for fast and efficient data transformation, aggregation, reshaping, and statistical computation. Built to offer a more performant alternative to dplyr and data.table, it is particularly well-suited for large datasets and econometric applications. It operates on base R data structures like data frames and vectors and uses highly optimized C++ code under the hood to deliver significant speed improvements. collapse also includes tools for grouped operations, weighted statistics, and time series manipulation, making it a compact yet powerful utility for data scientists and researchers working in R. -
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Compose.jl
Declarative vector graphics
Compose is a vector graphics library for Julia. It forms the basis for the statistical graphics system Gadfly. Compose is a declarative vector graphics system written in Julia. It's designed to simplify the creation of complex graphics and serves as the basis of the Gadfly data visualization package. -
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F2
An elegant, interactive and flexible charting library for mobile
F2 is an out-of-the-box visualization engine focused on the mobile terminal, oriented to conventional statistical charts, perfectly supporting the H5 environment and compatible with multiple environments (Node, applet), complete graphics grammar theory, to meet your various visualization needs , professional mobile design guidelines to bring you the best mobile graphics experience. Best practices for moving side charts around design, performance and heterogeneous environments. ... -
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Orange Data Mining
Orange: Interactive data analysis
Open source machine learning and data visualization. Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. ... -
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gtsummary
Presentation-Ready Data Summary and Analytic Result Tables
gtsummary is an R package for creating elegant, customizable, publication-ready summary tables of datasets and statistical models. It provides concise code to produce demographic tables (tbl_summary()), regression result tables, and more, with flexible styling options for reporting. -
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Beads Viewer
Task management system for coding agents
Beads Viewer is a visualization tool designed to display, inspect, and interact with “bead plots,” graphical representations often used in statistical analysis, time series exploration, or clustering visualizations where data points are represented as beads on strands or axes to emphasize patterns and relationships. The viewer provides an intuitive graphical interface that automatically renders bead plots from structured data inputs, enabling users to observe trends at a glance, highlight clusters, or drill down into individual sequences. ... -
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ydata-profiling
Create HTML profiling reports from pandas DataFrame objects
ydata-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json. -
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FinMind
Open Data, more than 50 financial data
...Regardless of the program, you can download data through the api provided by FinMind, or you can download data directly from the website. After data is available, statistical analysis, regression analysis, time series analysis, machine learning, and deep learning can be performed. For individual stocks, provide visual analysis of technical, fundamental, and chip levels. According to different strategies, back-test analysis is performed to provide performance, profit and loss, and stock selection targets of different strategy investment portfolios. -
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Bayesian Statistics
This repository holds slides and code for a full Bayesian statistics
This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from classical inferential statistics that prohibits probability statements about parameters and is based on asymptotically sampling infinite samples from a theoretical population and finding parameter values that maximize the likelihood function. ... -
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ggpubr
'ggplot2' Based Publication Ready Plots
ggpubr is an R package that provides easy-to-use wrapper functions around ggplot2 to create publication-ready visualizations with minimal code. It streamlines plot creation for researchers and analysts, allowing features such as statistical annotation, theme customization, and plot arrangement with fewer lines of code. -
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whylogs
The open standard for data logging
...With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to track changes in their dataset Create data constraints to know whether their data looks the way it should. Quickly visualize key summary statistics about their datasets. whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond simple mean, median, and standard deviation measures), the number of missing values, and a wide range of configurable custom metrics. By capturing these summary statistics, we are able to accurately represent the data and enable all of the use cases described in the introduction. -
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Population Shift Monitoring
Monitor the stability of a Pandas or Spark dataframe
popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets. popmon creates histograms of features binned in time-slices, and compares the stability of the profiles and distributions of those histograms using statistical tests, both over time and with respect to a reference. It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two features. popmon can automatically flag and alert on changes observed over time, such as trends, shifts, peaks, outliers, anomalies, changing correlations, etc, using monitoring business rules. ... -
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ggforce
Accelerating ggplot2
ggforce is an extension package for ggplot2 that introduces specialized statistical transforms, geoms, and layout utilities to enhance and complement the built-in ggplot2 offerings. It enables more advanced visualization techniques such as faceting enhancements, hulls, annotation marks, and novel layouts for network data and marked regions. -
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Gretel Synthetics
Synthetic data generators for structured and unstructured text
...Generate unlimited data in minutes with synthetic data delivered as-a-service. Synthesize data that are as good or better than your original dataset, and maintain relationships and statistical insights. Customize privacy settings so that data is always safe while remaining useful for downstream workflows. Ensure data accuracy and privacy confidently with expert-grade reports. Need to synthesize one or multiple data types? We have you covered. Even take advantage or multimodal data generation. Synthesize and transform multiple tables or entire relational databases. ... -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions... -
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awesome-single-cell
Community-curated list of software packages and data resources
...Rapid, accurate and memory-frugal preprocessing of single-cell and single-nucleus RNA-seq data. Find bimodal, unimodal, and multimodal features in your data. Ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. An analytical framework for big-scale single cell data. Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction. -
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see
Visualisation toolbox for beautiful and publication-ready figures
see is an R package that serves as the visualization component of the easystats ecosystem, providing plotting utilities to produce publication-ready visualizations of statistical model parameters, diagnostics, predictions, and performance metrics. It works in conjunction with other easystats packages (such as parameters, performance, modelbased, bayestestR, etc.) to convert model outputs or summary objects into visual forms (dot-and-whisker plots, diagnostic plots, residual plots, etc.). It includes themes, scales, geoms for ggplot2, and custom color palettes to make visual summaries more informative and attractive. -
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seaborn
Statistical data visualization in Python
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. ... -
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Gwyddion
Scanning probe microscopy data visualisation and analysis
A data visualization and processing tool for scanning probe microscopy (SPM, i.e. AFM, STM, MFM, SNOM/NSOM, ...) and profilometry data, useful also for general image and 2D data analysis. -
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HEALPix
Data Analysis, Simulations and Visualization on the Sphere
Software for pixelization, hierarchical indexation, synthesis, analysis, and visualization of data on the sphere. Please acknowledge HEALPix by quoting the web page http://healpix.sourceforge.net (or https://healpix.sourceforge.io) and publication: K.M. Gorski et al., 2005, Ap.J., 622, p.759 Full software documentation available at https://healpix.sourceforge.io/documentation.php Wiki Pages: https://sourceforge.net/p/healpix/wiki/Home Exchanging Data with HEALPix (in FITS files):...
