Search Results for "statistics"
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Showing 194 open source projects for "statistics"
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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. ... -
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StatsBase.jl
Basic statistics for Julia
StatsBase.jl is a Julia package that provides basic support for statistics. Particularly, it implements a variety of statistics-related functions, such as scalar statistics, high-order moment computation, counting, ranking, covariances, sampling, and empirical density estimation. -
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SOFA is a statistics, analysis, and reporting program with an emphasis on ease of use, learn as you go, and beautiful output.
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OnlineStats.jl
Single-pass algorithms for statistics
OnlineStats does statistics and data visualization for big/streaming data via online algorithms. High-performance single-pass algorithms for statistics and data viz. Updated one observation at a time. Algorithms use O(1) memory. Algorithms use O(1) memory. -
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MultivariateStats.jl
A Julia package for multivariate statistics and data analysis
A Julia package for multivariate statistics and data analysis (e.g. dimensionality reduction). -
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Panda-Helper
Panda-Helper: Data profiling utility for Pandas DataFrames and Series
Panda-Helper is a simple data-profiling utility for Pandas DataFrames and Series. Assess data quality and usefulness with minimal effort. Quickly perform initial data exploration, so you can move on to more in-depth analysis. -
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pandas
Fast, flexible and powerful Python data analysis toolkit
pandas is a Python data analysis library that provides high-performance, user friendly data structures and data analysis tools for the Python programming language. It enables you to carry out entire data analysis workflows in Python without having to switch to a more domain specific language. With pandas, performance, productivity and collaboration in doing data analysis in Python can significantly increase. pandas is continuously being developed to be a fundamental high-level building... -
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Optopsy
A nimble options backtesting library for Python
...As such, we just need to call the long_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. Here we also use Panda's round() function afterwards to return statistics within two decimal places. -
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BPB-Worker-Panel
A GUI Panel providing Worker subscriptions for VLESS
BPB-Worker-Panel is a web interface for monitoring and managing distributed workers and task queues. It provides real-time statistics on job statuses, worker performance, queue lengths, and logs. Designed for systems using task handlers like Celery, Sidekiq, or custom job runners, the panel helps administrators visualize worker load, inspect tasks, and manually retry or delete them as needed. -
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Datumaro
Dataset Management Framework, a Python library and a CLI tool to build
...Datumaro makes it easy to merge datasets, split them into training/validation/test subsets, filter or transform annotations, and validate annotation quality — all while preserving metadata and supporting detailed statistics. It’s especially useful when you’re dealing with heterogeneous data sources or need to prepare complex datasets for machine learning workflows, freeing you from writing custom scripts for every format conversion. -
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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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Metaflow
A framework for real-life data science
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. -
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,... -
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Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
Julia can track how many times, if any, each line of your code is run. This is useful for measuring how much of your code base your tests actually test, and can reveal the parts of your code that are not tested and might be hiding a bug. You can use Coverage.jl to summarize the results of this tracking or to send them to a service like Coveralls.io or Codecov.io. Julia can track how much memory is allocated by each line of your code. This can reveal problems like type instability, or... -
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cgm-remote-monitor
Nightscout web monitor
Nightscout’s cgm-remote-monitor is a real-time web application for DIY diabetes management. It connects with CGM devices like Dexcom, Medtronic, and Libre, pulls glucose readings, and visualizes them on a browser-based dashboard, accessible from phones, watches, or IV bags. The system supports trend alerts, historical graphs, and community plug-ins for remote patient monitoring, educational use, and family connectivity. -
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Tracearr
Real-time monitoring for Plex, Jellyfin, and Emby servers
Tracearr is an open-source streaming access manager designed to help media server owners understand and control who is accessing their content across platforms like Plex, Jellyfin, and Emby. It centralizes access statistics and provides tools for filtering and visualizing usage, making it easier to answer questions about viewer behavior and resource consumption, such as who is watching what and when. By monitoring requests in real time, the system can differentiate between unique users, bots, and unauthorized clients, helping administrators tighten access control and improve server performance. ... -
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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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CausalityTools.jl
Algorithms for detecting associations, dynamical influences
CausalityTools.jl is a package for quantifying associations and dynamical coupling between datasets, independence testing, and causal inference. Association measures from conventional statistics, information theory, and dynamical systems theory, for example, distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more. A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can think of. For example, we offer the generic SurrogateTest, which is fully compatible with TimeseriesSurrogates.jl, and the LocalPermutationTest for conditional independence testing. -
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Profile Summary
Tool for visualizing GitHub profiles
Profile Summary for GitHub is a web application for turning a public GitHub account into a more visual overview of its development activity. It uses the GitHub API to collect profile and repository information and present it as summary graphics and statistics. Its visualizations include repository distribution by programming language and commit distribution by language. Users can generate summaries for GitHub usernames through the web interface. The project can also be self-hosted and configured with GitHub API tokens to increase request capacity. Built with Kotlin and Javalin, it provides a compact way to inspect a developer's GitHub presence beyond the standard profile layout. -
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collapse
Advanced and Fast Data Transformation in R
...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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Apache Hudi
Upserts, Deletes And Incremental Processing on Big Data
Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and Incrementals. Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage). Apache Hudi is a transactional data lake platform that brings database and data warehouse capabilities to the data lake. Hudi reimagines slow old-school batch data processing with a powerful new incremental processing framework for low latency minute-level analytics. Hudi provides... -
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Riemann
A network event stream processing system, in Clojure
Riemann aggregates events from your servers and applications with a powerful stream processing language. Send an email for every exception in your app. Track the latency distribution of your web app. See the top processes on any host, by memory and CPU. Combine statistics from every Riak node in your cluster and forward to Graphite. Track user activity from second to second. Riemann streams are just functions which accept an event. Events are just structs with some common fields like :host and :service You can use dozens of built-in streams for filtering, altering, and combining events, or write your own. ... -
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forecast
Forecasting Functions for Time Series and Linear Models
...It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. Residual checks, model accuracy, plots, forecast error measures etc. -
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clusterProfiler
A universal enrichment tool for interpreting omics data
clusterProfiler is an R/Bioconductor package that provides a unified workflow for functional enrichment analysis to interpret high-throughput omics results. It supports both over-representation analysis and gene set enrichment analysis, letting you work with unranked gene lists or ranked statistics from differential pipelines. The package connects to multiple knowledge bases—such as Gene Ontology, KEGG, Reactome, Disease Ontology, MeSH and others—through a consistent interface so you can query different biological lenses without rewriting code. It is designed for breadth, covering coding and non-coding features and thousands of organisms by leveraging continuously updated annotations. ... -
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targets
Function-oriented Make-like declarative workflows for R
The targets package is a pipeline / workflow management tool in R, designed to coordinate multi‐step computational workflows in data science / statistics. It tracks dependencies between “targets” (computational steps), skips steps whose upstream data or code hasn’t changed, supports parallel computation, branching (dynamic generation of sub‐targets), file format abstractions, and encourages reproducible and efficient analyses. It’s something like GNU Make for R, but more integrated. ...
