Search Results for "v2ray-core"
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Showing 9 open source projects for "v2ray-core"
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R Source
Read-only mirror of R source code
The wch/r-source repository is a read-only mirror of the official R language source code, maintained to reflect the upstream Subversion (SVN) R core development tree. This mirror provides public visibility into R’s internals—everything from the interpreter, base and recommended packages, documentation, and C/Fortran code under the hood. It is updated hourly to stay in sync with the upstream SVN. Although it mirrors the R source for browsing and reference, it is not the “canonical development repo* (i.e. you can’t submit pull requests via that mirror). ... -
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tidyr
Tidy Messy Data
tidyr is a core tidyverse package designed to help reshape and clean messy datasets into tidy data—i.e., data frames where each variable is a column, each observation is a row, and each value is a cell. -
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easystats
The R easystats-project
easystats is a meta‑package that installs and unifies a suite of R packages for post‑processing statistical models. It delivers a consistent API to assess model performance, effect sizes, parameters, and to generate reports and visualizations, all with minimal dependencies and maximum clarity. -
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rayshader
R Package for 2D and 3D mapping and data visualization
This is an R package designed for producing beautiful and interactive 2D and 3D visualizations — especially maps and terrain renderings — using elevation/gridded data and ray-tracing / hill-shading methods. At its core, rayshader takes a matrix of elevations and applies shading, texture, ambient occlusion, overlays, and light modeling (ray shade, lambertian shading, etc.) to produce realistic relief maps. Users can rotate, zoom, and animate the scenes or script camera trajectories programmatically. It supports outputting high-quality renders via path tracing (using a companion package) and also offers depth-of-field (“cinematic blur”) effects to bring visual focus into scenes. ... -
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mlr3
mlr3: Machine Learning in R - next generation
mlr3 is a modern, object-oriented R framework for machine learning. It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via companion packages. -
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tidyverse
Easily install and load packages from the tidyverse
tidyverse is a meta‑package that installs and loads a cohesive suite of R packages designed for data science, sharing underlying design principles, grammar, and data structures. Core components include ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats, and more. It promotes tidy data workflows and consistency across tasks. -
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Covidex
Ultra fast and accurate subtyping tool of viral genomes.
...If more than 1000 sequences are loaded the tool will run in multithread mode. Capable of classifying 16000 genome sequences in less than a minute (AMD Ryzen 7 1700 8-core Processor 3 GHz) For a Web-based version of the app (only for small datasets: 100 seqs max) please go to http://covidex.unlu.edu.ar If you use Covidex please consider citing the following preprint: https://biorxiv.org/cgi/content/short/2020.08.21.261347v1 If you think my work is useful you can buy me a coffee! https://www.buymeacoffee.com/mcacciabue -
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Statistics for Data Scientists
"Statistics for Data Scientists: 50 Essential Concepts"
The “statistics-for-data-scientists” repository is a pedagogical resource designed to bridge rigorous statistics theory and practical data science workflows. The code and materials are intended to help data scientists and analysts grasp statistical principles (e.g. inference, regressions, hypothesis testing, probability, confidence intervals) in contexts relevant to real data analysis tasks. The repository includes Jupyter notebooks, R scripts, worked examples, and possibly problem sets that... -
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DataScienceR
a curated list of R tutorials for Data Science, NLP
The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting, and reproducible workflows. The repository also shows examples of linking R with external resources — APIs, databases, and file formats — and integrating into larger pipelines. ... -
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