Search Results for "data integration"
Sort By:
Showing 25 open source projects for "data integration"
View related business solutions-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
1
reticulate
R Interface to Python
reticulate is an R package from Posit that creates seamless interoperability between R and Python. It lets you call Python modules, classes, and functions from within R, automatically translating between R and Python data structures. Useful for combining Python tooling with R projects, data analysis, and RMarkdown reports. -
2
Seurat
R toolkit for single cell genomics
Seurat is a comprehensive R toolkit for single-cell genomics analysis, introduced by the Satija Lab at NYGC. It supports quality control, normalization, clustering, integration of multimodal data (e.g., scRNA‑seq, spatial, CITE‑seq), and visualization. Seurat v5 introduces scalable workflows and spatial transcriptomics support, commonly used in academic and industry research for single-cell studies. -
3
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. -
4
tidytext
Text mining using tidy tools
tidytext brings tidy data principles to text mining by converting text into a tidy data frame format. It provides tools for tokenization, sentiment analysis, n‑gram creation, and term‑document matrices, enabling interoperability with dplyr, ggplot2, and other tidyverse workflows. -
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
5
sparklyr
R interface for Apache Spark
sparklyr is an R package that provides seamless interfacing with Apache Spark clusters—either local or remote—while letting users write code in familiar R paradigms. It supplies a dplyr-compatible backend, Spark machine learning pipelines, SQL integration, and I/O utilities to manipulate and analyze large datasets distributed across cluster environments. -
6
forecast
Forecasting Functions for Time Series and Linear Models
The forecast package is a comprehensive R package for time series analysis and forecasting. 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... -
7
rmarkdown
Dynamic Documents for R
R Markdown is an R package for creating dynamic, reproducible documents that combine code (R, Python, SQL, etc.), results (figures, tables), and narrative text. Built on Knitr and Pandoc, it supports generating HTML, PDF, Word, slideshows, dashboards, and more. It’s widely used in data science and reproducible reporting workflows. -
8
rollama
Wrap the Ollama API, which allows you to run different LLMs
rollama is an R package that provides a convenient interface for interacting with local large language models through the Ollama API, bringing modern AI capabilities into the R ecosystem. It is designed to make LLM usage accessible to data scientists and researchers who work primarily in R, allowing them to generate text, analyze data, and create embeddings without relying on external cloud services. The package emphasizes reproducibility and privacy by enabling local execution of models,... -
9
workflowr
Organize your project into a research website
workflowr is an R package that helps researchers organize, version, and share their data science projects in a reproducible and transparent manner. It combines R Markdown, Git, and a structured file system to create a research website that tracks analysis, results, and code changes over time. It’s ideal for academic and collaborative research workflows. -
Veeam Data Platform v13.1Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
10
blogdown
Create Blogs and Websites with R Markdown
blogdown is an R package that enables the creation and maintenance of static websites and blogs using R Markdown and Hugo (or other static-site generators). Developed by Yihui Xie and team, it provides functions to initialize sites, write posts, manage themes, and deploy with minimal fuss. It seamlessly blends R code chunks and web content, ideal for data storytellers and technical bloggers. -
11
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... -
12
nichenetr
NicheNet: predict active ligand-target links between interacting cells
nichenetr: the R implementation of the NicheNet method. The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest. This model of prior information on... -
13
Statistical Rethinking 2024
This course teaches data analysis
The 2024 repository is the most recent version of the course, reflecting ongoing refinements in pedagogy, statistical modeling techniques, and coding practices. It provides updated notebooks, R scripts, and model examples, some streamlined and restructured compared to previous years. The 2024 repo also highlights the transition toward more robust Stan models and integration with newer Bayesian workflow practices, continuing to emphasize accessibility for learners while modernizing the tools.... -
14
Statistical Rethinking 2023
Statistical Rethinking Course for Jan-Mar 2023
The 2023 edition modernizes and expands on the same curriculum, adjusting exercises and code for newer versions of R, Stan, and supporting packages. It continues to provide scripts for lectures and tutorials, while integrating refinements to examples, notation, and computational workflows introduced that year. Compared with 2022, some models are rewritten for clarity, and teaching materials reflect refinements in McElreath’s evolving presentation of Bayesian data analysis. Students following... -
15
Data Analysis for the Life Sciences
Rmd source files for the HarvardX series PH525x
This repository holds the R Markdown (.Rmd) source files for the PH525x / HarvardX course series (Data Analysis for the Life Sciences / Genomics) managed by GenomicsClass. It functions as the canonical source for course lab exercises, lecture modules, and reading materials in reproducible format. Students and learners use these R Markdown files to follow along, knit notebooks, run code samples, and complete the lab-based assignments. The repo is licensed under MIT, allowing reuse and... -
16
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... -
17
Reproducible-research
A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.
In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. ... -
18
Harmony Data Integration
Fast, sensitive and accurate integration of single-cell data
Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets. It is especially useful for large single-cell datasets such as single-cell RNA-seq. Harmony has been tested on R versions =4. Please consult the DESCRIPTION file for more details on required R packages. Harmony has been tested on Linux, OS X, and Windows platforms. -
19
benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different... -
20
ComplexHeatmap
Make Complex Heatmaps
ComplexHeatmap is an R/Bioconductor package by Zuguang Gu et al. designed to create highly flexible, complex, richly annotated heatmaps and related visualizations. It allows arranging multiple heatmaps, adding annotations, combining heatmaps, customizing colors, layouts, and integrating other plots. Often used in genomics/bioinformatics to show expression, methylation, etc., with sidebars, annotations, clustering, etc. Highly customizable layout: combining different heatmaps, arranging and... -
21
sf (Simple Features)
Simple Features for R
sf is an R package that implements “simple features” (standardized vector spatial data) for R. It allows spatial vector data (points, lines, polygons etc.) to be represented as records in data frames (or tibbles) with geometry list columns, and performs spatial operations (geometry operations, coordinate reference system transformations, reading/writing spatial data, integration with spatial databases etc.). It interfaces to GDAL, GEOS, PROJ libraries for robust operations. ... -
22
TOFSIMS
R/Bioconductor toolkit for mass spectrometry data
The tofsims project is an R/Bioconductor toolkit designed for processing, analyzing, and visualizing imaging mass spectrometry data from Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) instruments. It supports importing raw and preprocessed data from popular instrument platforms (e.g. IONTOF, Ulvac-Phi) and provides methods for mass calibration, peak picking, and peak integration. The package allows transformation of spectra into 2D image structures (mass images), with operations such as binning, scaling, subsetting, and visual rendering. ... -
23
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,... -
24
Mastering Shiny
Mastering Shiny: a book
Mastering Shiny is a book (and its accompanying source repository) by Hadley Wickham that teaches people how to build interactive web applications using Shiny in R. It starts from basics (your first app, UI components, reactivity) and progresses to more advanced topics (dynamic UIs, modules, testing, security, performance). It is intended to help data scientists, analysts, or R users who may not have deep experience in web technologies become expert Shiny developers. The source code is open,... -
25
RStan
RStan, the R interface to Stan
RStan is the R interface to Stan, a C++ library for statistical modeling and high-performance statistical computation. It lets users specify models in the Stan modeling language (for Bayesian inference), compile them, and perform inference from R. Key inference approaches include full Bayesian inference via Hamiltonian Monte Carlo (specifically the No-U-Turn Sampler, NUTS), approximate Bayesian inference via variational methods, and optimization (penalized likelihood). RStan integrates with...
- Previous
- You're on page 1
- Next
