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OPENCPU - API DOCS
The root of the API is dynamic. It defaults to /ocpu/, however system administrators can change this.Clients should make the OpenCPU server address and root path configurable. In the examples below we assumethe default /ocpu/.
OPENCPU USER MANUAL
1.2 How it works. API for calling R functions and scripts, and managing resources (objects, files, graphics, ) Central: leverages R packages. Packages are natural and powerful container format for R bundling and shipping R code, data, manuals and files. OPENCPU - JAVASCRIPT CLIENT Management of state in OpenCPU is quite different from what R users are used to, which can be confusing at first. In OpenCPU, the client does not have a single private R process on the server which handles all incoming requests, such as in e.g. Shiny or in an R terminal session. Instead, OpenCPU is plain HTTP, and therefore each requests is anonymous and stateless.OPENCPU - HELP
An OpenCPU app is an R package which includes some web page(s) that call the R functions in the package using the OpenCPU API. This makes a convenient way to develop, package and ship portable, standalone Rweb applications.
OPENCPU - ANNOUNCING OPENCPU 2.0: BUILDING AND DEPLOYING The OpenCPU 2.0 branch is the biggest upgrade to the system since the 1.0 release 4 years ago. The server API is backwards compatible so that existing clients and apps will keep working. Internals have been rewritten to make development easier and further enhance the performance and robustness of the server system. OPENCPU - CALLING R FUNCTIONS THROUGH AJAX USING OPENCPU.JSSEE MORE ONOPENCPU.ORG
OPENCPU - DEPLOYING A SCORING ENGINE FOR PREDICTIVE TLDR/abstract: See the tvscore demo app or this jsfiddle for all of this in action. This post explains how to use the OpenCPU system to setup a scoring engine for calculating real time predictions. In our example we use the predict.gam function from the mgcv package to make predictions based on a generalized additive model. The entire process consists of four steps: OPENCPU - OPENCPU 2.1 RELEASE: SCALABLE R SERVICES The 2.1 branch is the new major release of the OpenCPU system. The changes in this version are mostly internal. The built-in CI system has switched to the new `remotes` package for installing packages from GitHub. The `opencpu-server` package has been tweaked to work better inside docker, and we now target R 3.5 on all platforms. OPENCPU - WHY USE DOCKER WITH R? A DEVOPS PERSPECTIVE There have been several blog posts going around about why one would use Docker with R. In this post I’ll try to add a DevOps point of view and explain how containerizing R is used in the context of the OpenCPU system for building and deploying R servers. OPENCPU - PRODUCING AND REPRODUCING RESULTSBLOGAPIDOWNLOADAPPSCLOUDJS OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable HTTP API for data analysis based on R. You can either use the public servers or host your own.OPENCPU - API DOCS
The root of the API is dynamic. It defaults to /ocpu/, however system administrators can change this.Clients should make the OpenCPU server address and root path configurable. In the examples below we assumethe default /ocpu/.
OPENCPU USER MANUAL
1.2 How it works. API for calling R functions and scripts, and managing resources (objects, files, graphics, ) Central: leverages R packages. Packages are natural and powerful container format for R bundling and shipping R code, data, manuals and files. OPENCPU - JAVASCRIPT CLIENT Management of state in OpenCPU is quite different from what R users are used to, which can be confusing at first. In OpenCPU, the client does not have a single private R process on the server which handles all incoming requests, such as in e.g. Shiny or in an R terminal session. Instead, OpenCPU is plain HTTP, and therefore each requests is anonymous and stateless.OPENCPU - HELP
An OpenCPU app is an R package which includes some web page(s) that call the R functions in the package using the OpenCPU API. This makes a convenient way to develop, package and ship portable, standalone Rweb applications.
OPENCPU - ANNOUNCING OPENCPU 2.0: BUILDING AND DEPLOYING The OpenCPU 2.0 branch is the biggest upgrade to the system since the 1.0 release 4 years ago. The server API is backwards compatible so that existing clients and apps will keep working. Internals have been rewritten to make development easier and further enhance the performance and robustness of the server system. OPENCPU - CALLING R FUNCTIONS THROUGH AJAX USING OPENCPU.JSSEE MORE ONOPENCPU.ORG
OPENCPU - DEPLOYING A SCORING ENGINE FOR PREDICTIVE TLDR/abstract: See the tvscore demo app or this jsfiddle for all of this in action. This post explains how to use the OpenCPU system to setup a scoring engine for calculating real time predictions. In our example we use the predict.gam function from the mgcv package to make predictions based on a generalized additive model. The entire process consists of four steps: OPENCPU - OPENCPU 2.1 RELEASE: SCALABLE R SERVICES The 2.1 branch is the new major release of the OpenCPU system. The changes in this version are mostly internal. The built-in CI system has switched to the new `remotes` package for installing packages from GitHub. The `opencpu-server` package has been tweaked to work better inside docker, and we now target R 3.5 on all platforms. OPENCPU - WHY USE DOCKER WITH R? A DEVOPS PERSPECTIVE There have been several blog posts going around about why one would use Docker with R. In this post I’ll try to add a DevOps point of view and explain how containerizing R is used in the context of the OpenCPU system for building and deploying R servers.OPENCPU - HELP
An OpenCPU app is an R package which includes some web page(s) that call the R functions in the package using the OpenCPU API. This makes a convenient way to develop, package and ship portable, standalone Rweb applications.
OPENCPU - ANNOUNCING OPENCPU 2.0: BUILDING AND DEPLOYING The OpenCPU 2.0 branch is the biggest upgrade to the system since the 1.0 release 4 years ago. The server API is backwards compatible so that existing clients and apps will keep working. Internals have been rewritten to make development easier and further enhance the performance and robustness of the server system. OPENCPU - CALLING R FUNCTIONS THROUGH AJAX USING OPENCPU.JS The opencpu.js library builds on jQuery to call R functions through AJAX, straight from the browser. This makes it easy to embed R based computation or graphics in apps.Moreover, asynchronous requests (which are native in Javascript) make parallelization a natural part of theapplication.
OPENCPU - DEPLOYING A SCORING ENGINE FOR PREDICTIVE TLDR/abstract: See the tvscore demo app or this jsfiddle for all of this in action. This post explains how to use the OpenCPU system to setup a scoring engine for calculating real time predictions. In our example we use the predict.gam function from the mgcv package to make predictions based on a generalized additive model. The entire process consists of four steps: OPENCPU - NEW PACKAGE: JSONLITE. A SMART(ER) JSON ENCODER This week we released a new package on CRAN: jsonlite.This package is a fork of RJSONIO by Duncan Temple Lang and builds on the same parser, but uses a different mapping between R objects and JSON data. The package vignette goes in great detail and has many examples on how JSON data are converted to R objects and vice versa. To try it: #install install.packages ("jsonlite", repos = "httpOPENCPU USER MANUAL
OpenCPU: Producing and Reproducing Results. 6.2.4 Performance Tuning. Important: each request gets handed in a fork. Thefore they are unaffected but also means that each requests has to load all packages that are not preloaded. OPENCPU - COMPRESSION BENCHMARKS: BROTLI, GZIP, XZ, BZ2 Brotli is a new compression algorithm optimized for the web, in particular small text documents. Brotli decompression is at least as fast as for gzip while significantly improving the compression ratio. SIA PLOTS USING GGPLOT2 # import the data. Replace this line with a read.csv() or similar call # to you own local file. data ("demo.siber.data") # make a copy of our data for use here in this example, and # set the columns group and community to be factor type using dplyr. # Additionally rename the isotope data columns and drop the iso1 and iso2 # columns using the .keep option to keep only those that were not used GETTING STARTED WITH NNS: PARTIAL MOMENTS Partial Moments. Why is it necessary to parse the variance with partial moments? The additional information generated from partial moments permits a level of analysis simply not possible with traditional summary statistics. GETTING STARTED WITH NNS: CORRELATION AND DEPENDENCE Correlation and Dependence. The limitations of linear correlation are well known. Often one uses correlation, when dependence is the intended measure for defining the relationship between variables.* Blog
* API
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* JS
* Papers
* Help
* __
AN API FOR EMBEDDED SCIENTIFIC COMPUTING OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable HTTP API for data analysis based on R. You can either use the public servers or host your own. SEAMLESS R AND JAVASCRIPT INTEGRATION The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today. Enjoy simple RPC and data I/O through standard Ajax techniques. Check for yourself by trying the jsfiddle examples.
LESS IS MORE
No need to learn crazy widgets or obscure framworks. The OpenCPU API is a clean and simple interface to R, nothing more nothing less. It is compatible with any language or framework that speaksHTTP.
STATISTICAL APPS
OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source example apps are available from Github. Any app can be used on the public server or locally within an R session. CONCURRENCY BY DESIGN The OpenCPU API is concurrent by design. Your applications will naturally support parallel computing and asynchronous requests without introducing any additional complexity. EASY INSTALLATION, CONFIGURATION Installing your own OpenCPU server is super easy and only takes a few minutes. The cloud server supports continuous integration and is highly configurable to fit yourevery need.
RELIABLE, SECURE AND SCALABLE The cloud server builds on AppArmor to enforce security, and Nginx for caching and load balancing. All profiles and configurations can easily be customized. Our public servers have been steadily online for over a year without any problems. BUSINESS FRIENDLY LICENSING All parts of OpenCPU are released under the Apache2 license . The software can be used, modified and redistributed for both for open source and proprietary projects in academia, industry or elsewhere.__OpenCPU Home
__OpenCPU on Twitter __Github Development__OpenCPU Apps
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