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What is Jenner?

Jenner serves as an analytics platform that seamlessly integrates with SAS, enabling organizations to run their pre-existing SAS programs—including the DATA step, macro language, PROC SQL, and a total of 230 documented procedures—without requiring a SAS license. Built using Rust, this platform features a clean-room implementation of the SAS 9.4 language and is compatible with multiple operating systems, including Windows, macOS, and Linux. By allowing organizations to execute current programs without any modifications, Jenner significantly reduces the financial strain often associated with moving away from SAS. The platform is accessible in three distinct environments, each with its unique billing model: it can be utilized through a shared JupyterLab workspace online (beginning with a free tier followed by custom pricing), installed as a native application on personal devices (with options for free use or an annual fee of 199 USD), or operated via HTTP for pipelines and agents (also starting with a free tier and subsequent pricing arrangements). In addition, Jenner provides comprehensive reference documentation for all 230 procedures across 16 languages, enhancing accessibility for a wide range of users. This versatile strategy positions Jenner as an appealing solution for organizations aiming to maximize their existing SAS functionalities without the usual licensing expenses, offering a practical pathway to modern analytics. As such, it reflects a significant advancement in the realm of data analytics integration.

What is DataMelt?

DataMelt, commonly referred to as "DMelt," is a versatile environment designed for numerical computations, data analysis, data mining, and computational statistics. It facilitates the plotting of functions and datasets in both 2D and 3D, enables statistical testing, and supports various forms of data analysis, numeric computations, and function minimization. Additionally, it is capable of solving linear and differential equations, and provides methods for symbolic, linear, and non-linear regression. The Java API included in DataMelt integrates neural network capabilities alongside various data manipulation techniques utilizing different algorithms. Furthermore, it offers support for symbolic computations through Octave/Matlab programming elements. As a computational environment based on a Java platform, DataMelt is compatible with multiple operating systems and supports various programming languages, distinguishing it from other statistical tools that often restrict users to a single language. This software uniquely combines Java, the most prevalent enterprise language globally, with popular data science scripting languages such as Jython (Python), Groovy, and JRuby, thereby enhancing its versatility and user accessibility. Consequently, DataMelt emerges as an essential tool for researchers and analysts seeking a comprehensive solution for complex data-driven tasks.

Media

No images available

Media

Integrations Supported

Integrations Supported

Apache NetBeans
Eclipse BIRT

API Availability

API Availability

Has API

Pricing Information

Free tier; Mac app $199/year
Free Version

Pricing Information

$0
Access to full documentation (examples, Java API documentation, online manual) requires one-time membership payment (20$/user)
Free Version

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

Android
Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support

Training Options

Documentation Hub

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Jenner Analytics

Company Website

jenneranalytics.com

Company Facts

Organization Name

jWork.ORG

Date Founded

2005

Company Location

United States

Company Website

datamelt.org

Categories and Features

Statistical Analysis

Not specified

Categories and Features

Artificial Intelligence

For Healthcare
Image Recognition
Machine Learning
Natural Language Processing
Predictive Analytics

Data Analysis

Data Discovery
Data Visualization
Predictive Analytics
Regression Analysis
Statistical Modeling
Text Analytics

Data Mining

Data Extraction
Data Visualization
Machine Learning
Predictive Modeling
Statistical Analysis
Text Mining

Data Visualization

Analytics
Simulation Models
Visual Discovery

Deep Learning

Convolutional Neural Networks
Document Classification
ML Algorithm Library
Model Training
Self-Learning
Visualization

Education

Not specified

Neural Network

Not specified

Statistical Analysis

Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Simulation
Survival Analysis
Time Series
Visualization

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