camLine welcomes the release of CornerstoneR 3.3, a free extension providing users with various flexible statistical functionalities to pre-process, analyze, and visualize data within the dynamic, interactive, and user-friendly Workmap structure of Cornerstone. The latest release introduces the new Validation Scores function to compute metrics for model evaluation.
Users can now enjoy improvements of the Reliability Distribution Fitting and Gaussian Process Regression functions, and other continuous improvements for the overall available functions. With the powerful and continuously developing open-source software R, CornerstoneR 3.3 serves the latest state-of-the-art functionalities to the Cornerstone environment.
“Our team is excited to offer this extension to our users, which allows them to extend Cornerstone's data analysis capabilities without the need for R programming skills. Users with R knowledge can still write or load their own R scripts and packages into the software, providing them almost endless possibilities to explore data within Cornerstone's compact Workmap structure,” said Alessandra Corvonato, Product Manager at camLine.
The Validation Scores function is a new addition from CornerstoneR 3.3, allowing a direct calculation and visualization of the most common metrics for statistical model evaluation and displaying Actual vs Predicted Values Graph for Regression Models.
This release is an extension with a wide range of functionalities for statistical modeling and predicting, including the updated Gaussian Process Regression with improved starting values for the hyperparameters, Decision Trees, Random Forests, Logistic Regression, and Function Fitting.
More flexibility to customize graph outputs is offered in this latest release for the Reliability Distribution Fitting feature for censored and uncensored data, in addition to data pre-processing (Reshaping, Transposing, Missing Value Handling, and more), the Mosaic Plot as a graphic tool, and more.
The Model Predict function enables users to predict response values for unseen data, especially in Machine Learning when it comes to splitting data into training and testing data, and using the test dataset for model validation.
The release also comes with K-Means Clustering, a common cluster analysis method for Unsupervised Learning, Time Series Analysis Methods (Feature Extraction, Auto and Cross-Correlation, Moving Average Filter), and Time Series Modeling.
CornerstoneR 3.3 is now available – users can access the functions overview with detailed user guides at https://camline.gitlab.io/CornerstoneR/docs/. The extension requires Cornerstone 8 and R 4.1.
Users can now enjoy improvements of the Reliability Distribution Fitting and Gaussian Process Regression functions, and other continuous improvements for the overall available functions. With the powerful and continuously developing open-source software R, CornerstoneR 3.3 serves the latest state-of-the-art functionalities to the Cornerstone environment.
“Our team is excited to offer this extension to our users, which allows them to extend Cornerstone's data analysis capabilities without the need for R programming skills. Users with R knowledge can still write or load their own R scripts and packages into the software, providing them almost endless possibilities to explore data within Cornerstone's compact Workmap structure,” said Alessandra Corvonato, Product Manager at camLine.
The Validation Scores function is a new addition from CornerstoneR 3.3, allowing a direct calculation and visualization of the most common metrics for statistical model evaluation and displaying Actual vs Predicted Values Graph for Regression Models.
This release is an extension with a wide range of functionalities for statistical modeling and predicting, including the updated Gaussian Process Regression with improved starting values for the hyperparameters, Decision Trees, Random Forests, Logistic Regression, and Function Fitting.
More flexibility to customize graph outputs is offered in this latest release for the Reliability Distribution Fitting feature for censored and uncensored data, in addition to data pre-processing (Reshaping, Transposing, Missing Value Handling, and more), the Mosaic Plot as a graphic tool, and more.
The Model Predict function enables users to predict response values for unseen data, especially in Machine Learning when it comes to splitting data into training and testing data, and using the test dataset for model validation.
The release also comes with K-Means Clustering, a common cluster analysis method for Unsupervised Learning, Time Series Analysis Methods (Feature Extraction, Auto and Cross-Correlation, Moving Average Filter), and Time Series Modeling.
CornerstoneR 3.3 is now available – users can access the functions overview with detailed user guides at https://camline.gitlab.io/CornerstoneR/docs/. The extension requires Cornerstone 8 and R 4.1.
