CornerstoneR 3.2 Now Available, Integrating R’s State-of-the-Art Functionalities for Enhanced Data Analysis
New release of CornerstoneR extension offers statistical modeling and predicting functionalities, time series analysis methods, and more, all accessible from within Cornerstone
According to Alessandra Corvonato, the product manager at camLine, “By connecting Cornerstone with R, we enable access to R’s powerful functionalities to pre-process, analyze, and visualize data. Our team is thrilled to offer this integration to our users, allowing them to take advantage of R’s latest statistical functionalities without any programming knowledge in R.” Furthermore, she continued: “However, users with R knowledge can also write their own R script in Cornerstone or load their R scripts and packages into Cornerstone.”
The CornerstoneR 3.2 extension features a wide range of functionalities for statistical modeling and predicting, including Gaussian Process Regression, Decision Trees, Random Forests, Logistic Regression, and Function Fitting. The Model Predict function enables users to predict response values for unseen data. The new version also includes K-Means Clustering, a common cluster analysis method for Unsupervised Learning, time series analysis methods such as Feature Extraction, Auto- and Cross-Correlation, Moving Average Filter and Time Series Modeling, as well as a tool for Correlation Analysis. Moreover, CornerstoneR 3.2 offers Reliability Distribution Fitting for censored and uncensored data, functions for data pre-processing such as Reshaping, Transposing, and Missing Value Handling, the Mosaic Plot as graphical tool, and many more.
CornerstoneR 3.2 is available now, and users can access an overview of its functions with detailed user guides at https://camline.gitlab.io/CornerstoneR/docs/. The extension requires Cornerstone 8 and R 4.1.