Solo empowers scientists and engineers with a host of point-and-click data-discovery tools including PLS, PCA and many other multivariate and machine learning methods. It includes the main PLS_Toolbox graphical user interfaces, but MATLAB is not required! Import data from a variety of different file types and quickly assemble it into convenient DataSet objects to easily manage labels, axis scales, and classes. Include/exclude data from the analysis with just a click. Drag-and-drop data to our comprehensive Analysis window for modeling and analyzing results. Our integrated modeling guide and access to a large number of analysis and preprocessing techniques makes this graphical user interface environment appropriate for novice and expert users alike.
Solo provides the Graphical Interfaces to quickly manage and analyze data, author and apply models and interpret results.
Key Methods Included:
- Data Exploration and Pattern Recognition (Principal Components Analysis (PCA), Parallel Factor Analysis (PARAFAC), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Multiway PCA…)
- Classification (SIMCA, k-nearest neighbors, Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine Classification (SVM-DA), Artificial Neural Network Classification (ANN-DA), Deep Learning ANN Classification (ANNDL-DA)Boosted Regression and Classification Trees (XGBoost), Clustering (HCA)…)
- Linear and Non-Linear Regression (Partial Least Squares (PLS), Principal Components Regression (PCR), Multiple Linear Regression (MLR), Classical Least Squares (CLS), Support Vector Machine Regression (SVM), Artificial Neural Networks (ANN), Deep Learning ANN (ANNDL), Boosted Regression and Classification Trees (XGBoost), N-way PLS, Locally Weighted Regression…)
- Self-modeling Curve Resolution, Pure Variable Methods (Multivariate Curve Resolution (MCR), Purity (compare to SIMPLSMA), CODA_DW, CompareLCMS…)
- Curve fitting and Distribution fitting and analysis tools
- Instrument Standardization (Piece-wise Direct, Windowed Picewise, OSC, Generalized Least Squares Preprocessing, Spectral Subspace Transformation (SST)…)
- Advanced Graphical Data Set Editing and Visualization Tools
- Advanced Customizable Order-Specific Preprocessing (Centering, Scaling, Smoothing, Derivatizing, Transformations, Baselining, Generalized Least Squares Weighting (GLSW) and many, many more)
- Missing Data Support (SVD and NIPALS)
- Variable Selection (Genetic algorithms, iPLS, Selectivity Ratio, VIP…)
In general, Solo should work on most modern computers. For specific requirements see our Release Notes page, identify the version of MATLAB used to compile Solo from the Solo MCR column. See the MathWorks system requirements page for more information on specific system requirements for that version.
Eigenvector Research offers user support for Solo by e-mail at email@example.com. Questions are almost always answered within 24 hours (and usually much less). Updates and bug fixes can be downloaded from our web site. For information on other support options, see our technical support page.
Get More Information:
- Download a fully functional 45-day demo
- Learn about our latest release
- View the Software User Guide on-line
- View the License Maintenance Agreement
- Users with current License Maintenance Agreements:
download the newest version now. IT’S FREE!
- New users: order Solo on-line.
For information on multi-client servers, site-licenses, and OEM options, contact us by phone (509.662.9213) or e-mail (firstname.lastname@example.org). Our product pricelist information pageincludes pricing and other order information for all of our products.