Want to try it now? Contact us and start immediately your free trial of Kodemetrics!
Kodemetrics
The no-code and user-friendly tool
to analyse experimental data
Programming languages are now essential for carrying out advanced experimental designs and analyses, but they are often an entry barrier, becoming a significant waste of time for many domain experts. Kodemetrics helps researchers skip this barrier and focus on the research aspects of their studies.
Our experience at your service.
Thanks to our experience in the chemometrics and research field, we have a clear understanding of experimentation needs and processes in labs. Indeed, we have put together the most useful techniques of data analysis into a user-friendly tool, allowing users (whether they are students, researchers, labs or industrial R&D departments) to easily explore their experimental data. The selection of all the parameters required for each step of the analysis is guided in Kodemetrics through a simple graphical interface.
Features and Plans
Kodemetrics was designed for two types of use, with features tailored both for academic settings—ideal for small-scale research and studies—and for companies that need to conduct structured research, easily save their workspace, and generate advanced reports.
Discover the features that best suit your needs.
Data Entry & Visualization
- Data Entry in multiple format (.csv, .xls/.xlsx e .RData)
- Tabular and graphic visualization of the variables and their patterns
- Missin values visualization and management
Exploratory Analysis: PCA
- Option to select the number of components to use
- Option to select variables to include
- Results visualization through scree plot, scores plot, loadings plot, T² and Q error plots with related contribution plots
- Model download
- Analysis report download in .docx format
Predictive Analysis: Linear Regression Models
- Preprocessing, model type and variable selection
- Coefficient estimates, p-values
- Diagnostic plots
- Response surface visualization
- Model download
- Analysis report download in .docx format
- Prediction of new observations
Predictive Analysis : PLS-R and PLS-DA
- Option to select the number of components to use
- Option to select variables to include
- Model summary with metric values (RMSE, R-squared, and MAE / Accuracy, Sensitivity, Specificity, Precision, Recall, etc.)
- Model goodness-of-fit plots
- Confusion matrix (for PLS-DA)
- Variable Importance plots and component coefficient plots
- Model download
- Analysis report download in .docx format
Models for mixtures
- Preprocessing, model type and variable selection
- Coefficient estimates, p-values
- Diagnostic plots
- Response surface visualization
- Effects plot
- Model download
- Analysis report download in .docx format
Design of Experiments
- Full Factorial Design (2 or 3 levels)
- Fractional Factorial Design (2 levels)
- Plackett-Burman Design
- Doehlert Design
- Central Composite Design (CCD)
- Box-Behnken Design
- Mixture designs with constraints on individual components and linear constraints between components
- D-optimal and I-optimal Designs
- Customization of factor levels and names
- Visualization of the experimental region
- Design download in .csv format
Multi-Criteria Decision Making
- Pareto method, for choices based on a single criterion
- Simple Additive Ranking (SAR)
- Utility
- Desirability
- Dominance
- Multi-Attribute Utility Theory (MAUT)
- Technique for Order Preference by Similarity to Ideal Design (TOPSIS)
- Analytical Hierarchy Process (AHP), for decisions based on qualitative criteria
- Results in tabular and graphical format
Want to try it now? Contact us and start immediately your free trial of Kodemetrics!
