| Datasources | Support for unstructured data (Word, pdf, text) | feature_is_ready |
| Datasources | Support for Excel files (.xls, .xlsx) | feature_is_ready |
| Datasources | Support for SQL databases | feature_is_ready |
| Datasources | ‘Vertically aggregated’ datasource | feature_is_ready |
| Datasources | Datasource generated by an Agent Based Model (ABM) | feature_is_ready |
| Datasources | Support for CSV files | feature_is_ready |
| Datasources | Support for R package datasets | feature_is_ready |
| Datasources | ‘Horizontally aggregated’ datasource | Feature is on the roadmap |
| Datasources | Geographical data | Feature is on the roadmap |
| Datasources | Support of Twitter API | Feature is on the roadmap |
| API | Dedicated web interface for each saved predictive model | feature_is_ready |
| API | Rest JSON API for every saved predictive model | feature_is_ready |
| API | Serialization of predictive models | feature_is_ready |
| Agent based model | Datasource generated by an Agent Based Model (ABM) | feature_is_ready |
| Causal analysis | Creation of Bayesian Network | feature_is_ready |
| Causal analysis | Choice between different techniques for the building of the Bayesian network | feature_is_ready |
| Causal analysis | Conditional probability calculation | feature_is_ready |
| Causal analysis | Display of the complete network in the form of a table | feature_is_ready |
| Causal analysis | Export CSV of the complete network | feature_is_ready |
| Causal analysis | Modification of arcs of the bayesian network | feature_is_ready |
| Causal analysis | Network Restriction to causal variables of level 1 | feature_is_ready |
| Causal analysis | Network restriction to causal variables of level 2 | feature_is_ready |
| Clustering | Robust consensus clustering | feature_is_ready |
| Creation of additional variable | Creation of boolean additional variable | feature_is_ready |
| Creation of additional variable | Creation of categorical additional variable | feature_is_ready |
| Creation of additional variable | Creation of semantic variables based on the document-term matrix (DTM) | feature_is_ready |
| Descriptive analysis | Dataset summary | feature_is_ready |
| Descriptive analysis | Comparison of complete and incomplete data | feature_is_ready |
| Descriptive analysis | Interactive exploration of data in reduced dimension | feature_is_ready |
| Hypothesis testing | ‘Groupwise’ test of difference of a categorical variable between more than two groups | feature_is_ready |
| Hypothesis testing | ‘Groupwise’ test of difference of a continuous variable between more than two groups | feature_is_ready |
| Hypothesis testing | ‘Two by two’ test of difference of a categorical variable between more than two groups | feature_is_ready |
| Hypothesis testing | ‘Two by two’ test of difference of a continuous variable between more than two groups | feature_is_ready |
| Hypothesis testing | Box plot of a continuous variable for different groups | feature_is_ready |
| Hypothesis testing | Difference test of a continuous variable between two groups | feature_is_ready |
| Hypothesis testing | Probability distribution test | feature_is_ready |
| Hypothesis testing | Test of difference of a categorical variable between two groups | feature_is_ready |
| Misc | Serialization of user’s preferences | feature_is_ready |
| New feature | Robust consensus clustering | feature_is_ready |
| New feature | Creation of semantic variables based on the document-term matrix (DTM) | feature_is_ready |
| New feature | Interactive exploration of data in reduced dimension | feature_is_ready |
| New feature | Feature selection in predictive analysis | |
| Predictive analysis | Search for the best categorical predictive model for two groups by technique Vector Machine Support (SVM) | feature_is_ready |
| Predictive analysis | Graph of the specificity / sensitivity of the different models | feature_is_ready |
| Predictive analysis | Search for the best categorical predictive model for more than two groups by Vector Machine Support (SVM) | feature_is_ready |
| Predictive analysis | Searching for the best continuous predictive model using the Super Learner algorithm | feature_is_ready |
| Predictive analysis | Feature selection in predictive analysis | |
| Regression | Binomial logistic model for categorical variable with two possible values | feature_is_ready |
| Regression | Calculation of a Gaussian model for the continuous case | feature_is_ready |
| Regression | Logistic multinomial model for regression on categorical variable with more than two different values | feature_is_ready |
| Sample selection | Categorical filters by drop-down menu | feature_is_ready |
| Sample selection | Chain of filters on the sample | feature_is_ready |
| Sample selection | Flowchart of inclusion | feature_is_ready |
| Sample selection | Selection of variables of interest and sample size with the mouse | feature_is_ready |
| Sample selection | Simple semantic filters | feature_is_ready |
| Sample selection | Advanced semantic filters | Feature is on the roadmap |
| Sample transformation | Automatic transformation of the sample after selection | feature_is_ready |
| Sample transformation | Manual transformation of the sample after selection | feature_is_ready |
| Sample transformation | Automatic tranformation of the sample before analysis | feature_is_ready |
| Semantic | Support for unstructured data (Word, pdf, text) | feature_is_ready |
| Semantic | Creation of semantic variables based on the document-term matrix (DTM) | feature_is_ready |
| Semantic | Simple semantic filters | feature_is_ready |
| Semantic | Advanced semantic filters | Feature is on the roadmap |
| System dynamics | Search for the best system of differential equations | feature_is_ready |
