Dynamic Modeling and Machine Learning Environment3 days ago
Introduction | Installation | constrainedforecast: Constrained Forecast of One-sided Integer Response Model | Example | data_transform: Standardize data.frame for comparable Machine Learning prediction and visualization | Examples | View the data without transformation | Transformation by min-max method | log transformation | Mean-SD transformation | DynamicForecast: Dynamic Forecast of Five Models and their Ensembles | Twenty eight points less than full length of data | Fourteen points less than full length of data | formattedcut: Convert continuous vector variable to formatted factors | garrett_ranking: Garrett Ranking of Categorical Data | Ranking is supplied | Ranking not supplied | Rank subset of the data | gender: Create Gender Variable | Linearsystems: Linear Model and various Transformations for Efficiency | Estimation Without test data, 14 models | Estimation Without test data, polynomial models | Estimation With test data, linear models | Estimation With test data, power models | Estimation With test data, root models | Estimation Without test data, inverse models | mdi: Sequential Computation of Dynamic Multidimensional Indices (MDI) | With three dimensions and factor | With three dimensions, no factor | With four dimensions and factor | With five dimensions and factor | With five dimensions, no plot | With five dimensions, no factor, no plot | With six dimensions and factor | With seven dimensions and factor | With eight dimensions and factor | With nine dimensions and factor | MLMetrics: Collection of Machine Learning Model Metrics for Easy Reference | model_factors: Latent Factors Recovery from Variables Loadings | Percent: Attach Per Cent Sign to Data | A vector data | Data frame | quicksummary: Quick Formatted Summary of Machine Learning Data | Likert-type data | Continuous data | treatment_model: Enhanced Estimation of Treatment Effects of Binary Data from Randomized Experiments | index_construction: Index Construction for estimation of Exposure or Sensitivity | relative_likert: Convert Likert Data to Relative Scores and knowledge-based Adaptive Capacity | odds_summary: Odds-Based Measures for Binary and Categorical Models
