All functions |
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Industry-Factor Returns Data Set |
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Classification on tidy data |
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Extract coefficients from a |
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Adaptive Lasso regression or classification for |
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ANOVA for |
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Bayesian generalized linear regression for |
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Bayesian Lasso regression for |
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Bayesian model averaging for |
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Gradient boosting regression for |
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Bayesian ridge regression for |
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Pearson's Chi-squared test for |
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Pearson's correlation for |
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ElasticNet regression or classification for |
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Genetic algorithm with linear regression fitness evaluator for |
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General-to-specific regression for |
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Generalized linear regression for |
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Generalized linear mixed-effects model for |
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Grouped Lasso regression and classification for |
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Hierarchical feature regression for |
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Lasso regression and classification for |
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Linear regression for |
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Minimum redundancy, maximum relevance feature selection for |
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Markov-Switching Regression for |
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Neural Network regression for |
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Principal Components Regression for |
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Partial Least Squares Regression for |
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Quantile regression for |
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Quantile regression forest for |
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ReliefF and RReliefF feature selection algorithm for |
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Random Forest regression or classification for |
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Ridge regression and classification for |
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Robust regression for |
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Bayesian Spike and Slab regression or classification for |
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Best subset regression and classification for |
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Support vector regression or classification for |
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Bayesian Time-Varying Regression for |
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An interface for variable importance measures for a fitted tidyfit.models frames |
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An interface for variable importance measures for a fitted tidyfit.models frames |
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Obtain fitted values from models in a |
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Get a fitted model from a tidyfit.models frame |
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Get a tidyFit model from a tidyfit.models frame |
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Generic model wrapper for |
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Predict using a |
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Linear regression on tidy data |
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Obtain residuals from models in a |