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Found 3 papers, 3 papers with code
Covariance regression with random forests
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine.
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests
The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by Roy and Larocque (2020).
Conditional canonical correlation estimation based on covariates with random forests
We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates.
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