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Breiman and Cutler's Random Forests for Classification and Regression
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001)
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
Regularized Random Forest
Feature Selection with Regularized Random Forest. This
package is based on the 'randomForest' package by Andy Liaw.
The key difference is the RRF() function that builds a
regularized random forest. Fortran original by Leo Breiman
and Adele Cutler, R port by Andy Liaw and Matthew Wiener,
Regularized random forest for classification by Houtao Deng,
Regularized random forest for regression by Xin Guan.
Reference: Houtao Deng (2013)
Nonparametric Missing Value Imputation using Random Forest
The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.
Distributional Random Forests
An implementation of distributional random forests as introduced in Cevid & Michel & Meinshausen & Buhlmann (2020)
Adversarial Random Forests
Adversarial random forests (ARFs) recursively partition data into
fully factorized leaves, where features are jointly independent. The
procedure is iterative, with alternating rounds of generation and
discrimination. Data becomes increasingly realistic at each round, until
original and synthetic samples can no longer be reliably distinguished.
This is useful for several unsupervised learning tasks, such as density
estimation and data synthesis. Methods for both are implemented in this
package. ARFs naturally handle unstructured data with mixed continuous and
categorical covariates. They inherit many of the benefits of random forests,
including speed, flexibility, and solid performance with default parameters.
For details, see Watson et al. (2022)
Ordered Random Forests
An implementation of the Ordered Forest estimator as developed
in Lechner & Okasa (2019)
Generalized Random Forests
Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.
Modified Ordered Random Forest
Nonparametric estimator of the ordered choice model using random forests. The estimator modifies a standard random forest splitting criterion to build a collection of forests, each estimating the conditional probability of a single class. The package also implements a nonparametric estimator of the covariates’ marginal effects.
Random Forests for Longitudinal Data
Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020)