Quantile Regression Forests is a tree-based ensemble method
for estimation of conditional quantiles. It is particularly well suited for
high-dimensional data. Predictor variables of mixed classes can be
handled. The package is dependent on the package randomForests,
written by Andy Liaw.

Quantile Regression Forests is a tree-based ensemble
method for estimation of conditional quantiles. It is
particularly well suited for high-dimensional data. Predictor
variables of mixed classes can be handled. The package is
dependent on the package randomForests, written by Andy Liaw.

Quantile Regression Forests is a tree-based ensemble
method for estimation of conditional quantiles. It is
particularly well suited for high-dimensional data. Predictor
variables of mixed classes can be handled. The package is
dependent on the package 'randomForest', written by Andy Liaw.

Fits noncrossing regression quantiles as a function of
linear covariates and a smooth term via B-spline with quadratic
penalty.

c(person(given = c("Vito","M.","R."), family = "Muggeo", role =
c("aut", "cre"), email = "vito.muggeo@unipa.it"))
Fits non-crossing regression quantiles as a function of linear covariates and a smooth term via B-spline with quadratic penalty.

Fits non-crossing regression quantiles as a function of linear covariates and a smooth terms via B-splines with quadratic penalties.

Fits non-crossing regression quantiles as a function of linear covariates and a smooth terms via B-splines with difference penalties.

Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms via B-splines with L1-norm difference penalties. See Muggeo, Sciandra, Tomasello and Calvo (2013) <doi:10.1007/s10651-012-0232-1> and <doi:10.13140/RG.2.2.12924.85122> per same example code.

Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms via B-splines with L1-norm difference penalties. See Muggeo, Sciandra, Tomasello and Calvo (2013) <doi:10.1007/s10651-012-0232-1> and <doi:10.13140/RG.2.2.12924.85122> for some code example.