c(person(given = c("Vito","M.","R."), family = "Muggeo", role =
c("aut", "cre"), email = "vito.muggeo@unipa.it", comment=c(ORCID="0000-0002-3386-4054")))
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms via B-splines with L1-norm difference penalties. Monotonicity constraints on the fitted curves are allowed. 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.

Growth Charts via Smooth Regression Quantiles with Automatic
Smoothness Estimation and Additive Terms
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms via B-splines with L1-norm difference penalties. The smoothing parameters are estimated as part of the model fitting. Monotonicity and concavity constraints on the fitted curves are allowed. 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 examples.
Smoothing parameter selection with additive terms is discussed in Muggeo and others (2020) <doi:10.1177/1471082X20929802>.

Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties.
The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity
constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1> and also <doi:10.13140/RG.2.2.12924.85122> for some code examples.

Quantile Regression Forests for 'ranger'
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.

Quantile-based Spectral Analysis of Time Series
c(person("Tobias","Kley", role = c("aut","cre"), email =
"tobias.kley@ruhr-uni-bochum.de"))
R (>= 2.12.0), quantreg, rje, tcltk, zoo
Methods to determine, smooth and plot Laplace periodograms	and rank-based Laplace periodograms for univariate time series.