B. Hofner, A. Mayr, M. Schmid (2016). gamboostLSS: An R Package for
Model Building and Variable Selection in the GAMLSS Framework.
Journal of Statistical Software, 74(1), 1-31.

Available as `vignette("gamboostLSS_Tutorial")`

.

Mayr, A., Fenske, N., Hofner, B., Kneib, T. and Schmid, M. (2012):
Generalized additive models for location, scale and shape for
high-dimensional data - a flexible approach based on boosting. Journal
of the Royal Statistical Society, Series C (Applied Statistics) 61(3):
403-427.

M. Schmid, S. Potapov, A. Pfahlberg, and T. Hothorn. Estimation and
regularization techniques for regression models with multidimensional
prediction functions. Statistics and Computing, 20(2):139-150, 2010.

Rigby, R. A. and D. M. Stasinopoulos (2005). Generalized additive models
for location, scale and shape (with discussion). Journal of the Royal
Statistical Society, Series C (Applied Statistics), 54, 507-554.

Stasinopoulos, D. M. and R. A. Rigby (2007). Generalized additive models
for location scale and shape (GAMLSS) in R. Journal of Statistical
Software 23(7).

Buehlmann, P. and Hothorn, T. (2007). Boosting algorithms: Regularization,
prediction and model fitting. Statistical Science, 22(4), 477--505.

Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M. and Hofner, B. (2010).
Model-based boosting 2.0. Journal of Machine Learning Research 11(Aug),
2109-2113.

Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M. and Hofner, B. (2015).
mboost: Model-based boosting. R package version 2.4-2.
https://CRAN.R-project.org/package=mboost

Thomas, J., Mayr, A., Bischl, B., Schmid, M., Smith, A., and Hofner, B. (2018),
Gradient boosting for distributional regression - faster tuning and improved
variable selection via noncyclical updates.
*Statistics and Computing*. 28: 673-687.
tools:::Rd_expr_doi("10.1007/s11222-017-9754-6")

(Preliminary version: https://arxiv.org/abs/1611.10171).