This package provides linear models based
on Theil-Sen single median and Siegel repeated
medians. They are very robust (29 or 50 percent breakdown
point, respectively), and if no outliers are present,
the estimators are very similar to OLS.

This package provides linear models based on Theil-Sen
single median and Siegel repeated medians. They are very robust
(29 or 50 percent breakdown point, respectively), and if no
outliers are present, the estimators are very similar to OLS.

Provides linear models based on Theil-Sen
single median and Siegel repeated medians. They are very robust
(29 or 50 percent breakdown point, respectively), and if no
outliers are present, the estimators are very similar to OLS.

Model Based Multifactor Dimensionality Reduction
Victor Urrea, Malu Calle, Kristel Van Steen, Nuria Malats
Victor Urrea <victor.urrea@uvic.cat> and Malu Calle
<malu.calle@uvic.cat>
Model Based Multifactor Dimension Reduction proposed by
Calle et al. (2008) as a dimension reduction method for
exploring gene-gene interactions.
This work was supported by the Ministerio de Educacion
y Ciencia (Spain) (Grant: TM2008-06747-C02-02)

This work was supported by the Ministerio de Educacion
y Ciencia (Spain) (Grant: MTM2008-06747-C02-02)

Evaluate whether a microbiome sample is a mixture of two
samples, by fitting a model for the number of read counts as a
function of single nucleotide polymorphism (SNP) allele and the
genotypes of two potential source samples.
Lobo et al. (2019) <doi:10.1101/529040>.

Functional gradient descent algorithms
(boosting) for optimizing general loss functions utilizing
componentwise least squares, either of parametric linear form or
smoothing splines, or regression trees as base learners for fitting
generalized linear, additive and interaction models to potentially
high-dimensional data.

R (>= 2.3.0), methods, modeltools, party (>= 0.8-6)

Gradient Boosting for Fitting Generalized Linear, Additive
and Interaction Models
Functional gradient descent algorithms for optimizing
arbitrary loss functions utilizing componentwise
least squares, either linear models or smoothing splines, and
regression trees as base learners.