Ingo Steinwart <ingo.steinwart@mathematik.uni-stuttgart.de>,
Philipp Thomann <philipp.thomann@mathematik.uni-stuttgart.de>,
Mohammad Farooq <mohammad.farooq@mathematik.uni-stuttgart.de>
Support vector machines (SVMs) and related kernel-based learning
algorithms are a well-known class of machine learning algorithms, for non-
parametric classification and regression. liquidSVM is an implementation of
SVMs whose key features are: fully integrated hyper-parameter selection, extreme
speed on both small and large data sets, inclusion of a variety of different
classification and regression scenarios, and full flexibility for experts.
knitr, rmarkdown, e1071, deldir, testthat

Ingo Steinwart, Philipp Thomann, Mohammad Farooq
Support vector machines (SVMs) and related kernel-based learning
algorithms are a well-known class of machine learning algorithms, for
non-parametric classification and regression.
liquidSVM is an implementation of SVMs whose key features are:
fully integrated hyper-parameter selection,
extreme speed on both small and large data sets,
full flexibility for experts, and
inclusion of a variety of different learning scenarios:
multi-class classification, ROC, and Neyman-Pearson learning, and
least-squares, quantile, and expectile regression.

Package for Bayesian linear regression in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables), time evolution of slopes, normalization and scatters, Malmquist and Eddington bias, and break of linearity. The posterior distribution of the regression parameters  is sampled with a Gibbs method exploiting the JAGS library.
JAGS (>= 3.0.0) (see
http://mcmc-jags.sourceforge.net)

Performs Bayesian linear regression in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables), time evolution of slopes, normalization and scatters, Malmquist and Eddington bias, and break of linearity. The posterior distribution of the regression parameters  is sampled with a Gibbs method exploiting the JAGS library.