Title: Implements Queue, PriorityQueue and Stack Classes
Description: Provides three classes: Queue, PriorityQueue and Stack. Queue is just a
"plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.

Description: 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.
Suggests: knitr, rmarkdown, e1071, deldir, testthat

Suggests: knitr, rmarkdown, deldir, testthat

Copyright: Ingo Steinwart, Philipp Thomann, Mohammad Farooq
algorithms are a well-known class of machine learning algorithms, for
liquidSVM is an implementation of SVMs whose key features are:
extreme speed on both small and large data sets,
inclusion of a variety of different learning scenarios:
multi-class classification, ROC, and Neyman-Pearson learning, and
least-squares, quantile, and expectile regression.

Packaged: 2019-01-10 19:28:51 UTC; philippthomann

Packaged: 2019-09-03 12:11:38 UTC; philippthomann

Packaged: 2019-09-14 10:19:42 UTC; philippthomann

Description: 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.
SystemRequirements: JAGS (>= 3.0.0) (see
Packaged: 2015-09-16 19:04:34 UTC; maurosereno

Description: 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.
Packaged: 2016-03-20 20:06:17 UTC; maurosereno