Fit Regularized Nonparametric Regression Models Using COSSO
Penalty.
COSSO is the short term for COmponent Selection and
Smoothing Operator. The new regularization method automatically
estimates and selects important function components by a
soft-thresholding penalty in the context of smoothing spline
ANOVA models.

Hao Helen Zhang <hzhang@math.arizona.edu> and Chen-Yen Lin
<clin5@ncsu.edu>
COSSO is a new regularization method that automatically
estimates and selects important function components by a
soft-thresholding penalty in the context of smoothing spline
ANOVA models. Implemented models include mean regression,
quantile regression, logistic regression and the Cox regression
models.

Time series costationarity determination and tests of
stationarity.
Contains functions that can determine whether a time
series is second-order stationary or not (and hence evidence
for locally stationarity). Given two non-stationary series
(i.e. locally stationary series) this package can then discover
time-varying linear combinations that are second-order
stationary.

Contains functions that can determine whether a time
series is second-order stationary or not (and hence evidence
for locally stationarity). Given two non-stationary series
(i.e.  locally stationary series) this package can then
discover time-varying linear combinations that are second-order
stationary.

Contains functions that can determine whether a time series
is second-order stationary or not (and hence evidence for
locally stationarity). Given two non-stationary series (i.e.
locally stationary series) this package can then discover
time-varying linear combinations that are second-order stationary.

c(person("Guy", "Nason", role=c("aut", "cre"),
email="g.p.nason@bristol.ac.uk"),
person("Alessandro", "Cardinali", role=c("aut", "ctb")))
Guy Nason [aut, cre],
Alessandro Cardinali [aut, ctb]

Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation.
Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest.
Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers
that don't accept observation weights.