Adaptive Mixture of Student-t Distributions
c(person("David", "Ardia", role = c("aut", "cre"),
email = "david.ardia.ch@gmail.com"),
person("Lennart", "Hoogerheide", role = "ctb"),
person("Herman", "van Dijk", role = "ctb"))
David Ardia [aut, cre],
Lennart Hoogerheide [ctb],
Herman van Dijk [ctb]
Provides functions to perform the fitting of an adaptive mixture
of Student-t distributions to a target density through its kernel function as described in
Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The
mixture approximation can then be used as the importance density in importance
sampling or as the candidate density in the Metropolis-Hastings algorithm to
obtain quantities of interest for the target density itself.

c(person("David", "Ardia", role = c("aut", "cre", "cph"),
email = "david.ardia.ch@gmail.com", comment = c(ORCID = "0000-0003-2823-782X")),
person("Lennart", "Hoogerheide", role = "ctb"),
person("Herman", "van Dijk", role = "ctb"))
David Ardia [aut, cre, cph] (<https://orcid.org/0000-0003-2823-782X>),
Lennart Hoogerheide [ctb],
Herman van Dijk [ctb]

Adaptive Sampling for Positive Unlabeled and Label Noise
Learning
Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) <doi:10.1109/TCYB.2018.2816984>.

A class of adaptive elastic net methods for censored data
The package provides new approaches to variable selection for censored data, based on AFT models optimized using regularized weighted least squares. Namely, a weighted elastic net, an adaptive elastic net, and two of their extensions by adding censoring observations as constraints into their model optimization frameworks.

A Class of Adaptive Elastic-net Methods for Censored Data
imputeYn, glmnet, lars, quadprog, R (>= 3.0.2)
Provides new approaches to variable selection for censored data including its high-dimensionality, based on AFT models optimized using regularized weighted least squares. Approaches namely, a weighted elastic net, an adaptive elastic net, and two of their extensions by adding censoring observations as constraints into their model optimization frameworks are provided with both simulated and real (MCL) data examples.