Process in-situ Gamma-Ray Spectrometry for
Luminescence Dating. This package allows to import, inspect and
correct the energy shifts of Gamma-ray spectra. It provides methods
for estimating the gamma dose rate by the use of a calibration curve
as described in Mercier and Falguères (2007).
The package only supports Canberra CNF and TKA files.

Robust and Sparse Regression via Gamma-Divergence
Robust regression via gamma-divergence with L1, elastic net and ridge.

Fit Regularization Path for Generalized Additive Models
Alexandra Chouldechova and Trevor Hastie
Using overlap grouped lasso penalties, gamsel selects whether a term in a gam is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.

Alexandra Chouldechova [aut, cre],
Trevor Hastie [aut, cre],
Vitalie Spinu [ctb]

Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.

c(person("Alexandra", "Chouldechova", role=c("aut")),
person("Trevor", "Hastie", role=c("aut", "cre"), email = "hastie@stanford.edu"),
person("Balasubramanian", "Narasimhan", role=c("ctb")),
person("Vitalie", "Spinu", role=c("ctb")))
Alexandra Chouldechova [aut],
Trevor Hastie [aut, cre],
Balasubramanian Narasimhan [ctb],
Vitalie Spinu [ctb]

Bayesian Generalized Additive Model Selection
c(person("Virginia X.", "He", role = "aut",
email = "virginia.x.he@student.uts.edu.au"),
person("Matt P.", "Wand", role = c("aut","cre"),
email = "matt.wand@uts.edu.au",
comment = c(ORCID = "0000-0003-2555-896X")))
Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2021) <arXiv:2201.00412>.
Virginia X. He [aut],
Matt P. Wand [aut, cre] (<https://orcid.org/0000-0003-2555-896X>)