A collection of functions to deal
with nonparametric measurement error problems using
deconvolution kernel methods. We focus two measurement error
models in the package: (1) an additive measurement error model,
where the goal is to estimate the density or distribution
function from contaminated data; (2) nonparametric regression
model with errors-in-variables. The R functions allow the
measurement errors to be either homoscedastic or
heteroscedastic. To make the deconvolution estimators
computationally more efficient in R, we adapt the "Fast Fourier
Transform" (FFT) algorithm for density estimation with
error-free data to the deconvolution kernel estimation. Several
methods for the selection of the data-driven smoothing
parameter are also provided in the package. See details in:
Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
measurement error models: The R package decon. Journal of
Statistical Software, 39(10), 1-24.

Identifies Signatures Present in a Tumor Sample
Takes sample information in the form of the fraction of mutations
in each of 96 trinucleotide contexts and identifies the weighted combination
of published signatures that, when summed, most closely reconstructs the
mutational profile.
reshape2, BSgenome, BSgenome.Hsapiens.UCSC.hg19, grDevices,
graphics, utils

reshape2, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb,
grDevices, graphics, utils

c(person("Bradley", "Efron", role=c("aut"),
email = "brad@stat.stanford.edu"),
person("Balasubramanian", "Narasimhan", role=c("aut", "cre"),
email = "naras@stat.Stanford.EDU"))
Empirical Bayes methods for learning prior distributions from data.
An unknown prior distribution (g) has yielded (unobservable) parameters, each of
which produces a data point from a parametric exponential family (f). The goal
is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
Bayes methods.

Empirical Bayes methods for learning prior distributions from data.
An unknown prior distribution (g) has yielded (unobservable) parameters, each of
which produces a data point from a parametric exponential family (f). The goal
is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
Bayes methods. Details and examples are in the paper by Narasimhan and Efron
(2020, <doi:10.18637/jss.v094.i11>).