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,

An unknown prior density $g(\theta)$ has yielded (unobservable) $\Theta_1, \Theta_2,\ldots,\Theta_N$, and each $\Theta_i$ produces
an observation $X_i$ from an exponential family. `deconvolveR`

is an R package for estimating prior distribution $g(\theta)$ from the data
using Empirical Bayes deconvolution.

The current package is still under construction but will soon appear on CRAN along with a manuscript. Meanwhile, you can reproduce many examples by installing the package in R thus:

```
devtools::install_github("bnaras/deconvolveR")
library(deconvolveR)
vignette("deconvolution")
```

- Major change to
`deconv`

. Now,`ignoreZero = TRUE`

by default makes the thinning correction. Please note this change which is also discussed in the vignette.

- Initial release