Based on the manuscript entitled “Regression and Classification with I-priors” by Wicher Bergsma (2018, arXiv:1707.00274). In a general regression setting, priors can be assigned to the regression function in a vector space framework, and the posterior estimate of the regression function obtained. An I-prior is defined as Gaussian with some prior mean (usually zero) and covariance kernel proportional to the Fisher information for the regression function.

This package performs regression modelling using I-priors in R. It is
intuitively designed to be similar to `lm`

, making use of
familiar syntactical conventions and S3 methods, with both formula and
non-formula based input. The package estimates these parameters using
direct log-likelihood maximisation, the expectation-maximisation (EM)
algorithm, or a combination of both. While the main interest of I-prior
modelling is prediction, inference is also possible, e.g. via
log-likelihood ratio tests.

For installation instructions and some examples of I-prior modelling, continue reading below. The package is documented with help files, and the vignette provides an introduction to the concept of I-priors and also to using the package.

Install R/iprior either by downloading the latest CRAN release

```
install.packages("iprior")
library(iprior)
```

or the developmental version from this GitHub repository. R/iprior makes use of several C++ code, so as a prerequisite, you must have a working C++ compiler. To get it:

- On Windows, install Rtools.
- On Mac, install Xcode from the app store.
- On Linux,
`sudo apt-get install r-base-dev`

or similar.

The easiest way to then install from this repo is by using the devtools package. Install this first.

`install.packages("devtools")`

Then, run the following code to install and attach the
`iprior`

package.

```
::install_github("haziqj/iprior", build_vignettes = TRUE)
devtoolslibrary(iprior)
```

To fit an I-prior model to `mod`

regressing `y`

against `x`

, where these are contained in the data frame
`dat`

, the following syntax are equivalent.

```
<- iprior(y ~ x, data = dat) # formula based input
mod <- iprior(y = dat$y, x = dat$x) # non-formula based input mod
```

The call to `iprior()`

can be accompanied by several other
model specification arguments, including choosing the RKHS,
hyperparameters to estimate, estimation method, and others. Control
options for the estimation method of choice is done through the option
`control = list()`

. Find the full list of options by typing
`?iprior`

in R.

View the package vignette by typing
`browseVignettes("iprior")`

in R or visiting this link. This package is
part of the PhD project entitled “Regression Modelling using priors
depending on Fisher information covariance kernels (I-priors)” by Haziq
Jamil [link].