The 'installr' package aims at making the process of
updating R on Windows as easy as possible, the main function
for this purpose is the updateR() function. Also, the package
contains a set of functions allowing users to install new
software (on Windows OS) using R. While for Linux users, the
installation process of new software may be just running a
short line of code, for the regular Windows user it often
includes browsing online, finding the latest version,
downloading it, running the installer, and deleting the
installation file. All of these steps are automatically done
using functions in this package.

'create.global.library.r' 'install.r' 'updateR.r'
'os.manage.r' 'zzz.r'

The 'installr' package aims at making the process of
updating R on Windows as easy as possible. Also, the package
contains a (growing) set of functions for allowing users to
install new software (on Windows OS) using R. By simply running
the installr() function, the user can easily select (via a GUI
interface) a software to install. There are also dedicated
functions for installing each software, such as: updateR(),
install.RTools(), install.pandoc(), and many more. While for
Linux users, the installation process of new software may be
just running a short line of code, for the regular Windows user
it often includes browsing online, finding the latest version,
downloading it, running the installer, and deleting the
installation file. All of these steps are automatically done
using functions in this package.

Estimate Regression Models with Instrumental Variables
A consistent API for estimating linear and generalized linear models with instrumental variables.

A Collection of Insurance Datasets Useful in Risk Classification
in Non-life Insurance.
Alicja Wolny--Dominiak and Michal Trzesiok
Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.