How to download cran packages from archive

For more information about the Bioconductor installation process refer to the official Bioconductor R packages page. Updating R packages can be tedious if you have to reinstall the packages over and over again when some has a newer version. You can see the full list of your R packages that are not up-to-date with the old.

You can update some of them with the install. Once installed, you can get a list of all the functions in the package. Recall you can access this documentation in HTML format with the help function. You can also use the lsf. In classic R you will have to press the tab button to show the functions on the screen, although it should be noted that if the package contains many functions not all will be shown, as is the case with the ggplot2 package:.

Sometimes it can be interesting to inspect the code of any function. For that purpose, you have several options:. In order to avoid this, you can use the require function. Note that the main difference between require and library is that the first one returns a boolean and the second one returns an error if the package is not installed. The require function is designed to be used inside other functions. If you encountered this error, you might be using different versions of R in the same computer.

The solutions are:. If nothing works, try to close and open R again or try in another computer to verify if the problem persists. Home » Introduction » How to install packages in R? How to install packages in R? Introduction to R. CRAN is the official R repository.

All packages have been tested automatically and meet the CRAN policy. Accelerated line search algorithm for simultaneous orthogonal transformation of several positive definite symmetric matrices to nearly diagonal form.

This package includes pricing function for selected American call options with underlying assets that generate payouts. Animal track reconstruction for high frequency 2-dimensional 2D or 3-dimensional 3D movement data. Bayesian bandwidth estimation and semi-metric selection for the functional kernel regression with unknown error density.

Functions to fit cell volume distributions and thereby estimate cell growth rates and division times. Calculate AUC-type measure when gold standard is continuous and the corresponding optimal linear combination of variables with respect to it.

Plot and add custom coloring to Venn diagrams for 2-dimensional, 3-dimensional and 4-dimensional data.

It takes a vector of names and a destination library, downloads the packages from the repositories and installs them. If the library is omitted it defaults to the first directory in. If lib is omitted or is of length one and is not a group writable directory, in interactive use the code offers to create a personal library tree the first element of Sys. For installs from a repository an attempt is made to install the packages in an order that respects their dependencies.

You are advised to run update. R packages are primarily distributed as source packages, but binary packages a packaging up of the installed package are also supported, and the type most commonly used on Windows and by the CRAN builds for macOS. This function can install either type, either by downloading a file from a repository or from a local file.

Possible values of type are currently "source" , "mac. For a binary install from a repository, the function checks for the availability of a source package on the same repository, and reports if the source package has a later version, or is available but no binary version is. This check can be suppressed by using.

The action if there are source packages which are preferred but may contain code which needs to be compiled is controlled by getOption "install.

It is safe to always set the latter when installing from a repository or tarballs, although it will be a little slower. When installing a package on Windows, install. In some circumstances e. This has two purposes: it prevents any other process installing into that library concurrently, and is used to store any previous version of the package to restore on error.

A finer-grained locking is provided by the option --pkglock which creates a separate lock for each package: this allows enough freedom for parallel installation. Finally locking and restoration on error can be suppressed by --no-lock.

For a macOS binary install, no locking is done by default. For Windows binary install, per-directory locking is used by default lock defaults to the value of getOption "install.

If the value is "pkglock" per-package locking will be used. Note that it is possible for the package installation to fail so badly that the lock directory is not removed: this inhibits any further installs to the library directory or for --pkglock , of the package until the lock directory is removed manually. It makes use of a parallel make , so the make specified default make when R was built must be capable of supporting make -j n : GNU make, dmake and pmake do, but Solaris make and older FreeBSD make do not: if necessary environment variable MAKE can be set for the current session to select a suitable make.

For non-parallel installs this is implemented via the timeout argument of system2 : for parallel installs via the OS's timeout command. If no such command is available the timeout request is ignored, with a warning. For parallel installs a Error message from make indicates that timeout occurred. Timeouts during installation might leave lock directories behind and not restore previous versions.