# G. Grothendieck

#### 15 packages on CRAN

The gsubfn function is like gsub but can take a replacement function or certain other objects instead of the replacement string. Matches and back references are input to the replacement function and replaced by the function output. gsubfn can be used to split strings based on content rather than delimiters and for quasi-perl-style string interpolation. The package also has facilities for translating formulas to functions and allowing such formulas in function calls instead of functions. This can be used with R functions such as apply, sapply, lapply, optim, integrate, xyplot, Filter and any other function that expects another function as an input argument or functions like cat or sql calls that may involve strings where substitution is desirable. There is also a facility for returning multiple objects from functions and a version of transform that allows the RHS to refer to LHS used in the same transform.

The sqldf() function is typically passed a single argument which is an SQL select statement where the table names are ordinary R data frame names. sqldf() transparently sets up a database, imports the data frames into that database, performs the SQL select or other statement and returns the result using a heuristic to determine which class to assign to each column of the returned data frame. The sqldf() or read.csv.sql() functions can also be used to read filtered files into R even if the original files are larger than R itself can handle. 'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.

Time series regression. The dyn class interfaces ts, irts(), zoo() and zooreg() time series classes to lm(), glm(), loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(), quantreg::rq(), randomForest::randomForest() and other regression functions allowing those functions to be used with time series including specifications that may contain lags, diffs and missing values.

R is great for installing software. Through the 'installr' package you can automate the updating of R (on Windows, using updateR()) and install new software. Software installation is initiated through a GUI (just run installr()), or through functions such as: install.Rtools(), install.pandoc(), install.git(), and many more. The updateR() command performs the following: finding the latest R version, downloading it, running the installer, deleting the installation file, copy and updating old packages to the new R installation.

Provides a test of replacement and extension of the optim() function to unify and streamline optimization capabilities in R for smooth, possibly box constrained functions of several or many parameters. This version has a reduced set of methods and is intended to be on CRAN.

Provides a replacement and extension of the optim() function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that function 'optimr()' was prepared to simplify the incorporation of minimization codes going forward. Also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here. This is the version for CRAN.

An object oriented system using object-based, also called prototype-based, rather than class-based object oriented ideas.

A collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in <http://pj.freefaculty.org/guides>. Includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette 'rockchalk' offers a fairly comprehensive overview. The vignette 'Rstyle' has advice about coding in R. The package title 'rockchalk' refers to our school motto, 'Rock Chalk Jayhawk, Go K.U.'.

A legacy version of 'Ryacas', an interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).

An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.