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List environments are environments that have list-like properties. For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.
A scripting and command-line front-end is provided by 'r' (aka 'littler') as a lightweight binary wrapper around the GNU R language and environment for statistical computing and graphics. While R can be used in batch mode, the r binary adds full support for both 'shebang'-style scripting (i.e. using a hash-mark-exclamation-path expression as the first line in scripts) as well as command-line use in standard Unix pipelines. In other words, r provides the R language without the environment.
Computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis
A collection of tests, data sets and examples for diagnostic checking in linear regression models in R.
Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The 'lubridate' package has a consistent and memorable syntax that makes working with dates easy and fun. Parts of the 'CCTZ' source code, released under the Apache 2.0 License, are included in this package. See <https://github.com/google/cctz> for more details.
Analysis of N-dye Micro Array experiment using mixed model effect Containing analysis of variance, permutation and bootstrap, cluster and consensus tree
Provides R bindings to the Sundown Markdown rendering library. Markdown is a plain-text formatting syntax that can be converted to 'XHTML' or other formats.
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems.
A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.