Work with GitHub Gists (gistr)
Work with GitHub gists from R (e.g., http://en.wikipedia.org/wiki/GitHub#Gist, https://help.github.com/articles/about-gists/). A gist is simply one or more files with code/text/images/etc. gistr allows the user to create new gists, update gists with new files, rename files, delete files, get and delete gists, star and un-star gists, fork gists, open a gist in your default browser, get embed code for a gist, list gist commits, and get rate limit information when authenticated. Some requests require authentication and some do not. Gists website: https://gist.github.com/.

Data and Functions for USA State and County Unemployment Data (rUnemploymentData)
Contains data and visualization functions for USA unemployment data. Data comes from the US Bureau of Labor Statistics (BLS). State data is in ?df_state_unemployment and covers 2000-2013. County data is in ?df_county_unemployment and covers 1990-2013. Choropleth maps of the data can be generated with ?state_unemployment_choropleth and ?county_unemployment_choropleth respectively.

Sparse Distance Weighted Discrimination (sdwd)
Solves the solution paths of the sparse distance weighted discrimination (DWD) with the L1, the elastic-net, and the adaptive elastic-net penalties.

Read-Write Support for NumPy Files via Rcpp (RcppCNPy)
The cnpy library written by Carl Rogers provides read and write facilities for files created with (or for) the NumPy extension for Python. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with -std=c++11 which is the default starting with release 0.2.3 following the release of R 3.1.0.

Utilities for Data Manipulation, Disk Caching, Testing (jwutil)
This is a set of simple utilities for various data manipulation and caching tasks. The goal is to use base tools well, without bringing in any dependencies. Main areas of interest are data frame manipulation, such as converting factors in multiple binary indicator columns, and disk caching of data frames (which is optionally done by date range). There are testing functions which provide testthat expectations to permute arguments to function calls. There are functions and data to test extreme numbers, dates, and bad input of various kinds which should allow testing failure and corner cases. The test suite has many examples of usage.

Anomaly Detection with R (AnomalyDetection)
AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. The AnomalyDetection package can be used in wide variety of contexts. For example, detecting anomalies in system metrics after a new software release, user engagement post an A/B test, or for problems in econometrics, financial engineering, political and social sciences.

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