Publish Package Manifests to GitHub Gists (switchrGist)
Provides a simple plugin to the switchr framework which allows users to publish manifests of packages – or of specific versions thereof – as single-file GitHub repositories (Gists). These manifest files can then be used as remote seeds (see switchr documentation) when creating new package libraries.

Anomalous time series package for R (anomalous)
It is becoming increasingly common for organizations to collect very large amounts of data over time, and to need to detect unusual or anomalous time series. For example, Yahoo has banks of mail servers that are monitored over time. Many measurements on server performance are collected every hour for each of thousands of servers. A common use-case is to identify servers that are behaving unusually. Methods in this package compute a vector of features on each time series, measuring characteristics of the series. For example, the features may include lag correlation, strength of seasonality, spectral entropy, etc. Then a robust principal component decomposition is used on the features, and various bivariate outlier detection methods are applied to the first two principal components. This enables the most unusual series, based on their feature vectors, to be identified. The bivariate outlier detection methods used are based on highest density regions and alpha-hulls. For demo purposes, this package contains both synthetic and real data from Yahoo.

Augments the Use of ‘Asreml’ in Fitting Mixed Models (asremlPlus)
Provides functions that assist in automating the testing of terms in mixed models when ‘asreml’ is used to fit the models. The package ‘asreml’ is marketed by ‘VSNi’ ( ) as ‘asreml-R’ and provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. The content falls into the following natural groupings: (i) Data, (ii) Object manipulation functions, (iii) Model modification functions, (iv) Model testing functions, (v) Model diagnostics functions, (vi) Prediction production and presentation functions, (vii) Response transformation functions, and (viii) Miscellaneous functions. A history of the fitting of a sequence of models is kept in a data frame. Procedures are available for choosing models that conform to the hierarchy or marginality principle and for displaying predictions for significant terms in tables and graphs.