Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.

A new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.

Holds functions developed by the University of Ottawa's SAiVE
    (Spatio-temporal Analysis of isotope Variations in the Environment)
    research group with the intention of facilitating the re-use of code,
    foster good code writing practices, and to allow others to benefit
    from the work done by the SAiVE group. Contributions are welcome via
    the 'GitHub' repository <https://github.com/UO-SAiVE/SAiVE> by group members as well as non-members.

Surveys to collect employment data so as to obtain data estimates on the number of employed people, the number of unemployed, and other employment indicators.

Extends the functionality of R serialization by augmenting the
    built-in reference hook system. This enhanced implementation allows optimal,
    one-pass integrated serialization that combines R serialization with
    third-party serialization methods. Facilitates the serialization of even
    complex R objects, which contain non-system reference objects, such as those
    accessed via external pointers, for use in parallel and distributed
    computing.