A simple interface to the instance metadata for a virtual machine running in Microsoft's 'Azure' cloud. This provides information about the VM's configuration, such as its processors, memory, networking, storage, and so on. Part of the 'AzureR' family of packages.

Fast, dependency free, and vectorized base32 encoding and
    decoding. 'b32' supports the Crockford, Z, RFC 4648 lower, hex, and
    lower hex alphabets.

Provides a fast, lightweight, and vectorized base 64 engine
    to encode and decode character and raw vectors as well as files stored
    on disk. Common base 64 alphabets are supported out of the box
    including the standard, URL-safe, bcrypt, crypt, 'BinHex', and
    IMAP-modified UTF-7 alphabets. Custom engines can be created to
    support unique base 64 encoding and decoding needs.

Approximate Bayesian inference and Monte Carlo validation for
    small-N repeated-measures designs with two time points and two raters.
    The package is intended for applications in which sample size is limited
    and the observed outcome may be affected by rater-specific bias.
    User-supplied data are standardised into a common long-format structure.
    Pre-post effects are analysed using difference scores in a linear model
    with a rater indicator as covariate. Posterior summaries for the
    regression coefficients are obtained from a large-sample normal
    approximation centred at the least-squares estimate with plug-in
    covariance under a flat improper prior. Evidence for a non-zero
    pre-post effect, adjusted for rater differences, is summarised using a
    BIC-based approximation to the Bayes factor for comparison between
    models with and without the pre-post effect. Monte Carlo validation uses
    design quantities estimated from the observed data, including sample
    size, mean pre-post change, and second-rater additive discrepancy, and
    summarises inferential performance in terms of bias, root mean squared
    error, credible interval coverage, posterior tail probabilities, and
    mean Bayes factor values. For background on the BIC approximation and
    Bayes factors, see Schwarz (1978)
    <doi:10.1214/aos/1176344136> and Kass and Raftery (1995)
    <doi:10.1080/01621459.1995.10476572>.