Mine metrics on common places on the web through the power of their APIs (application programming interfaces). 
    It also helps make the data in a format that is easily used for a dashboard or other purposes.
    There is an associated dashboard template and tutorials that are underdevelopment that help you  
    fully utilize 'metricminer'.

An implementation of evaluation metrics in R that are commonly
             used in supervised machine learning. It implements metrics for
             regression, time series, binary classification, classification,
             and information retrieval problems. It has zero dependencies and
             a consistent, simple interface for all functions.

Provides weighted versions of several metrics and performance
    measures used in machine learning, including average unit deviances of
    the Bernoulli, Tweedie, Poisson, and Gamma distributions, see
    Jorgensen B. (1997, ISBN: 978-0412997112).  The package also contains
    a weighted version of generalized R-squared, see e.g. Cohen, J. et al.
    (2002, ISBN: 978-0805822236).  Furthermore, 'dplyr' chains are
    supported.

Calculate different metrics based on aquatic
    macroinvertebrate density data (individuals per square meter) to
    assess water quality (Prat N et al. 2009).

The Washington Metropolitan Area Transit Authority is a
    government agency operating light rail and passenger buses in the
    Washington D.C. area. With a free developer account, access their
    'Metro Transparent Data Sets API' <https://developer.wmata.com/> to
    return data frames of transit data for easy analysis.

Provides classes and calculation and plotting functions 
 for metrology applications, including measurement uncertainty estimation
 and inter-laboratory metrology comparison studies.

Learning and using the Metropolis algorithm for
    Bayesian fitting of a generalized linear model. The package vignette
    includes examples of hand-coding a logistic model using several
    variants of the Metropolis algorithm. The package also contains R
    functions for simulating posterior distributions of Bayesian
    generalized linear model parameters using guided, adaptive,
    guided-adaptive and random walk Metropolis algorithms. The random walk
    Metropolis algorithm was originally described in Metropolis et al
    (1953); <doi:10.1063/1.1699114>.