Functions and utilities to perform
    Statistical Analyses in the Six Sigma way.
    Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
    you can manage several Quality Management studies:
    Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
    etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2]
    and "Quality Control with R" [ISBN 978-3-319-24046-6], 
    are also included in the package.

An opinionated interface to Amazon Web Services <https://aws.amazon.com>, 
    with functions for interacting with 'IAM' (Identity and Access Management),
    'S3' (Simple Storage Service), 'RDS' (Relational Data Service), Redshift, and Billing.
    Lower level functions ('aws_' prefix) are for do it yourself workflows, while
    higher level functions ('six_' prefix) automate common tasks.

Estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots.

Calculates and plots the SiZer map for scatterplot data.  A 
  SiZer map is a way of examining when the p-th derivative of a 
  scatterplot-smoother is significantly negative, possibly zero or 
  significantly positive across a range of smoothing bandwidths.

Provides a database-independent JDBC interface.

Collection of functions dealing with labelled data, like reading and 
    writing data between R and other statistical software packages like 'SPSS',
    'SAS' or 'Stata', and working with labelled data. This includes easy ways 
    to get, set or change value and variable label attributes, to convert 
    labelled vectors into factors or numeric (and vice versa), or to deal with 
    multiple declared missing values.

Collection of miscellaneous utility functions, supporting data
    transformation tasks like recoding, dichotomizing or grouping variables,
    setting and replacing missing values. The data transformation functions
    also support labelled data, and all integrate seamlessly into a
    'tidyverse'-workflow.