Tools, methods and processes for the management 
  of analysis workflows. These lightweight solutions facilitate 
  structuring R&D activities. These solutions were developed to comply with FAIR 
  principles as discussed by Jacobsen et al. (2017) <doi:10.1162/dint_r_00024>, 
  and with ALCOA+ principles as proposed by the U.S. FDA.

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.

Functions and data sets used in examples and exercises in the
        text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data
        Analysis and Graphics Using R", and in an upcoming Maindonald,
        Braun, and Andrews text that builds on this earlier text.

Data sets and functions, for the display of gene expression array (microarray) data, and for demonstrations with such data.

Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.

Data Analysis using Bootstrap-Coupled ESTimation.
  Estimation statistics is a simple framework that avoids the pitfalls of
  significance testing. It uses familiar statistical concepts: means,
  mean differences, and error bars. More importantly, it focuses on the
  effect size of one's experiment/intervention, as opposed to a false
  dichotomy engendered by P values.
  An estimation plot has two key features:
  1. It presents all datapoints as a swarmplot, which orders each point to
  display the underlying distribution.
  2. It presents the effect size as a bootstrap 95% confidence interval on a
  separate but aligned axes.
  Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105.
  <doi:10.1038/s41592-019-0470-3>.
  The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.