Provides an interface to 'D4Science' 'StorageHub' API (<https://dev.d4science.org/>). Allows to get user profile, and perform 
 actions over the 'StorageHub' (workspace) including creation of folders, files management (upload/update/deletion/sharing), and listing of 
 stored resources.

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 Good Documentation Practice (GDP), 
  with ALCOA+ principles as proposed by the U.S. FDA, and 
  with FAIR principles as discussed by Jacobsen et al. (2017) <doi:10.1162/dint_r_00024>.

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>.