A collection of functions which aim to assist common computational workflow for analysis of matabolomic data..

Define a spatial 'Area of Interest' (AOI) around a constructed dam using hydrology data.
  Dams have environmental and social impacts, both positive and negative.
  Current analyses of dams have no consistent way to specify at what spatial extent we should evaluate these impacts.
  'damAOI' implements methods to adjust reservoir polygons to match satellite-observed surface water areas, plot upstream and downstream rivers using elevation data and accumulated river flow, and draw buffers clipped by river basins around reservoirs and relevant rivers. 
  This helps to consistently determine the areas which could be impacted by dam construction, facilitating comparative analysis and informed infrastructure investments.

Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.

Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.

Loads behavioural data from the widely used Drosophila Activity Monitor System (DAMS, TriKinetics <https://trikinetics.com/>) into the rethomics framework.

The single largest source of dams in the United States is the
    National Inventory of Dams (NID) <http://nid.usace.army.mil> from the US
    Army Corps of Engineers. Entire data from the NID cannot be obtained all at
    once and NID's website limits extraction of more than a couple of thousand
    records at a time. Moreover, selected data from the NID's user interface
    cannot not be saved to a file. In order to make the analysis of this data
    easier, all the data from NID was extracted manually. Subsequently, the raw
    data was checked for potential errors and cleaned. This package provides
    sample cleaned data from the NID and provides functionality to access the
    entire cleaned NID data.

Contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.