An extendable toolkit for interactive data visualization and exploration.

Data used as examples in the 'loon' package.

Provides a bridge between the 'loon' and  'ggplot2' packages.  Extends the grammar of ggplot to add clauses to create interactive 'loon' plots. Existing ggplot(s) can be turned into interactive 'loon' plots and 'loon' plots into static ggplot(s); the function 'loon.ggplot()' is the bridge from one plot structure to the other.

Package 'shiny' provides interactive web applications in R. Package 'loon' is an interactive toolkit engaged in open-ended, creative and unscripted data exploration. The 'loon.shiny' package can take 'loon' widgets and display a selfsame 'shiny' app.

Implement tour algorithms in interactive graphical system 'loon'.

Performs Levins' loop analysis of qualitatively-specified complex causal systems. Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means direct causal relationships and indirect causal effects are coded as sign only (i.e. increases, decreases, no change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as 'graphviz' (<https://graphviz.org>). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.

Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. Feedback loops are paths from a node to itself without visiting any other node twice, and they have important regulatory functions. Loops are reported with their order of participating nodes and their length, and whether the loop is a positive or a negative feedback loop. An upper limit of the number of feedback loops limits runtime (which scales with feedback loop count). Model parametrizations and values of the modelled variables are accounted for. Computation uses the characteristics of the Jacobian matrix as described e.g. in Thomas and Kaufman (2002) <doi:10.1016/s1631-0691(02)01452-x>. Input can be the Jacobian matrix of the ODE model or the ODE function definition; in the latter case, the Jacobian matrix is determined using 'numDeriv'. Graph-based algorithms from 'igraph' are employed for path detection.