data transformation step before using other methods to infer
association, function, or causality without assuming a

Description: Discretize multivariate continuous data using a grid

Title: Cluster-Preserving Multivariate Joint Grid Discretization
the original data (Wang et al. 2020). Joint grid discretization
is applicable as a data transformation step to prepare data for
model-free inference of association, function, or causality.
Imports: Rcpp, cluster, fossil, dqrng, Rdpack, plotrix

Joint grid discretization is applicable as a data transformation step
to prepare data for model-free inference of association, function, or
Imports: Rcpp, Ckmeans.1d.dp, cluster, fossil, dqrng, mclust, Rdpack,
Suggests: FunChisq, knitr, testthat (>= 2.1.0), rmarkdown

Title: Executes functions on remote hosts, clusters or grids.
Author: Dennis Wegener, Malte Lohmeyer, Stefan Rueping
Description: GridR is an R-Package that can be used to submit R functions for execution on remote computers, clusters or grids. In addition, users are provided with an interface to share variables and functions with other users.
Packaged: Thu Dec  4 13:23:16 2008; root

Packaged: Fri Jan 30 13:50:59 2009; root

Title: Uses Grieg-Smith method on 2 dimentional spatial data
Description: The function GriegSmith accepts either quadrat count data,
a point process object(ppp) or a matrix of x and y coordinates.
The function calculates a nested analysis of variance and

Title: Calculate Optical Parameters from Spindle Stage Measurements
Description: Calculates optical parameters of crystals like the optical
axes, the axis angle 2V, and the direction of the principal axes of
the indicatrix from extinction angles measured on a spindle stage
mounted on a polarisation microscope stage. Details of the method can be found in Dufey (2017) <arXiv:1703.00070>.

Title: Learn Group Bayesian Networks using Hierarchical Clustering
Description: Learn group Bayesian Networks using hierarchical Clustering. This package implements the inference of group Bayesian networks based on hierarchical Clustering, and the adaptive refinement of the grouping regarding an outcome of interest.
Depends: R (>= 2.14.0), bnlearn, ClustOfVar, PCAmixdata, arules, zoo
Imports: PRROC, MLmetrics, rlist, stats, purrr, magrittr, visNetwork,