Title: Framework for the Analysis of Kinetic Visual Field Data
Imports: circular, lqmm, splancs, sp, plotrix, MASS
Author: Dipesh E Patel & Mario Cortina-Borja
Maintainer: Dipesh E Patel <dipesh_patel@ucl.ac.uk>
Description: Data cleaning, processing, visualisation and analysis for manual (Goldmann) and automated (Octopus 900) kinetic visual field data.

Title: Routines for fitting kinetic models to chemical degradation data
Description: Simple calculation routines based on the FOCUS Kinetics
Report (2006). Only kinetic models for parent only datasets are
included (no metabolite formation/decline or multicompartment
kinetics). Please note that no warranty is implied for
correctness of results or fitness for a particular purpose.

Author: Johannes Ranke and Katrin Lindenberger

included (no metabolite formation/decline or multicompartment kinetics).
Please note that no warranty is implied for correctness of results or

kinetics).  Please note that no warranty is implied for

Author: Johannes Ranke, with contributions from Katrin Lindenberger

This package is not actively developed at the moment. Please check the mkin
package for an actively maintained package for kinetic evaluations of

Title: Routines for Fitting Kinetic Models to Chemical Degradation Data
person("Katrin", "Lindenberger", role = "ctb"))
The FOCUS Kinetics Report first published in 2006 describes mathematical
models and recommends statistical methods for the evaluation of
chemical degradation data. This package implements fitting the kinetic
models suitable for observations of the decline of a single chemical
compound (no metabolite formation/decline or multi-compartment kinetics).
fitness for a particular purpose.  'kinfit' is maintained, but not
actively developed at the moment. Please check the 'mkin' package for an
actively developed package for kinetic evaluations of degradation data.

Title: An Implementation of 'kinn' Algorithm, a Graph Based Regression
Description: A graph based regression model from flat unstructured dataset.
Each line in the input data set is treated as a node from which an edge to another line (node) can be formed.
In the training process, a model is created which contains sparse graph adjacency matrix.
This model is then used for prediction by taking a predictor and the model as inputs and outputs
a prediction which is an average of the most similar node and its neighbours in the model graph.
Packaged: 2016-05-22 15:32:02 UTC; josephkeshet