Title: End-member modelling algorithm and supporting functions for
Author: Michael Dietze, Elisabeth Dietze
Description: This package provides a set of functions for convenient
end-member modelling analysis of grain size-data (EMMAgeo).
Depends: R (>= 3.0.1), GPArotation, limSolve, shape

Description: This package provides a set of functions for convenient end-member modelling analysis of grain-size data (EMMAgeo).

Title: End-Member Modelling of Grain-Size Data
Description: End-member modelling analysis of grain-size data.
Depends: R (>= 3.2.1), GPArotation, limSolve, shape, shiny
"EMMA.R" "get.limits.R" "get.l.opt.R" "get.l.R" "get.q.R"

Description: End-member modelling analysis of grain-size data is an approach
to unmix a data set's underyling distributions and their contribution to
the data set. EMMAgeo provides deterministic and robust protocols for
Imports: GPArotation, limSolve, caTools, shiny, matrixStats

to unmix a data set's underlying distributions and their contribution to

Title: Semi-Supervised Learning via Gaussian Mixture Model
Author: Ziyang Lyu, Daniel Ahfock, Geoffrey J. McLachlan
Description: The algorithm of semi-supervised learning for a partially classified sample via Gaussian mixture model with the missing-label mechanism is designed for a fitting g-component Gaussian mixture model via maximum likelihood (ML). The classifier is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. It suggests that the missingness of the labels of the features can be modelled by representing the probability of a missing-label for a feature via the logistic model depending on the entropy of the feature or an appropriate proxy for it.

Title: Semi-Supervised Gaussian Mixture Model with a Missing-Data
Description: The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayes’ rule.