Generalized Minimum Distance of distributions
Xiaobei Zhao <xiaobei@binf.ku.dk>, Albin Sandelin
<albin@binf.ku.dk>
GMD is a package for non-parametric distance measurement
between two discrete frequency distributions.
'zzz.R' 'GMD-package.R' 'GMD-internal.R' 'gmd.R' 'gmdm.R'
'cage.R'

'zzz.R' 'GMD-package.R' 'GMD-internal.R' 'GMD-data.R'
'ghist.R' 'gdist.R' 'css.R' 'heatmap3.R' 'gmdp.R' 'gmdm.R'

GMD is a package for non-parametric distance measurement between
two discrete frequency distributions.
'GMD-package.R' 'imports.R' 'util.R' 'GMD-internal.R'
'GMD-data.R' 'ghist.R' 'gdist.R' 'css.R' 'heatmap3.R' 'gmdp.R'
'gmdm.R'
c(
person("Xiaobei", "Zhao", email="xiaobei@binf.ku.dk",
role=c("aut", "cre", "cph")),
person("Albin", "Sandelin", email="albin@binf.ku.dk",
role=c("aut"))
)
Xiaobei Zhao [aut, cre, cph],
Albin Sandelin [aut]

Xiaobei Zhao <xiaobei@binf.ku.dk>, Albin Sandelin <albin@binf.ku.dk>
GMD is a package for non-parametric distance measurement between two discrete frequency distributions.

Predicting and Forecasting Time Series via GMDH-Type Neural
Network Algorithms
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to predict and forecast a univariate time series.

Short Term Forecasting via GMDH-Type Neural Network Algorithms
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.

Binary Classification via GMDH-Type Neural Network Algorithm
e1071, utils, graphics, stats, randomForest, glmnet, nnet,
MASS, plotly, magrittr, xtable
Osman Dag [aut, cre], Erdem Karabulut [aut], Reha Alpar [aut]
Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithm. Also, it produces a well-formatted table of descriptives for a binary response. Moreover, it returns confusion matrix and related statistics and scatter plot with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response. See Dag O. and Yozgatligil C. (2016, ISSN:2073-4859) and Kondo T. and Ueno J. (2016, ISSN:1349-4198) for the details of GMDH algorithms.