Chapter 6 附录 A: 使用到的包

6.1 library(lattice)

The lattice add-on of Trellis graphics for R. Ref:

6.2 A.2 library(knitr)

For Dynamic Report Generation in R. Ref:

6.3 A.3 library(gplots)

Various R Programming Tools for Plotting Data. Ref:

6.4 A.4 library(ggplot2)

An Implementation of the Grammar of Graphics. Ref:

6.5 A.5 library(ClustOfVar)

Clustering of variables. Ref:

6.6 A.6 library(ape)

Analyses of Phylogenetics and Evolution (as.phylo). Ref:

6.7 A.7 library(Information)

Data Exploration with Information Theory (Weight-of-Evidence and Information Value). Ref:

6.8 A.8 library(ROCR)

Visualizing the Performance of Scoring Classifiers. Ref:

6.9 A.9 library(caret)

Classification and Regression Training - for any machine learning algorithms. Ref:

6.10 A.10 library(rpart)

Recursive partitioning for classification, regression and survival trees. Ref:

6.11 A.10.1 library(rpart.utils)

Tools for parsing and manipulating rpart objects, including generating machine readable rules. Ref:

6.12 A.10.2 library(rpart.plot)

Plot ‘rpart’ Models: An Enhanced Version of ‘plot.rpart’. Ref:

6.13 A.11 library(randomForest)

Leo Breiman and Cutler’s Random Forests for Classification and Regression. Ref:

6.14 A.12 library(party)

A computational toolbox for recursive partitioning - Conditional inference Trees. Ref:

6.15 A.13 library(bnlearn)

Bayesian Network Structure Learning, Parameter Learning and Inference. Ref:

6.16 A.14 library(DAAG)

Data Analysis and Graphics Data and Functions. Ref:

6.17 A.15 library(vcd)

Visualizing Categorical Data. Ref:

6.18 A.16 library(neuralnet)

Neural Network implementation. Ref:

6.19 A.17 library(kernlab)

Kernel-Based Machine Learning Lab. Ref:

6.20 A.18 library(glmnet)

Lasso and Elastic-Net Regularized Generalized Linear Models. Ref:

6.21 A.19 library(lars)

Least Angle Regression, Lasso and Forward Stagewise. Ref: