DMwR2-package |
Functions and data for the second edition of the book "Data Mining with R" |
algae |
Training data for predicting algae blooms |
algae.sols |
The solutions for the test data set for predicting algae blooms |
centralImputation |
Fill in NA values with central statistics |
centralValue |
Obtain statistic of centrality |
createEmbedDS |
Creates an embeded data set from an univariate time series |
dist.to.knn |
An auxiliary function of 'lofactor()' |
DMwR2 |
Functions and data for the second edition of the book "Data Mining with R" |
GSPC |
A set of daily quotes for SP500 |
kNN |
k-Nearest Neighbour Classification |
knneigh.vect |
An auxiliary function of 'lofactor()' |
knnImputation |
Fill in NA values with the values of the nearest neighbours |
lofactor |
An implementation of the LOF algorithm |
manyNAs |
Find rows with too many NA values |
nrLinesFile |
Counts the number of lines of a file |
outliers.ranking |
Obtain outlier rankings |
plot-method |
Class "tradeRecord" |
reachability |
An auxiliary function of 'lofactor()' |
rpartXse |
Obtain a tree-based model |
rt.prune |
Prune a tree-based model using the SE rule |
sales |
A data set with sale transaction reports |
sampleCSV |
Drawing a random sample of lines from a CSV file |
sampleDBMS |
Drawing a random sample of records of a table stored in a DBMS |
SelfTrain |
Self train a model on semi-supervised data |
show-method |
Class "tradeRecord" |
sigs.PR |
Precision and recall of a set of predicted trading signals |
SoftMax |
Normalize a set of continuous values using SoftMax |
sp500 |
A set of daily quotes for SP500 in CSV Format |
summary-method |
Class "tradeRecord" |
test.algae |
Testing data for predicting algae blooms |
tradeRecord |
Class "tradeRecord" |
tradeRecord-class |
Class "tradeRecord" |
trading.signals |
Discretize a set of values into a set of trading signals |
trading.simulator |
Simulate daily trading using a set of trading signals |
tradingEvaluation |
Obtain a set of evaluation metrics for a set of trading actions |