Running examples in 'FeatureHashing-Ex.R' failed
The error most likely occurred in:

> ### Name: hashed.model.matrix
> ### Title: Create a model matrix with feature hashing
> ### Aliases: hash.sign hashed.interaction.value hashed.model.matrix
> ###   hashed.value
> 
> ### ** Examples
> 
> # The following scripts show how to fit a logistic regression
> # after feature hashing
> ## Not run: 
> ##D data(ipinyou)
> ##D f <- ~ IP + Region + City + AdExchange + Domain +
> ##D  URL + AdSlotId + AdSlotWidth + AdSlotHeight +
> ##D  AdSlotVisibility + AdSlotFormat + CreativeID +
> ##D  Adid + split(UserTag, delim = ",")
> ##D # if the version of FeatureHashing is 0.8, please use the following command:
> ##D # m.train <- as(hashed.model.matrix(f, ipinyou.train, 2^16, transpose = FALSE), "dgCMatrix")
> ##D m.train <- hashed.model.matrix(f, ipinyou.train, 2^16)
> ##D m.test <- hashed.model.matrix(f, ipinyou.test, 2^16)
> ##D 
> ##D # logistic regression with glmnet
> ##D 
> ##D library(glmnet)
> ##D 
> ##D cv.g.lr <- cv.glmnet(m.train, ipinyou.train$IsClick,
> ##D  family = "binomial")#, type.measure = "auc")
> ##D p.lr <- predict(cv.g.lr, m.test, s="lambda.min")
> ##D auc(ipinyou.test$IsClick, p.lr)
> ##D 
> ##D ## Per-Coordinate FTRL-Proximal with $L_1$ and $L_2$ Regularization for Logistic Regression
> ##D 
> ##D # The following scripts use an implementation of the FTRL-Proximal for Logistic Regresion,
> ##D # which is published in McMahan, Holt and Sculley et al. (2013), to predict the probability
> ##D # (1-step prediction) and update the model simultaneously.
> ##D 
> ##D 
> ##D source(system.file("ftprl.R", package = "FeatureHashing"))
> ##D m.train <- hashed.model.matrix(f, ipinyou.train, 2^16, transpose = TRUE)
> ##D ftprl <- initialize.ftprl(0.1, 1, 0.1, 0.1, 2^16)
> ##D ftprl <- update.ftprl(ftprl, m.train, ipinyou.train$IsClick, predict = TRUE)
> ##D auc(ipinyou.train$IsClick, attr(ftprl, "predict"))
> ##D 
> ##D # If we use the same algorithm to predict the click through rate of the 3rd season of iPinYou,
> ##D # the overall AUC will be 0.77 which is comparable to the overall AUC of the
> ##D # 3rd season 0.76 reported in Zhang, Yuan, Wang, et al. (2014).
> ## End(Not run)
> 
> # The following scripts show the implementation of the FeatureHashing.
> 
> # Below the original values will be project in a space of 2^6 dimensions
> m <- hashed.model.matrix(~ ., CO2, 2^6, create.mapping = TRUE,
+  transpose = TRUE, is.dgCMatrix = FALSE)
> 
> # Print the matrix via dgCMatrix
> as(m, "dgCMatrix")
64 x 84 sparse Matrix of class "dgCMatrix"
Error in isFALSE(suppRows) : could not find function "isFALSE"
Calls: <Anonymous> -> <Anonymous> -> printSpMatrix2
Execution halted