From eda81fd5e0a601a082f480f92cc297690511c48c Mon Sep 17 00:00:00 2001
From: Vitalie Spinu
Date: Sat, 26 Nov 2016 17:28:33 +0100
Subject: [PATCH] Fix some links to online documentation
---
bookdown/19-GA.Rmd | 4 ++--
bookdown/20-SA.Rmd | 4 ++--
html/GA.Rhtml | 4 ++--
html/SA.Rhtml | 4 ++--
pkg/caret/R/gafs.R | 4 ++--
pkg/caret/R/misc.R | 2 +-
pkg/caret/R/preProcess.R | 2 +-
pkg/caret/R/rfe.R | 12 +++++------
pkg/caret/R/safs.R | 20 +++++++++----------
pkg/caret/R/selection.R | 2 +-
pkg/caret/man/gafs_initial.Rd | 4 ++--
pkg/caret/man/oneSE.Rd | 2 +-
pkg/caret/man/preProcess.Rd | 2 +-
pkg/caret/man/rfe.Rd | 8 ++++----
pkg/caret/man/rfeControl.Rd | 4 ++--
pkg/caret/man/safs.Rd | 4 ++--
pkg/caret/man/safsControl.Rd | 12 +++++------
pkg/caret/man/safs_initial.Rd | 4 ++--
.../Open_Data_Science_Conference/caret.Rnw | 2 +-
.../Open_Data_Science_Conference/caret.tex | 2 +-
release_process/talk/caret.Rnw | 2 +-
21 files changed, 52 insertions(+), 52 deletions(-)
diff --git a/bookdown/19-GA.Rmd b/bookdown/19-GA.Rmd
index 795308e44..7b57e640c 100644
--- a/bookdown/19-GA.Rmd
+++ b/bookdown/19-GA.Rmd
@@ -82,8 +82,8 @@ Other options, such as `preProcess`, can be passed in as well.
Some important options to `gafsControl` are:
- - `method`, `number`, `repeats`, `index`, `indexOut`, etc: options similar to those for [`train`](http://topepo.github.io/caret/training.html#control) top control resampling.
- - `metric`: this is similar to [`train`](http://topepo.github.io/caret/training.html#control)'s option but, in this case, the value should be a named vector with values for the internal and external metrics. If none are specified, the first value returned by the summary functions (see details below) are used and a warning is issued. A similar two-element vector for the option `maximize` is also required. See the [last example here](#example2) for an illustration.
+ - `method`, `number`, `repeats`, `index`, `indexOut`, etc: options similar to those for [`train`](http://topepo.github.io/caret/model-training-and-tuning.html#control) top control resampling.
+ - `metric`: this is similar to [`train`](http://topepo.github.io/caret/model-training-and-tuning.html#control)'s option but, in this case, the value should be a named vector with values for the internal and external metrics. If none are specified, the first value returned by the summary functions (see details below) are used and a warning is issued. A similar two-element vector for the option `maximize` is also required. See the [last example here](#example2) for an illustration.
- `holdout`: this is a number between `[0, 1)` that can be used to hold out samples for computing the internal fitness value. Note that this is independent of the external resampling step. Suppose 10-fold CV is being used. Within a resampling iteration, `holdout` can be used to sample an additional proportion of the 90% resampled data to use for estimating fitness. This may not be a good idea unless you have a very large training set and want to avoid an internal resampling procedure to estimate fitness.
- `allowParallel` and `genParallel`: these are logicals to control where parallel processing should be used (if at all). The former will parallelize the external resampling while the latter parallelizes the fitness calculations within a generation. `allowParallel` will almost always be more advantageous.
diff --git a/bookdown/20-SA.Rmd b/bookdown/20-SA.Rmd
index 4d52b8899..6e3184a85 100644
--- a/bookdown/20-SA.Rmd
+++ b/bookdown/20-SA.Rmd
@@ -75,8 +75,8 @@ Other options, such as `preProcess`, can be passed in as well.
Some important options to `safsControl` are:
- - `method`, `number`, `repeats`, `index`, `indexOut`, etc: options similar to those for [`train`](http://topepo.github.io/caret/training.html#control) top control resampling.
- - `metric`: this is similar to [`train`](http://topepo.github.io/caret/training.html#control)'s option but, in this case, the value should be a named vector with values for the internal and external metrics. If none are specified, the first value returned by the summary functions (see details below) are used and a warning is issued. A similar two-element vector for the option `maximize` is also required. See the [last example here](#example2) for an illustration.
+ - `method`, `number`, `repeats`, `index`, `indexOut`, etc: options similar to those for [`train`](http://topepo.github.io/caret/model-training-and-tuning.html#control) top control resampling.
+ - `metric`: this is similar to [`train`](http://topepo.github.io/caret/model-training-and-tuning.html#control)'s option but, in this case, the value should be a named vector with values for the internal and external metrics. If none are specified, the first value returned by the summary functions (see details below) are used and a warning is issued. A similar two-element vector for the option `maximize` is also required. See the [last example here](#example2) for an illustration.
- `holdout`: this is a number between `[0, 1)` that can be used to hold out samples for computing the internal fitness value. Note that this is independent of the external resampling step. Suppose 10-fold CV is being used. Within a resampling iteration, `holdout` can be used to sample an additional proportion of the 90% resampled data to use for estimating fitness. This may not be a good idea unless you have a very large training set and want to avoid an internal resampling procedure to estimate fitness.
- `improve`: an integer (or infinity) defining how many iterations should pass without an improvement in fitness before the current subset is reset to the last known improvement.
- `allowParallel`: should the external resampling loop be run in parallel?.
diff --git a/html/GA.Rhtml b/html/GA.Rhtml
index 83df94245..c5a5f6004 100644
--- a/html/GA.Rhtml
+++ b/html/GA.Rhtml
@@ -171,10 +171,10 @@ Some important options to gafsControl are:
- method, number, repeats, index, indexOut, etc:
- options similar to those for train top control resampling.
+ options similar to those for train top control resampling.
- metric:
- this is similar to train's option but, in this case, the value should be a named vector with values for the internal and external metrics. If none are specified, the first value returned by the summary functions (see details below) are used and a warning is issued. A similar two-element vector for the option maximize is also required. See the last example here for an illustration.
+ this is similar to train's option but, in this case, the value should be a named vector with values for the internal and external metrics. If none are specified, the first value returned by the summary functions (see details below) are used and a warning is issued. A similar two-element vector for the option maximize is also required. See the last example here for an illustration.
- holdout:
this is a number between [0, 1) that can be used to hold out samples for computing the internal fitness value. Note that this is independent of the external resampling step. Suppose 10-fold CV is being used. Within a resampling iteration, holdout can be used to sample an additional proportion of the 90% resampled data to use for estimating fitness. This may not be a good idea unless you have a very large training set and want to avoid an internal resampling procedure to estimate fitness.
diff --git a/html/SA.Rhtml b/html/SA.Rhtml
index 7f59a04e0..f63f842da 100644
--- a/html/SA.Rhtml
+++ b/html/SA.Rhtml
@@ -163,10 +163,10 @@ Some important options to safsControl are:
- method, number, repeats, index, indexOut, etc:
- options similar to those for train top control resampling.
+ options similar to those for train top control resampling.
- metric:
- this is similar to train's option but, in this case, the value should be a named vector with values for the internal and external metrics. If none are specified, the first value returned by the summary functions (see details below) are used and a warning is issued. A similar two-element vector for the option maximize is also required. See the last example here for an illustration.
+ this is similar to train's option but, in this case, the value should be a named vector with values for the internal and external metrics. If none are specified, the first value returned by the summary functions (see details below) are used and a warning is issued. A similar two-element vector for the option maximize is also required. See the last example here for an illustration.
- holdout:
this is a number between [0, 1) that can be used to hold out samples for computing the internal fitness value. Note that this is independent of the external resampling step. Suppose 10-fold CV is being used. Within a resampling iteration, holdout can be used to sample an additional proportion of the 90% resampled data to use for estimating fitness. This may not be a good idea unless you have a very large training set and want to avoid an internal resampling procedure to estimate fitness.
diff --git a/pkg/caret/R/gafs.R b/pkg/caret/R/gafs.R
index 9c264a5f4..fe843d192 100644
--- a/pkg/caret/R/gafs.R
+++ b/pkg/caret/R/gafs.R
@@ -56,7 +56,7 @@ ga_func_check <- function(x) {
#'
#' These functions are used with the \code{functions} argument of the
#' \code{\link{gafsControl}} function. More information on the details of these
-#' functions are at \url{http://topepo.github.io/caret/GA.html}.
+#' functions are at \url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html}.
#'
#' Most of the \code{gafs_*} functions are based on those from the GA package
#' by Luca Scrucca. These functions here are small re-writes to work outside of
@@ -102,7 +102,7 @@ ga_func_check <- function(x) {
#'
#' \url{cran.r-project.org/web/packages/GA/}
#'
-#' \url{http://topepo.github.io/caret/GA.html}
+#' \url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html}
#' @examples
#'
#' pop <- gafs_initial(vars = 10, popSize = 10)
diff --git a/pkg/caret/R/misc.R b/pkg/caret/R/misc.R
index cbedf9848..c0de1d116 100644
--- a/pkg/caret/R/misc.R
+++ b/pkg/caret/R/misc.R
@@ -437,7 +437,7 @@ parse_sampling <- function(x) {
if(!(x_class %in% c("character", "function", "list"))) {
stop(paste("The sampling argument should be either a",
"string, function, or list. See",
- "http://topepo.github.io/caret/training.html"))
+ "http://topepo.github.io/caret/model-training-and-tuning.html"))
}
if(x_class == "character") {
x <- x[1]
diff --git a/pkg/caret/R/preProcess.R b/pkg/caret/R/preProcess.R
index 8d03fdc05..3ec356266 100644
--- a/pkg/caret/R/preProcess.R
+++ b/pkg/caret/R/preProcess.R
@@ -150,7 +150,7 @@ invHyperbolicSineFunc <- function(x) log(x+sqrt(x^2+1))
#' @seealso \code{\link{BoxCoxTrans}}, \code{\link{expoTrans}}
#' \code{\link[MASS]{boxcox}}, \code{\link[stats]{prcomp}},
#' \code{\link[fastICA]{fastICA}}, \code{\link{spatialSign}}
-#' @references \url{http://topepo.github.io/caret/preprocess.html}
+#' @references \url{http://topepo.github.io/caret/pre-processing.html}
#'
#' Kuhn and Johnson (2013), Applied Predictive Modeling, Springer, New York
#' (chapter 4)
diff --git a/pkg/caret/R/rfe.R b/pkg/caret/R/rfe.R
index e328ff71b..275170f04 100644
--- a/pkg/caret/R/rfe.R
+++ b/pkg/caret/R/rfe.R
@@ -1,16 +1,16 @@
#' Backwards Feature Selection
#'
-#' A simple backwards selection, a.k.a. recursive feature selection (RFE),
+#' A simple backwards selection, a.k.a. recursive feature elimination (RFE),
#' algorithm
#'
#' More details on this function can be found at
-#' \url{http://topepo.github.io/caret/featureselection.html}.
+#' \url{http://topepo.github.io/caret/recursive-feature-elimination.html}.
#'
#' This function implements backwards selection of predictors based on
#' predictor importance ranking. The predictors are ranked and the less
#' important ones are sequentially eliminated prior to modeling. The goal is to
#' find a subset of predictors that can be used to produce an accurate model.
-#' The web page \url{http://topepo.github.io/caret/featureselection.html#rfe}
+#' The web page \url{http://topepo.github.io/caret/recursive-feature-elimination.html#rfe}
#' has more details and examples related to this function.
#'
#' \code{rfe} can be used with "explicit parallelism", where different
@@ -48,7 +48,7 @@
#' @param maximize a logical: should the metric be maximized or minimized?
#' @param rfeControl a list of options, including functions for fitting and
#' prediction. The web page
-#' \url{http://topepo.github.io/caret/featureselection.html#rfe} has more
+#' \url{http://topepo.github.io/caret/recursive-feature-elimination.html#rfe} has more
#' details and examples related to this function.
#' @param object an object of class \code{rfe}
#' @param size a single integers corresponding to the number of features that
@@ -589,7 +589,7 @@ plot.rfe <- function (x,
#' details of the feature selection algorithms used in this package.
#'
#' More details on this function can be found at
-#' \url{http://topepo.github.io/caret/featureselection.html#rfe}.
+#' \url{http://topepo.github.io/caret/recursive-feature-elimination.html#rfe}.
#'
#' Backwards selection requires function to be specified for some operations.
#'
@@ -648,7 +648,7 @@ plot.rfe <- function (x,
#' \code{\link{nbFuncs}}.
#'
#' Model details about these functions, including examples, are at
-#' \url{http://topepo.github.io/caret/featureselection.html}. .
+#' \url{http://topepo.github.io/caret/recursive-feature-elimination.html}. .
#'
#' @param functions a list of functions for model fitting, prediction and
#' variable importance (see Details below)
diff --git a/pkg/caret/R/safs.R b/pkg/caret/R/safs.R
index 4a85adf55..5bfa99923 100644
--- a/pkg/caret/R/safs.R
+++ b/pkg/caret/R/safs.R
@@ -177,8 +177,8 @@ predict.safs <- function (object, newdata, ...) {
#' Many of these options are the same as those described for
#' \code{\link[caret]{trainControl}}. More extensive documentation and examples
#' can be found on the \pkg{caret} website at
-#' \url{http://topepo.github.io/caret/GA.html#syntax} and
-#' \url{http://topepo.github.io/caret/SA.html#syntax}.
+#' \url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html#syntax} and
+#' \url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html#syntax}.
#'
#' The \code{functions} component contains the information about how the model
#' should be fit and summarized. It also contains the elements needed for the
@@ -227,8 +227,8 @@ predict.safs <- function (object, newdata, ...) {
#' \code{new}, and \code{iteration}, computes the acceptance probabilities
#' }
#'
-#' The pages \url{http://topepo.github.io/caret/GA.html} and
-#' \url{http://topepo.github.io/caret/SA.html} have more details about each of
+#' The pages \url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html} and
+#' \url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html} have more details about each of
#' these functions.
#'
#' \code{holdout} can be used to hold out samples for computing the internal
@@ -301,8 +301,8 @@ predict.safs <- function (object, newdata, ...) {
#' @seealso \code{\link{safs}}, \code{\link{safs}}, , \code{\link{caretGA}},
#' \code{\link{rfGA}}, \code{\link{treebagGA}}, \code{\link{caretSA}},
#' \code{\link{rfSA}}, \code{\link{treebagSA}}
-#' @references \url{http://topepo.github.io/caret/GA.html},
-#' \url{http://topepo.github.io/caret/SA.html}
+#' @references \url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html},
+#' \url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html}
#' @keywords utilities
#' @export safsControl
safsControl <- function(functions = NULL,
@@ -447,9 +447,9 @@ safsControl <- function(functions = NULL,
#' @return an object of class \code{safs}
#' @author Max Kuhn
#' @seealso \code{\link{safsControl}}, \code{\link{predict.safs}}
-#' @references \url{http://topepo.github.io/caret/GA.html}
+#' @references \url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html}
#'
-#' \url{http://topepo.github.io/caret/SA.html}
+#' \url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html}
#'
#' Kuhn and Johnson (2013), Applied Predictive Modeling, Springer
#'
@@ -677,7 +677,7 @@ safs <- function (x, ...) UseMethod("safs")
#'
#' These functions are used with the \code{functions} argument of the
#' \code{\link{safsControl}} function. More information on the details of these
-#' functions are at \url{http://topepo.github.io/caret/SA.html}.
+#' functions are at \url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html}.
#'
#' The \code{initial} function is used to create the first predictor subset.
#' The function \code{safs_initial} randomly selects 20\% of the predictors.
@@ -732,7 +732,7 @@ safs <- function (x, ...) UseMethod("safs")
#' regression).
#' @author Max Kuhn
#' @seealso \code{\link{safs}}, \code{\link{safsControl}}
-#' @references \url{http://topepo.github.io/caret/SA.html}
+#' @references \url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html}
#' @examples
#'
#' selected_vars <- safs_initial(vars = 10 , prob = 0.2)
diff --git a/pkg/caret/R/selection.R b/pkg/caret/R/selection.R
index c75bffaf3..187ad8ee2 100644
--- a/pkg/caret/R/selection.R
+++ b/pkg/caret/R/selection.R
@@ -12,7 +12,7 @@
#' distributions.
#'
#' More details on these functions can be found at
-#' \url{http://topepo.github.io/caret/training.html#custom}.
+#' \url{http://topepo.github.io/caret/model-training-and-tuning.html#custom}.
#'
#' By default, \code{\link{train}} uses \code{best}.
#'
diff --git a/pkg/caret/man/gafs_initial.Rd b/pkg/caret/man/gafs_initial.Rd
index 65e2faec5..34b1adf66 100644
--- a/pkg/caret/man/gafs_initial.Rd
+++ b/pkg/caret/man/gafs_initial.Rd
@@ -54,7 +54,7 @@ Built-in functions related to genetic algorithms
These functions are used with the \code{functions} argument of the
\code{\link{gafsControl}} function. More information on the details of these
-functions are at \url{http://topepo.github.io/caret/GA.html}.
+functions are at \url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html}.
Most of the \code{gafs_*} functions are based on those from the GA package
by Luca Scrucca. These functions here are small re-writes to work outside of
@@ -121,7 +121,7 @@ Journal of Statistical Software, 53(4), 1-37.
\url{cran.r-project.org/web/packages/GA/}
-\url{http://topepo.github.io/caret/GA.html}
+\url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html}
}
\seealso{
\code{\link{gafs}}, \code{\link{gafsControl}}
diff --git a/pkg/caret/man/oneSE.Rd b/pkg/caret/man/oneSE.Rd
index 0ddc17648..e8ddda0f0 100644
--- a/pkg/caret/man/oneSE.Rd
+++ b/pkg/caret/man/oneSE.Rd
@@ -43,7 +43,7 @@ use either \code{"Accuracy"} or \code{"Kappa"} (for unbalanced class
distributions.
More details on these functions can be found at
-\url{http://topepo.github.io/caret/training.html#custom}.
+\url{http://topepo.github.io/caret/model-training-and-tuning.html#custom}.
By default, \code{\link{train}} uses \code{best}.
diff --git a/pkg/caret/man/preProcess.Rd b/pkg/caret/man/preProcess.Rd
index ff212ccf9..b51163a6a 100644
--- a/pkg/caret/man/preProcess.Rd
+++ b/pkg/caret/man/preProcess.Rd
@@ -183,7 +183,7 @@ test <- predict(preProc, bbbDescr[101:208,-3])
Max Kuhn, median imputation by Zachary Mayer
}
\references{
-\url{http://topepo.github.io/caret/preprocess.html}
+\url{http://topepo.github.io/caret/pre-processing.html}
Kuhn and Johnson (2013), Applied Predictive Modeling, Springer, New York
(chapter 4)
diff --git a/pkg/caret/man/rfe.Rd b/pkg/caret/man/rfe.Rd
index 2e8ead851..9410b604c 100644
--- a/pkg/caret/man/rfe.Rd
+++ b/pkg/caret/man/rfe.Rd
@@ -39,7 +39,7 @@ arguments.}
\item{rfeControl}{a list of options, including functions for fitting and
prediction. The web page
-\url{http://topepo.github.io/caret/featureselection.html#rfe} has more
+\url{http://topepo.github.io/caret/recursive-feature-elimination.html#rfe} has more
details and examples related to this function.}
\item{testX}{a matrix or data frame of test set predictors. This must have
@@ -70,18 +70,18 @@ columns for the test set outcome, the predicted outcome and the subset
size.}
}
\description{
-A simple backwards selection, a.k.a. recursive feature selection (RFE),
+A simple backwards selection, a.k.a. recursive feature elimination (RFE),
algorithm
}
\details{
More details on this function can be found at
-\url{http://topepo.github.io/caret/featureselection.html}.
+\url{http://topepo.github.io/caret/recursive-feature-elimination.html}.
This function implements backwards selection of predictors based on
predictor importance ranking. The predictors are ranked and the less
important ones are sequentially eliminated prior to modeling. The goal is to
find a subset of predictors that can be used to produce an accurate model.
-The web page \url{http://topepo.github.io/caret/featureselection.html#rfe}
+The web page \url{http://topepo.github.io/caret/recursive-feature-elimination.html#rfe}
has more details and examples related to this function.
\code{rfe} can be used with "explicit parallelism", where different
diff --git a/pkg/caret/man/rfeControl.Rd b/pkg/caret/man/rfeControl.Rd
index db088fd18..38378d519 100644
--- a/pkg/caret/man/rfeControl.Rd
+++ b/pkg/caret/man/rfeControl.Rd
@@ -71,7 +71,7 @@ details of the feature selection algorithms used in this package.
}
\details{
More details on this function can be found at
-\url{http://topepo.github.io/caret/featureselection.html#rfe}.
+\url{http://topepo.github.io/caret/recursive-feature-elimination.html#rfe}.
Backwards selection requires function to be specified for some operations.
@@ -130,7 +130,7 @@ Examples of these functions are included in the package:
\code{\link{nbFuncs}}.
Model details about these functions, including examples, are at
-\url{http://topepo.github.io/caret/featureselection.html}. .
+\url{http://topepo.github.io/caret/recursive-feature-elimination.html}. .
}
\examples{
diff --git a/pkg/caret/man/safs.Rd b/pkg/caret/man/safs.Rd
index b414b781a..29929f396 100644
--- a/pkg/caret/man/safs.Rd
+++ b/pkg/caret/man/safs.Rd
@@ -131,9 +131,9 @@ rf_search
Max Kuhn
}
\references{
-\url{http://topepo.github.io/caret/GA.html}
+\url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html}
-\url{http://topepo.github.io/caret/SA.html}
+\url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html}
Kuhn and Johnson (2013), Applied Predictive Modeling, Springer
diff --git a/pkg/caret/man/safsControl.Rd b/pkg/caret/man/safsControl.Rd
index 151c1d403..bce6d5308 100644
--- a/pkg/caret/man/safsControl.Rd
+++ b/pkg/caret/man/safsControl.Rd
@@ -82,8 +82,8 @@ Control the computational nuances of the \code{\link{gafs}} and
Many of these options are the same as those described for
\code{\link[caret]{trainControl}}. More extensive documentation and examples
can be found on the \pkg{caret} website at
-\url{http://topepo.github.io/caret/GA.html#syntax} and
-\url{http://topepo.github.io/caret/SA.html#syntax}.
+\url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html#syntax} and
+\url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html#syntax}.
The \code{functions} component contains the information about how the model
should be fit and summarized. It also contains the elements needed for the
@@ -132,8 +132,8 @@ changes to the subsets.
\code{new}, and \code{iteration}, computes the acceptance probabilities
}
-The pages \url{http://topepo.github.io/caret/GA.html} and
-\url{http://topepo.github.io/caret/SA.html} have more details about each of
+The pages \url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html} and
+\url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html} have more details about each of
these functions.
\code{holdout} can be used to hold out samples for computing the internal
@@ -166,8 +166,8 @@ number of workers and the amount of memory required exponentially.
Max Kuhn
}
\references{
-\url{http://topepo.github.io/caret/GA.html},
-\url{http://topepo.github.io/caret/SA.html}
+\url{http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html},
+\url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html}
}
\seealso{
\code{\link{safs}}, \code{\link{safs}}, , \code{\link{caretGA}},
diff --git a/pkg/caret/man/safs_initial.Rd b/pkg/caret/man/safs_initial.Rd
index c1d621c96..6effb3b91 100644
--- a/pkg/caret/man/safs_initial.Rd
+++ b/pkg/caret/man/safs_initial.Rd
@@ -72,7 +72,7 @@ Built-in functions related to simulated annealing
These functions are used with the \code{functions} argument of the
\code{\link{safsControl}} function. More information on the details of these
-functions are at \url{http://topepo.github.io/caret/SA.html}.
+functions are at \url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html}.
The \code{initial} function is used to create the first predictor subset.
The function \code{safs_initial} randomly selects 20\% of the predictors.
@@ -148,7 +148,7 @@ rf_sa <- safs(x = predictors,
Max Kuhn
}
\references{
-\url{http://topepo.github.io/caret/SA.html}
+\url{http://topepo.github.io/caret/feature-selection-using-simulated-annealing.html}
}
\seealso{
\code{\link{safs}}, \code{\link{safsControl}}
diff --git a/release_process/Open_Data_Science_Conference/caret.Rnw b/release_process/Open_Data_Science_Conference/caret.Rnw
index 184522faa..5285ef609 100644
--- a/release_process/Open_Data_Science_Conference/caret.Rnw
+++ b/release_process/Open_Data_Science_Conference/caret.Rnw
@@ -168,7 +168,7 @@ JSS Paper: \href{http://www.jstatsoft.org/v28/i05/paper}{http://www.jstatsoft.or
\vspace{.06in}
-Model List: \href{http://topepo.github.io/caret/bytag.html}{http://topepo.github.io/caret/bytag.html}
+Model List: \href{http://topepo.github.io/caret/train-models-by-tag.html}{http://topepo.github.io/caret/train-models-by-tag.html}
\vspace{.06in}
diff --git a/release_process/Open_Data_Science_Conference/caret.tex b/release_process/Open_Data_Science_Conference/caret.tex
index 618460fa3..efff4f319 100644
--- a/release_process/Open_Data_Science_Conference/caret.tex
+++ b/release_process/Open_Data_Science_Conference/caret.tex
@@ -205,7 +205,7 @@
\vspace{.06in}
-Model List: \href{http://topepo.github.io/caret/bytag.html}{http://topepo.github.io/caret/bytag.html}
+Model List: \href{http://topepo.github.io/caret/train-models-by-tag.html}{http://topepo.github.io/caret/train-models-by-tag.html}
\vspace{.06in}
diff --git a/release_process/talk/caret.Rnw b/release_process/talk/caret.Rnw
index 901f50418..e526ae1e6 100644
--- a/release_process/talk/caret.Rnw
+++ b/release_process/talk/caret.Rnw
@@ -157,7 +157,7 @@ JSS Paper: \href{http://www.jstatsoft.org/v28/i05/paper}{http://www.jstatsoft.or
\vspace{.06in}
-Model List: \href{http://topepo.github.io/caret/bytag.html}{http://topepo.github.io/caret/bytag.html}
+Model List: \href{http://topepo.github.io/caret/train-models-by-tag.html}{http://topepo.github.io/caret/train-models-by-tag.html}
\vspace{.06in}