docs: fix more urls in vignette

Author: mllg

vignettes/batchtools.Rmd

@@ -34,16 +34,16 @@ Furthermore, you may provide functions to list queued/running jobs and to kill j
 Usually you do not have to start from scratch but can just use one of the cluster functions which ship with the package:
 
 
-* Interactive Cluster Functions (default): [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsInteractive), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsInteractive.R)
-* Multicore Cluster Functions: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsMulticore), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsMulticore.R)
-* Socket Cluster Functions: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSocket), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsSocket.R)
-* Makeshift SSH cluster: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSSH), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsSSH.R)
-* Docker Swarm: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsDocker), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsDocker.R)
-* IBM Spectrum Load Sharing Facility (LSF): [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsLSF), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsLSF.R)
-* OpenLava: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsOpenLava), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsOpenLava.R)
-* Univa Grid Engine / Oracle Grid Engine (OGE) / Sun Grid Engine (SGE): [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSGE), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsSGE.R)
-* Slurm: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSlurm), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsSlurm.R)
-* TORQUE/OpenPBS: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsTORQUE), [implementation](https://github.com/mllg/batchtools/blob/master/R/clusterFunctionsTORQUE.R)
+* Interactive Cluster Functions (default): [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsInteractive), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsInteractive.R)
+* Multicore Cluster Functions: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsMulticore), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsMulticore.R)
+* Socket Cluster Functions: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSocket), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsSocket.R)
+* Makeshift SSH cluster: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSSH), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsSSH.R)
+* Docker Swarm: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsDocker), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsDocker.R)
+* IBM Spectrum Load Sharing Facility (LSF): [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsLSF), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsLSF.R)
+* OpenLava: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsOpenLava), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsOpenLava.R)
+* Univa Grid Engine / Oracle Grid Engine (OGE) / Sun Grid Engine (SGE): [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSGE), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsSGE.R)
+* Slurm: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSlurm), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsSlurm.R)
+* TORQUE/OpenPBS: [docs](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsTORQUE), [implementation](https://github.com/mlr-org/batchtools/blob/master/R/clusterFunctionsTORQUE.R)
 
 
 To use the package with the socket cluster functions, you would call the respective constructor [makeClusterFunctionsSocket()](https://mlr-org.github.io/batchtools/reference/makeClusterFunctionsSocket):
@@ -109,7 +109,7 @@ The development of [BatchJobs](https://github.com/tudo-r/BatchJobs/) and [BatchE
 * batchtools does not use SQLite anymore.
   Instead, all the information is stored directly in the registry using [data.tables](https://cran.r-project.org/package=data.table) acting as an in-memory database. As a side effect, many operations are much faster.
 * Nodes do not have to access the registry.
-  [submitJobs()](https://mlr-org.github.io/batchtools/reference/submitJobs) stores a temporary object of type [JobCollection](https://mllg.github.io/batchtools/reference/JobCollection) on the file system which holds all the information necessary to execute a chunk of jobs via [doJobCollection()](https://mllg.github.io/batchtools/reference/doJobCollection) on the node.
+  [submitJobs()](https://mlr-org.github.io/batchtools/reference/submitJobs) stores a temporary object of type [JobCollection](https://mlr-org.github.io/batchtools/reference/JobCollection) on the file system which holds all the information necessary to execute a chunk of jobs via [doJobCollection()](https://mlr-org.github.io/batchtools/reference/doJobCollection) on the node.
   This avoids file system locks because each job accesses only one file exclusively.
 * `ClusterFunctionsMulticore` now uses the parallel package for multicore execution.
 * `ClusterFunctionsSSH` can still be used to emulate a scheduler-like system which respects the work load on the local machine.
@@ -234,15 +234,15 @@ getStatus()
 ```
 The resulting output includes the number of jobs in the registry, how many have been submitted, have started to execute on the batch system, are currently running, have successfully completed, and have terminated due to an R exception.
 After jobs have successfully terminated, we can load their results on the master.
-This can be done in a simple fashion by using either [`loadResult()`](https://mlr-org.github.io/batchtools/reference/loadResult), which returns a single result exactly in the form it was calculated during mapping, or by using [`reduceResults()`](https://mllg.github.io/batchtools/reference/reduceResults), which is a version of `Reduce()` from the base package for registry objects.
+This can be done in a simple fashion by using either [`loadResult()`](https://mlr-org.github.io/batchtools/reference/loadResult), which returns a single result exactly in the form it was calculated during mapping, or by using [`reduceResults()`](https://mlr-org.github.io/batchtools/reference/reduceResults), which is a version of `Reduce()` from the base package for registry objects.
 ```{r}
 waitForJobs()
 mean(sapply(1:10, loadResult))
 reduceResults(function(x, y) x + y) / 10
 ```
 
 If you are absolutely sure that your function works, you can take a shortcut and use *batchtools* in an `lapply` fashion using [`btlapply()`](https://mlr-org.github.io/batchtools/reference/btlapply).
-This function creates a temporary registry (but you may also pass one yourself), calls [`batchMap()`](https://mlr-org.github.io/batchtools/reference/reduceResultsList), wait for the jobs to terminate with [`waitForJobs()`](https://mllg.github.io/batchtools/reference/waitForJobs) and then uses [`reduceResultsList()`](https://mllg.github.io/batchtools/reference/reduceResultsList) to return the results.
+This function creates a temporary registry (but you may also pass one yourself), calls [`batchMap()`](https://mlr-org.github.io/batchtools/reference/reduceResultsList), wait for the jobs to terminate with [`waitForJobs()`](https://mlr-org.github.io/batchtools/reference/waitForJobs) and then uses [`reduceResultsList()`](https://mlr-org.github.io/batchtools/reference/reduceResultsList) to return the results.
 
 ```{r, R.options=list(batchtools.verbose=FALSE)}
 res = btlapply(rep(1e5, 10), piApprox)
@@ -393,7 +393,7 @@ If everything turns out fine, we can proceed with the calculation.
 
 ## Submitting and Collecting Results
 
-To submit the jobs, we call [`submitJobs()`](https://mlr-org.github.io/batchtools/reference/submitJobs) and wait for all jobs to terminate using [`waitForJobs()`](https://mllg.github.io/batchtools/reference/waitForJobs).
+To submit the jobs, we call [`submitJobs()`](https://mlr-org.github.io/batchtools/reference/submitJobs) and wait for all jobs to terminate using [`waitForJobs()`](https://mlr-org.github.io/batchtools/reference/waitForJobs).
 ```{r}
 submitJobs()
 waitForJobs()
@@ -464,12 +464,12 @@ After you have submitted jobs and suspect that something is going wrong, the fir
 getStatus()
 ```
 The status message shows that two of the jobs could not be executed successfully.
-To get the IDs of all jobs that failed due to an error we can use [`findErrors()`](https://mlr-org.github.io/batchtools/reference/findJobs) and to retrieve the actual error message, we can use [`getErrorMessages()`](https://mllg.github.io/batchtools/reference/getErrorMessages).
+To get the IDs of all jobs that failed due to an error we can use [`findErrors()`](https://mlr-org.github.io/batchtools/reference/findJobs) and to retrieve the actual error message, we can use [`getErrorMessages()`](https://mlr-org.github.io/batchtools/reference/getErrorMessages).
 ```{r}
 findErrors()
 getErrorMessages()
 ```
-If we want to peek into the R log file of a job to see more context for the error we can use [`showLog()`](https://mlr-org.github.io/batchtools/reference/showLog) which opens a pager or use [`getLog()`](https://mllg.github.io/batchtools/reference/showLog) to get the log as character vector:
+If we want to peek into the R log file of a job to see more context for the error we can use [`showLog()`](https://mlr-org.github.io/batchtools/reference/showLog) which opens a pager or use [`getLog()`](https://mlr-org.github.io/batchtools/reference/showLog) to get the log as character vector:
 ```{r}
 tail(getLog(id = 9))
 ```
@@ -484,21 +484,21 @@ grepLogs(pattern = "simple", ignore.case = TRUE)
 
 ## On the Local System
 
-1. Create a Registry with [`makeRegistry()`](https://mlr-org.github.io/batchtools/reference/makeRegistry) (or [`makeExperimentRegistry()`](https://mllg.github.io/batchtools/reference/makeExperimentRegistry)) or load an existing from the file system with [`loadRegistry()`](https://mllg.github.io/batchtools/reference/loadRegistry).
-2. Define computational jobs with [`batchMap()`](https://mlr-org.github.io/batchtools/reference/batchMap) or [`batchReduce()`](https://mllg.github.io/batchtools/reference/batchReduce) if you used [`makeRegistry()`](https://mllg.github.io/batchtools/reference/makeRegistry) or define with [`addAlgorithm()`](https://mllg.github.io/batchtools/reference/addAlgorithm), [`addProblem()`](https://mllg.github.io/batchtools/reference/addProblem) and [`addExperiments()`](https://mllg.github.io/batchtools/reference/addExperiments) if you started with [`makeExperimentRegistry()`](https://mllg.github.io/batchtools/reference/makeExperimentRegistry).
+1. Create a Registry with [`makeRegistry()`](https://mlr-org.github.io/batchtools/reference/makeRegistry) (or [`makeExperimentRegistry()`](https://mlr-org.github.io/batchtools/reference/makeExperimentRegistry)) or load an existing from the file system with [`loadRegistry()`](https://mlr-org.github.io/batchtools/reference/loadRegistry).
+2. Define computational jobs with [`batchMap()`](https://mlr-org.github.io/batchtools/reference/batchMap) or [`batchReduce()`](https://mlr-org.github.io/batchtools/reference/batchReduce) if you used [`makeRegistry()`](https://mlr-org.github.io/batchtools/reference/makeRegistry) or define with [`addAlgorithm()`](https://mlr-org.github.io/batchtools/reference/addAlgorithm), [`addProblem()`](https://mllg.github.io/batchtools/reference/addProblem) and [`addExperiments()`](https://mllg.github.io/batchtools/reference/addExperiments) if you started with [`makeExperimentRegistry()`](https://mllg.github.io/batchtools/reference/makeExperimentRegistry).
    It is advised to test some jobs with [`testJob()`](https://mlr-org.github.io/batchtools/reference/testJob) in the interactive session and with `testJob(external = TRUE)` in a separate R process.
    Note that you can add additional jobs if you are using an [`ExperimentRegistry`](https://mlr-org.github.io/batchtools/reference/makeExperimentRegistry).
 3. If required, query the data base for job ids depending on their status, parameters or tags (see [`findJobs()`](https://mlr-org.github.io/batchtools/reference/findJobs)).
-   The returned tables can easily be combined in a set-like fashion with data base verbs: union ([`ojoin()`](https://mlr-org.github.io/batchtools/reference/JoinTables) for outer join), intersect ([`ijoin()`](https://mllg.github.io/batchtools/reference/JoinTables) for inner join), difference ([`ajoin()`](https://mllg.github.io/batchtools/reference/JoinTables) for anti join).
+   The returned tables can easily be combined in a set-like fashion with data base verbs: union ([`ojoin()`](https://mlr-org.github.io/batchtools/reference/JoinTables) for outer join), intersect ([`ijoin()`](https://mlr-org.github.io/batchtools/reference/JoinTables) for inner join), difference ([`ajoin()`](https://mlr-org.github.io/batchtools/reference/JoinTables) for anti join).
 4. Submit jobs with [`submitJobs()`](https://mlr-org.github.io/batchtools/reference/submitJobs). You can specify job resources here.
    If you have thousands of fast terminating jobs, you want to [`chunk()`](https://mlr-org.github.io/batchtools/reference/chunk) them first.
-   If some jobs already terminated, you can estimate the runtimes with [`estimateRuntimes()`](https://mlr-org.github.io/batchtools/reference/estimateRuntimes) and chunk jobs into heterogeneous groups with [`lpt()`](https://mllg.github.io/batchtools/reference/chunk) and [`binpack()`](https://mllg.github.io/batchtools/reference/chunk).
+   If some jobs already terminated, you can estimate the runtimes with [`estimateRuntimes()`](https://mlr-org.github.io/batchtools/reference/estimateRuntimes) and chunk jobs into heterogeneous groups with [`lpt()`](https://mlr-org.github.io/batchtools/reference/chunk) and [`binpack()`](https://mlr-org.github.io/batchtools/reference/chunk).
 5. Monitor jobs. [`getStatus()`](https://mlr-org.github.io/batchtools/reference/getStatus) gives a summarizing overview.
-   Use [`showLog()`](https://mlr-org.github.io/batchtools/reference/showLog) and [`grepLogs()`](https://mllg.github.io/batchtools/reference/grepLogs) to investigate log file.
+   Use [`showLog()`](https://mlr-org.github.io/batchtools/reference/showLog) and [`grepLogs()`](https://mlr-org.github.io/batchtools/reference/grepLogs) to investigate log file.
    Run jobs in the currently running session with [`testJob()`](https://mlr-org.github.io/batchtools/reference/testJob) to get a `traceback()`.
 6. Collect (partial) results. [`loadResult()`](https://mlr-org.github.io/batchtools/reference/loadResult) retrieves a single result from the file system.
    [`reduceResults()`](https://mlr-org.github.io/batchtools/reference/reduceResults) mimics `Reduce()` and allows to apply a function to many files in an iterative fashion.
-   [`reduceResultsList()`](https://mlr-org.github.io/batchtools/reference/reduceResultsList) and [`reduceResultsDataTable()`](https://mllg.github.io/batchtools/reference/reduceResultsList) collect results into a `list` or `data.table`, respectively.
+   [`reduceResultsList()`](https://mlr-org.github.io/batchtools/reference/reduceResultsList) and [`reduceResultsDataTable()`](https://mlr-org.github.io/batchtools/reference/reduceResultsList) collect results into a `list` or `data.table`, respectively.
 
 ```{r,echo=FALSE}
 knitr::include_graphics("function_overview.png", auto_pdf = TRUE)
@@ -507,7 +507,7 @@ knitr::include_graphics("function_overview.png", auto_pdf = TRUE)
 ## On Multiple Systems
 
 Most users develop and prototype their experiments on a desktop box in their preferred IDE and later deploy to a large computing cluster.
-This can be done by prototyping locally ([`testJob()`](https://mlr-org.github.io/batchtools/reference/testJob) or submit subsets via [`submitJobs()`](https://mllg.github.io/batchtools/reference/submitJobs)).
+This can be done by prototyping locally ([`testJob()`](https://mlr-org.github.io/batchtools/reference/testJob) or submit subsets via [`submitJobs()`](https://mlr-org.github.io/batchtools/reference/submitJobs)).
 To deploy to the cluster, just copy the file directory (as reported by `reg$file.dir`) to the remote system.
 Next, log in on the cluster (typically via `ssh`), `cd` to the copied directory and call `loadRegistry("<file.dir.on.remote">, "<work.dir.on.remote>", writeable = TRUE)`.
 This function will (a) source the local configuration file so that you can talk to the cluster (verify by checking the output of `reg$cluster.functions`) and (b) adjust the paths to the new system if argument `update.paths` is set.