Update README.md

README.md

@@ -80,55 +80,7 @@ results = matchingR::one2one(proposerPref = prefM, reviewerPref = prefW)
 ```
 The original Gale-Shapley algorithm can be easily modified to accommodate unequal numbers of participants on each side of the market as well as one-to-many matchings, i.e. the matching of workers to multi-worker firms or the matching of students to colleges.
 
-### Examples
-#### Marriage Market
-The following is an example of `one2one` with different numbers of participants on each side of the market. By construction, 500 men will remain unmatched.
-```{r}
-# set seed
-set.seed(1)
-
-# set number of men
-nmen = 2500
-
-# set number of women
-nwomen = 2000
-
-# generate preferences
-uM = matrix(runif(nmen*nwomen), nrow=nwomen, ncol=nmen) 
-uW = matrix(runif(nmen*nwomen), nrow=nmen, ncol=nwomen) 
-
-# male optimal matching
-resultsM = matchingR::one2one(uM, uW)
-# female optimal matching
-resultsW = matchingR::one2one(uW, uM)
-
-# check if matchings are stable
-matchingR::checkStability(uM, uW, resultsM$proposals, resultsM$engagements)
-matchingR::checkStability(uW, uM, resultsW$proposals, resultsW$engagements)
-```
-
-#### College Admissions Problem
-The following is an example of `one2many` where 1,000 students get matched to 400 colleges, where each college has two slots. By construction, 200 students will remain unmatched.
-```{r}
-# set seed
-set.seed(1)
-
-# set number of students
-nstudents = 1000
-
-# set number of colleges
-ncolleges = 400
-
-# generate preferences
-uStudents = matrix(runif(ncolleges*nstudents), nrow=ncolleges, ncol=nstudents) 
-uColleges = matrix(runif(nstudents*ncolleges), nrow=nstudents, ncol=ncolleges) 
-
-# worker optimal matching
-results = matchingR::one2many(uStudents, uColleges, slots=2)
-
-# check if matching is stable
-matchingR::checkStability(uStudents, uColleges, results$proposals, results$engagements)
-```
+For additional examples, take a look at the [vignette](http://jtilly.io/matchingR/matchingR-intro.html) or at the [examples page](EXAMPLES.md).
 
 ## Irving's Stable Roommate Algorithm: How does it work?