The weights-based algorithm for record linkage:

library(RecordLinkage)
 data("RLdata500")
 
 # weight calculation
 rec.pairs <- compare.dedup(RLdata500
 ,blockfld = list(1, 5:7)
 ,strcmp = c(2,3,4)
 ,strcmpfun = levenshteinSim)
 
 pairs.weights <- epiWeights(rec.pairs)
 hist(pairs.weights$Wdata)
 
 summary(pairs.weights)
 
 weights.df<-getPairs(pairs.weights)
 head(weights.df)
 
 # Classification
 pairs.classify <- emClassify(pairs.weights, threshold.upper = 0.5, threshold.lower = 0.3)
 
 # View the matches
 final.results <- pairs.classify$pairs
 final.results$weight <- pairs.classify$Wdata
 final.results$links <- pairs.classify$prediction
 head(final.results)
 
 counts <- table(final.results$links)
 barplot(counts, main="Link Distribution",
 xlab="Link Types")
 
 # Final output to our customer
 weights.df.srow <-getPairs( pairs.weights, single.rows = TRUE)
 final.matches <- final.results[final.results$links == 'L',]
 
 final <- merge(final.matches, weights.df.srow)
 final <- subset(final, select = -c(fname_c1.2, fname_c2.2, lname_c1.2, lname_c2.2, by.2, bm.2, bd.2, weight))
 head(final)

This completes the weights-based method.