As I recently tried several modeling techniques in R, I would like to share some of these, with a focus on linear regression.
Disclaimer: the code lines below work, but I would not suggest that they are the most efficient way to deal with this kind of data (as a matter of fact, all of them score slightly below 80% accuracy on the Kaggle datasets). Moreover, there are not always the most efficient way to implement a given model.
I see it as a way to quickly test several frameworks without going into details.
The column names used in the examples are from the Titanic track on Kaggle.
Generalized linear models
titanic.glm < glm (survived ~ pclass + sex + age + sibsp, data = titanic, family = binomial(link=logit))
glm.pred < predict.glm(titanic.glm, newdata = titanicnew, na.action = na.pass, type = "response")
cat(glm.pred)`
 ‘cat’ actually prints the output
 One might want to use the na.action switch to be able to deal with incomplete data (as in the Titanic dataset) : na.action=na.pass
Mixed GAM (Generalized Additive Models) Computation Vehicle
The commands are a little less obvious:
library(mgcv)
titanic.gam < gam (survived ~ pclass + sex + age + sibsp, data = titanic, family=quasibinomial(link = "logit"), method="GCV.Cp")
gam.pred < predict.gam(titanic.gam, newdata = titanicnew, na.action = na.pass, type = "response")
gam.pred < ifelse(gam.pred <= 0, 0, 1)`
 Quasibinomial, Poisson, Gamma and Gaussian are usually usable alternatives with both libraries.
 The method used (GCV.Cp) is select by crossvalidation. Others are available.
 The ifelse threshold is not necessarily 0. Moreover, this kind of conversion is not always required.
Robust statistics
library(robustbase)
titanic.glm < glmrob(survived ~ pclass + sex + age + sibsp + parch , data = titanic, family = binomial)`
 use predict.glmrob for prediction.
Link to robustbase package manual.
Partition trees
I mentioned this part in my last post about R.
library(rpart)
titanic.tree < rpart(survived ~ pclass + sex + age + sibsp, data = titanic, method="anova")
tree.pred < predict(titanic.tree, newdata = titanicnew)`
Support vector machines
There are many other implementations available.
library(e1071)
titanic.svm < svm(formula = survived ~ pclass + sex + age + sibsp, data = titanic, gamma = 10^1, cost = 10^1)
pred.svm < predict (titanic.svm, data = titanicnew)`
 optional :
decision.values = TRUE

A formula in order to automatically tune the result (certainly not the most accurate):
:::r
tuned < tune.svm(survived ~ pclass + sex + age + sibsp, data = titanic, gamma = 10^(5:5), cost = 10^(2:2), kernel = "polynomial")
Bagging (in this case bagging of a tree)
library(ipred)
titanic.bt < bagging(survived ~ pclass + sex + age + sibsp, data = titanic, nbagg=20,coob=T)
titanic.bt < ipredbag(survived ~ pclass + sex + age + sibsp, data = titanic, nbagg=20,coob=T)
exp < predict(titanic.bt, type="class")`
 Sampling : nbagg bootstrap samples are drawn and a tree is constructed for each of them.
 Coob : outofbag estimate of the error rate is computed.
Random forests
library(randomForest)
titanic.rf < randomForest(survived ~ pclass + sex + age + sibsp, data = titanic, importance=T, na.action=NULL)
rf.pred < predict(titanic.rf, data = titanicnew, type="response")`
 The mtry and ntree options can be useful here.