Methods & CRAN packages to predict probability using neural networks or others machine learning algorithms I have a medical database containing 7 input variables (4 are binary) and a binary outcome variable (Survival: yes/no). My objective is to train and test an algorithm that predict probability of survival (not a binary output). 
I used nnet package. But it returned for me a binary output (using "raw" and "class" type).
Which method can i use? 
Is there any R CRAN Packages to predict probability of survival using neural networks or others machine learning algorithms? 
Thank you very much
 A: I like to use the caret package for predictive modeling, as it provided a unified interface to a variety of algorithms, including nnet.  It's very easy to get predicted probabilities for any model that supports them, neural networks included:
set.seed(42)
require(caret)
model <- train(Species~., data=iris, method='nnet', 
               trControl=trainControl(method='cv'))
model
probs <- predict(model, iris, type='prob')
head(probs)
     setosa versicolor    virginica
1 0.9881512 0.01143536 0.0004134043
2 0.9839565 0.01550938 0.0005341130
3 0.9866016 0.01293839 0.0004599962
4 0.9820605 0.01735453 0.0005849775
5 0.9884653 0.01113064 0.0004040207
6 0.9875201 0.01204961 0.0004302507

A: I would second the recommendation of an "ensemble" method such as Random Forests. There is a variant of RF, "randomSurvivalForest" that is specific to survival analysis. Here is a link to the the R manual for the package randomSurvivalForest.  
A: you can try for example the glm function with the family=binomial and the logit link (or the probit) doing a logistic regression, which outputs are probabilitys.. Here you have a link
logistic regression R
You also can try with trees or with "ensembles" of trees , boosting, bagging or random forest wuth packages like rpart ,randomForest etc If your dependent variable is 1/0 you will have outcomes with estimated proportions.
CART trees R 
randomForest CRAN
.
A: You might also want to look at package RSNNS, which provides an R interface to the package SNNS, with ample functionality for general neural networks.
