title: Incorporating observation weights in the randomForest package
body: <p>How can I use the R <code>randomForest</code> package with observation weights? I know that there is no such option in this package. I have 2 questions:</p>
  
  <ol>
  <li><p>Are there any solutions to this problem using <code>randomForest</code> package? At this moment I'm drawing samples from data with weights as the probability so I can at least simulate it:</p>
  
  <pre><code>m = dim(data)[1]
  sample(data, m, replace=TRUE, prob=weights)
  </code></pre>
  
  <p>It works are there other (better) solutions?</p></li>
  <li><p>Are there any alternatives to the <code>randomForest</code> package. I found the <code>party</code> package (cforest) but it's terrible in terms of memory management (or I cannot use it the way I use <code>randomForest</code> package). I have around 200k observations and 30-40 variables.</p></li>
  </ol>
  
  <p>EDIT:</p>
  
  <p>Sorry for not clarifying details. I'm using the <code>randomForest</code> package for regression problem (not classification). It is a time series and every observation has its weight. Later on this weight is used to determine the model performance across test observations. The y variable is continuous.</p>