diff --git a/R/em.mixnorm.R b/R/em.mixnorm.R
index 98264e3..483cc03 100644
--- a/R/em.mixnorm.R
+++ b/R/em.mixnorm.R
@@ -5,6 +5,7 @@
 #' 
 #' @author Tyler Hunt \email{tyler@@psychoanalytix.com}
 #' @export
+
 em.mixnorm <-
 function (x,k){
 	p = rep(1/k,k)
diff --git a/R/jackknife.R b/R/jackknife.R
new file mode 100644
index 0000000..9c2a209
--- /dev/null
+++ b/R/jackknife.R
@@ -0,0 +1,37 @@
+#' Resamples Data using the Jackknife Method
+#' 
+#' @description 
+#' This function is used for estimating standard errors when the distribution is not know.
+#'  
+#' @param x a vector
+#' @param t estimation of parameter
+#' 
+#' @return est orignial estimation of parameter
+#' @return jkest jackknife estimation of parameter
+#' @return jkvar jackknife estimation of variance
+#' @return jkbias jackknife estimate of biasness of parameter
+#' @return jkbiascorr bias corrected parameter estimate
+#' 
+#' @author Damon McCafferty \email{damon.mccafferty@@economics.utah.edu}
+#' 
+#' @example x = runif(10, 0, 1)
+#' mean(x)
+#' jackknife(x,mean)
+#' 
+#' @export
+
+jackknife<-function (x,t) 
+{
+  n=length(x)
+  jk=rep(NA,n)
+  
+  for (i in 1:n)
+    {
+    jk[i]=t(x[-i])
+  jkest=mean(jk)
+  jkvar=(n-1)/n*sum((jk-jkest)^2)
+  jkbias=(n-1)*(jkest-t(x))
+  jkbiascorr=n*t(x)-(n-1)*jkest
+    }
+  list(est=t(x), jkest=jkest, jkvar=jkvar, jkbias=jkbias, jkbiascorr=jkbiascorr)
+}