Minor additions to the docs (#95)

* Clarify output to confidence interval calculation
* Update docstring for `confint` (except Student)
* Update docstring (use non-deprecated function)

README.md

@@ -81,7 +81,9 @@ estimated bias and standard error of our statistic.
 ```
 
 Furthermore, we can estimate confidence intervals (CIs) for our statistic of
-interest, based on the bootstrapped samples.
+interest, based on the bootstrapped samples. `confint` returns a `Tuple` of `Tuples`,
+where each `Tuple` is of the form `(statistic_value, upper_confidence_bound, lower_confidence_bound)`.
+A confidence interval is returned for each variable in the bootstrap model.
 
 ```julia
   ## calculate 95% confidence intervals

src/bootsampling.jl

@@ -163,7 +163,7 @@ nrun(bs::BootstrapSample) = nrun(sampling(bs))
 Return the statistic function of a `BootstrapSample`
 
 ```julia
-bs = bootstrap(randn(20), mean, BasicSampling(100))
+bs = bootstrap(mean, randn(20), BasicSampling(100))
 statistic(bs)
 ```
 

src/confint.jl

@@ -77,6 +77,13 @@ StudentConfInt() = StudentConfInt(_level)
 
 level(cim::ConfIntMethod) = cim.level
 
+"""
+    confint(bs::BootstrapSample, cim::ConfIntMethod, [i::Int])
+
+Return the confidence interval for the bootstrapped sample `bs` with the
+method defined by `cim`. If `i` is specified, only the confidence interval
+for the `ith` variable is returned.
+"""
 function confint(bs::BootstrapSample, cim::ConfIntMethod)
     tuple([confint(bs, cim, i) for i in 1:nvar(bs)]...)
 end