Tidy and fix spelling in documentation

Incorrect spelling of Breiman corrected
Added Breiman reference to README
Add URLs to package docs
Bump version number

DESCRIPTION

 Package: literanger
 Title: Random Forests for Multiple Imputation Based on 'ranger'
-Version: 0.0.1
+Version: 0.0.2
 Authors@R:
     c(person("Stephen", "Wade",
              email="stephematician@gmail.com",

NEWS.md

-# literanger 0.0.1
+literanger NEWS
+===============
+
+# version 0.0.2
+
+Performance enhancements
+-   Faster (correct) test for number of candidate values in node splitting.
+-   Remove lock on log gamma (beta splitting rule).
+
+Bug-fixes
+-   Fix container overrun and incorrect (unweighted) sampling without
+    replacement.
+
+
+# version 0.0.1
 
 This is the initial release of literanger, a refactoring and adaptation of the
 ranger package <https://github.com/imbs-hl/ranger> for random forests. The
@@ -11,7 +25,6 @@ for missing data imputation in the presence of interaction effects.
 _Computational statistics & data analysis_, 72, 92-104.
 
 Currently supports:
-
 -   Classification and regression trees/forests.
 -   Prediction types:
     -   Conventional 'bagged' prediction (most frequent value or mean).

R/literanger-package.R

@@ -136,6 +136,8 @@
 #'
 #' @references
 #'
+#' -   Breiman, L. (2001). Random forests. _Machine Learning_, 45, 5-32.
+#'     \doi{10.1023/A:1010933404324}.
 #' -   Cieslak, D. A., Hoens, T. R., Chawla, N. V., & Kegelmeyer, W. P. (2012).
 #'     Hellinger distance decision trees are robust and skew-insensitive. _Data
 #'     Mining and Knowledge Discovery_, 24, 136-158.

README.md

@@ -10,7 +10,7 @@ _Stephen Wade_
 `literanger` is an adaption of the [`ranger`][ranger_cran] R package for
 training and predicting from random forest models within multiple imputation
 algorithms. `ranger` is a fast implementation of random forests
-([Breiman, 2001][brieman2001_doi]) or recursive partitioning, particularly
+([Breiman, 2001][breiman2001_doi]) or recursive partitioning, particularly
 suited for high dimensional data ([Wright et al, 2017][wright2017_doi]).
 `literanger` enables random forests to be embedded in the fully conditional
 specification framework for multiple imputation known as 'Multiple Imputation
@@ -116,7 +116,7 @@ forests for high dimensional data in C++ and R. _Journal of Statistical
 Software_, 77(i01), pp. 1-17.
 [doi:10.18637/jss.v077.i01](https://doi.org/10.18637/jss.v077.i01).
 
-[brieman2001_doi]: https://doi.org/10.1023/A:1010933404324
+[breiman2001_doi]: https://doi.org/10.1023/A:1010933404324
 [doove2014_doi]: https://doi.org/10.1016/j.csda.2013.10.025
 [vanbuuren2007_doi]: https://doi.org/10.1177/0962280206074463
 [wright2017_doi]: https://doi.org/10.18637/jss.v077.i01

man/literanger-package.Rd

@@ -112,6 +112,8 @@ http://www.imbs-luebeck.d
 
 \references{
 \itemize{
+\item Breiman, L. (2001). Random forests. \emph{Machine Learning}, 45, 5-32.
+\doi{10.1023/A:1010933404324}.
 \item Cieslak, D. A., Hoens, T. R., Chawla, N. V., & Kegelmeyer, W. P. (2012).
 Hellinger distance decision trees are robust and skew-insensitive. \emph{Data
 Mining and Knowledge Discovery}, 24, 136-158.
@@ -139,6 +141,14 @@ variable selection for random survival forests using maximally selected
 rank statistics. \emph{Statistics in medicine}, 36(8), 1272-1284.
 \doi{10.1002/sim.7212}.
 }
+}
+\seealso{
+Useful links:
+\itemize{
+  \item \url{https://github.com/stephematician/literanger}
+  \item Report bugs at \url{https://github.com/stephematician/literanger/issues}
+}
+
 }
 \author{
 Stephen Wade \href{mailto:stephematician@gmail.com}{stephematician@gmail.com}, Marvin N Wright (original