Merge branch 'master' into develop

* master:
  fix README
  fix README
  update readme
  Update README with example
  update version nr
  Add LICENSE

DESCRIPTION

@@ -2,25 +2,23 @@ Package: nsoptim
 Type: Package
 Title: Utility Library to Implement Non-Smooth Optimization in C++
 Version: 0.1.0
-Date: 2019-01-30
+Date: 2019-10-24
 Authors@R: person("David", "Kepplinger", , "david.kepplinger@gmail.com",
                   role = c("aut", "cre"))
 Copyright: See the file COPYRIGHTS for copyright details on some of the
     functions and algorithms used.
 Encoding: UTF-8
 SystemRequirements: C++11
-URL: https://github.com/dakep/nsoptim-rpkg
-BugReports: https://github.com/dakep/nsoptim-rpkg/issues
-Description: A C++ library to help solve non-smooth optimziation problems
-             in R packages.
+URL: https://gitlab.math.ubc.ca/dakep/nsoptim
+BugReports: https://gitlab.math.ubc.ca/dakep/nsoptim/issues
+Description: A C++ template library for non-smooth optimziation.
 Depends:
     R (>= 3.4.0)
 Imports:
     Rcpp
 LinkingTo:
     Rcpp,
-    RcppArmadillo (>= 0.9.100),
-    testthat
+    RcppArmadillo (>= 0.9.100)
 Suggests:
     testthat (>= 2.1.0)
 License: MIT + file LICENSE

README.md

@@ -11,5 +11,39 @@ Due to the early stage of the library, the interface might change considerably i
 The C++ header files are in [inst/include](inst/include) and can be used from within other R packages by adding `nsoptim`
 to the `LinkingTo` field in the package's DESCRIPTION file.
 
+## Example Usage
+
+```cpp
+
+#include <memory>
+#include <nsoptim.hpp>  // This also includes the armadillo library
+
+// Alias for a linearized ADMM optimizer operating on the standard LS regression loss and an EN penalty using a dense coefficient vector.
+using LinearizedAdmmOptimizer = nsoptim::LinearizedAdmmOptimizer<nsoptim::LsLoss, nsoptim::EnPenalty, nsoptim::RegressionCoefficients<arma::vec>>;
+
+typename LinearizedAdmmOptimizer::Coefficients Foo() {
+  // Generate dummy data with 100 observations and 10 predictors.
+  auto data = std::make_shared<nsoptim::PredictorResponseData>(arma::randn(100, 10), arma::randn(10));
+
+  nsoptim::LsLoss loss(data);  // Create a LS loss function object with the generated data
+  nsoptim::EnPenalty penalty1(0.5, 2.4)  // Create an EN penalty function object with alpha=0.5 and lambda=2.4
+  nsoptim::EnPenalty penalty2(0.5, 1.5)  // Create an EN penalty function object with alpha=0.5 and lambda=1.5
+
+  // Create an optimizer for the given loss and penalty function, using default parameters for the ADMM algorithm.
+  LinearizedAdmmOptimizer optim(loss, penalty1);
+
+  // Compute the optimum for `penalty1`, starting at the 0-vector.
+  typename LinearizedAdmmOptimizer::Optimum optimum = optim.Optimize();
+
+  // Change the penalty to `penalty2`.
+  optim.penalty(penalty2);
+  // Compute the optimum for `penalty2`, starting at the optimum for `penalty1`.
+  typename LinearizedAdmmOptimizer::Optimum optimum = optim.Optimize();
+
+  // Only return the coefficients.
+  return optimum.coefs;
+}
+```
+
 ## Documentation
-The documentation for the library is a work in progress. Currently, source code files include doxygen-style comments.
+The documentation for the library is available at https://nsoptim.readthedocs.org.