diff --git a/.travis.yml b/.travis.yml
--- a/.travis.yml
+++ b/.travis.yml
@@ -17,7 +17,7 @@
 # - CABALVER=1.18 GHCVER=7.8.1  # see note about Alex/Happy for GHC >= 7.8
 # - CABALVER=1.18 GHCVER=7.8.2
  - CABALVER=1.18 GHCVER=7.8.3
- - CABALVER=1.22 GHCVER=7.10.1
+ - CABALVER=1.22 GHCVER=7.10.2
 # - CABALVER=head GHCVER=head   # see section about GHC HEAD snapshots
 
 # Note: the distinction between `before_install` and `install` is not important.
@@ -31,12 +31,12 @@
  - cabal --version
  - echo "$(ghc --version) [$(ghc --print-project-git-commit-id 2> /dev/null || echo '?')]"
  - travis_retry cabal update
- - cabal install --only-dependencies --enable-tests --enable-benchmarks
+ - cabal install --only-dependencies --enable-tests --enable-benchmarks -fBuildSamplePrograms
 
 # Here starts the actual work to be performed for the package under test; any command which exits with a non-zero exit code causes the build to fail.
 script:
  - if [ -f configure.ac ]; then autoreconf -i; fi
- - cabal configure --enable-tests --enable-benchmarks -v2  # -v2 provides useful information for debugging
+ - cabal configure --enable-tests --enable-benchmarks -fBuildSamplePrograms -v2  # -v2 provides useful information for debugging
  - cabal build   # this builds all libraries and executables (including tests/benchmarks)
  - cabal test
  - cabal check
diff --git a/nonlinear-optimization-ad.cabal b/nonlinear-optimization-ad.cabal
--- a/nonlinear-optimization-ad.cabal
+++ b/nonlinear-optimization-ad.cabal
@@ -2,7 +2,7 @@
 -- further documentation, see http://haskell.org/cabal/users-guide/
 
 name:                nonlinear-optimization-ad
-version:             0.2.0
+version:             0.2.1
 synopsis:            Wrapper of nonlinear-optimization package for using with AD package
 description:         Wrapper of nonlinear-optimization package for using with AD package
 homepage:            https://github.com/msakai/nonlinear-optimization-ad
@@ -25,6 +25,11 @@
   type:     git
   location: git://github.com/msakai/nonlinear-optimization-ad.git
 
+flag BuildSamplePrograms
+  Description: build sample programs
+  Default: False
+  Manual: True
+
 library
   exposed-modules:     Numeric.Optimization.Algorithms.HagerZhang05.AD
   build-depends:
@@ -42,3 +47,17 @@
     TypeFamilies
     CPP
 
+-- Sample Programs
+
+Executable LinearRegression
+  If !flag(BuildSamplePrograms)
+    Buildable: False
+  Main-is: LinearRegression.hs
+  HS-Source-Dirs: samples
+  Build-Depends:
+    base,
+    csv,
+    nonlinear-optimization-ad
+  Default-Language: Haskell2010
+  Other-extensions:
+    TypeFamilies
diff --git a/samples/LinearRegression.hs b/samples/LinearRegression.hs
--- a/samples/LinearRegression.hs
+++ b/samples/LinearRegression.hs
@@ -1,26 +1,37 @@
+{-# LANGUAGE GADTs #-}
+{-# OPTIONS_GHC -Wall #-}
 module Main where
 
-import Control.Monad
-import qualified Data.Vector as V
-import Numeric.AD
 import qualified Numeric.Optimization.Algorithms.HagerZhang05.AD as CG
 import Text.Printf
 import qualified Text.CSV as CSV
 
 main :: IO ()
 main = do
-  Right csv <- CSV.parseCSVFromFile "galton.csv"
+  Right csv <- CSV.parseCSVFromFile "samples/galton.csv"
   let samples :: [(Double, Double)]
       samples = [(read parent, read child) | [child,parent] <- tail csv]      
       -- hypothesis
       h [theta0,theta1] x = theta0 + theta1 * x
       -- cost function
-      cost theta = mse [(auto x, auto y) | (x,y) <- samples] (h theta)
+      -- cost :: (Fractional m, CG.Mode m, CG.Scalar m ~ Double) => [m] -> m]
+      cost theta = mse [(CG.auto x, CG.auto y) | (x,y) <- samples] (h theta)
       params   = CG.defaultParameters{ CG.printFinal = True, CG.printParams = True, CG.verbose = CG.Verbose }
       grad_tol = 0.0000001
-  (theta, result, stat) <- CG.optimize params grad_tol [0,0] cost
-  print theta
+  (theta@[theta0,theta1], _result, _stat) <- CG.optimize params grad_tol [0,0] cost
+  printf "y = %f x + %f\n" theta1 theta0
+  printf "MSE = %f\n" (mse samples (h theta))
+  printf "R^2 = %f\n" (r2 samples (h theta))
 
 -- mean squared error
 mse :: Fractional y => [(x,y)] -> (x -> y) -> y
-mse samples h = sum [(h x - y)^(2::Int) | (x,y) <- samples] / fromIntegral (length samples)
+mse samples h = mean [(h x - y)^(2::Int) | (x,y) <- samples]
+
+r2 :: Fractional y => [(x,y)] -> (x -> y) -> y
+r2 samples h = 1 - mse samples h / sum [(y - ym)^(2::Int) | y <- ys]
+  where
+    ys = map snd samples
+    ym = mean ys
+
+mean :: Fractional x => [x] -> x
+mean xs = sum [x | x <- xs] / fromIntegral (length xs)
diff --git a/src/Numeric/Optimization/Algorithms/HagerZhang05/AD.hs b/src/Numeric/Optimization/Algorithms/HagerZhang05/AD.hs
--- a/src/Numeric/Optimization/Algorithms/HagerZhang05/AD.hs
+++ b/src/Numeric/Optimization/Algorithms/HagerZhang05/AD.hs
@@ -3,9 +3,6 @@
 module Numeric.Optimization.Algorithms.HagerZhang05.AD
   ( -- * Main function
     optimize
-    -- ** Kinds of function types
-  , Simple
-  , Mutable
     -- * Result and statistics
   , Result(..)
   , Statistics(..)
@@ -18,6 +15,8 @@
   , EstimateError(..)
     -- * Technical parameters
   , TechParameters(..)
+    -- * Re-export
+  , Mode (..)
   ) where
 
 import Prelude hiding (mapM)