diff --git a/lib/statsample/bivariate.rb b/lib/statsample/bivariate.rb
index b84f915..d0e1572 100644
--- a/lib/statsample/bivariate.rb
+++ b/lib/statsample/bivariate.rb
@@ -156,15 +156,14 @@ def covariance_matrix_optimized(ds)
       # Order of rows and columns depends on Dataset#fields order
       
       def covariance_matrix(ds)
-        vars,cases=ds.fields.size,ds.cases
+        vars,cases = ds.vectors.size, ds.nrows
         if !ds.has_missing_data? and Statsample.has_gsl? and prediction_optimized(vars,cases) < prediction_pairwise(vars,cases)
           cm=covariance_matrix_optimized(ds)
         else
           cm=covariance_matrix_pairwise(ds)
-          
         end
         cm.extend(Statsample::CovariateMatrix)
-        cm.fields=ds.fields
+        cm.fields=ds.vectors.to_a
         cm
       end
       
@@ -243,14 +242,19 @@ def correlation_matrix_pairwise(ds)
       
       # Retrieves the n valid pairwise.
       def n_valid_matrix(ds)
-        ds.collect_matrix do |row,col|
-          if row==col
-            ds[row].valid_data.size
-          else
-            rowa,rowb=Statsample.only_valid_clone(ds[row],ds[col])
-            rowa.size
+        vectors = ds.vectors.to_a
+        m = vectors.collect do |row|
+          vectors.collect do |col|
+            if row==col
+              ds[row].only_valid.size
+            else
+              rowa,rowb = Statsample.only_valid_clone(ds[row],ds[col])
+              rowa.size
+            end
           end
         end
+
+        Matrix.rows m
       end
       
       # Matrix of correlation probabilities.
@@ -384,8 +388,8 @@ def sum_of_codeviated(v1,v2)
       # Report the minimum number of cases valid of a covariate matrix
       # based on a dataset
       def min_n_valid(ds)
-        min=ds.cases
-        m=n_valid_matrix(ds)
+        min = ds.nrows
+        m   = n_valid_matrix(ds)
         for x in 0...m.row_size
           for y in 0...m.column_size
             min=m[x,y] if m[x,y] < min
@@ -393,8 +397,6 @@ def min_n_valid(ds)
         end
         min
       end
-      
-      
     end
   end
 end
diff --git a/lib/statsample/daru.rb b/lib/statsample/daru.rb
index 029a0fc..8764b9b 100644
--- a/lib/statsample/daru.rb
+++ b/lib/statsample/daru.rb
@@ -26,13 +26,7 @@ def to_multiset_by_split_one_field(field)
       #puts "Ingreso a los dataset"
       ms.datasets.each do |k,ds|
         ds.update
-        # puts "idx #{self[field].index_of(k)}"
         ds.rename self[field].index_of(k)
-        # ds.vectors.each do |k1,v1|
-        #   v1.type   = self[k1].type
-        #   v1.name   = self[k1].name
-        #   v1.labels = self[k1].to_hash
-        # end
       end
 
       ms
@@ -69,12 +63,6 @@ def to_multiset_by_split_multiple_fields(*fields)
             self[f].index_of(sk)
           end.join("-")
         )
-
-        # ds.vectors.each do |k1,v1|
-        #   v1.type   = ds[k1].type
-        #   v1.name   = ds[k1].name
-        #   v1.labels = ds[k1].to_hash
-        # end
       end
       ms
     end
diff --git a/lib/statsample/regression/multiple/baseengine.rb b/lib/statsample/regression/multiple/baseengine.rb
index 5f287a1..f2fdf82 100644
--- a/lib/statsample/regression/multiple/baseengine.rb
+++ b/lib/statsample/regression/multiple/baseengine.rb
@@ -19,13 +19,12 @@ def self.univariate?
         end
         def initialize(ds, y_var, opts = Hash.new)
           @ds=ds
-          @predictors_n=@ds.fields.size-1
-          @total_cases=@ds.cases
-          @cases=@ds.cases
+          @predictors_n=@ds.vectors.size-1
+          @total_cases=@ds.nrows
+          @cases=@ds.nrows
           @y_var=y_var
           @r2=nil
-          @name=_("Multiple Regression:  %s over %s") % [ ds.fields.join(",") , @y_var]
-          
+          @name=_("Multiple Regression:  %s over %s") % [ ds.vectors.to_a.join(",") , @y_var]
           
           opts_default={:digits=>3}
           @opts=opts_default.merge opts
@@ -33,7 +32,6 @@ def initialize(ds, y_var, opts = Hash.new)
           @opts.each{|k,v|
             self.send("#{k}=",v) if self.respond_to? k
           }
-          
         end
         # Calculate F Test
         def anova
@@ -45,15 +43,17 @@ def se_estimate
         end
         # Retrieves a vector with predicted values for y
         def predicted
-          @total_cases.times.collect { |i|
-            invalid=false
-            vect=@dep_columns.collect {|v| invalid=true if v[i].nil?; v[i]}
-            if invalid
-              nil
-            else
-              process(vect)
+          Daru::Vector.new(
+            @total_cases.times.collect do |i|
+              invalid = false
+              vect = @dep_columns.collect {|v| invalid = true if v[i].nil?; v[i]}
+              if invalid
+                nil
+              else
+                process(vect)
+              end
             end
-          }.to_vector(:numeric)
+          )
         end
         # Retrieves a vector with standarized values for y
         def standarized_predicted
@@ -61,15 +61,17 @@ def standarized_predicted
         end
         # Retrieves a vector with residuals values for y
         def residuals
-          (0...@total_cases).collect{|i|
-            invalid=false
-            vect=@dep_columns.collect{|v| invalid=true if v[i].nil?; v[i]}
-            if invalid or @ds[@y_var][i].nil?
-              nil
-            else
-              @ds[@y_var][i] - process(vect)
+          Daru::Vector.new(
+            (0...@total_cases).collect do |i|
+              invalid=false
+              vect=@dep_columns.collect{|v| invalid=true if v[i].nil?; v[i]}
+              if invalid or @ds[@y_var][i].nil?
+                nil
+              else
+                @ds[@y_var][i] - process(vect)
+              end
             end
-          }.to_vector(:numeric)
+          )
         end
         # R Multiple
         def r
@@ -131,12 +133,10 @@ def probability
         # Tolerance for a given variable
         # http://talkstats.com/showthread.php?t=5056
         def tolerance(var)
-          ds=assign_names(@dep_columns)
-          ds.each{|k,v|
-          ds[k]=v.to_vector(:numeric)
-          }
-          lr=self.class.new(ds.to_dataset,var)
-          1-lr.r2
+          ds = assign_names(@dep_columns)
+          ds.each { |k,v| ds[k] = Daru::Vector.new(v) }
+          lr = self.class.new(Daru::DataFrame.new(ds),var)
+          1 - lr.r2
         end
         # Tolerances for each coefficient
         def coeffs_tolerances
@@ -165,12 +165,12 @@ def se_r2
         def estimated_variance_covariance_matrix
           #mse_p=mse
           columns=[]
-          @ds_valid.fields.each{|k|
-            v=@ds_valid[k]
-            columns.push(v.data) unless k==@y_var
+          @ds_valid.vectors.each{|k|
+            v = @ds_valid[k]
+            columns.push(v.to_a) unless k == @y_var
           }
           columns.unshift([1.0]*@valid_cases)
-          x=Matrix.columns(columns)
+          x=::Matrix.columns(columns)
           matrix=((x.t*x)).inverse * mse
           matrix.collect {|i| Math::sqrt(i) if i>=0 }
         end
diff --git a/lib/statsample/regression/multiple/gslengine.rb b/lib/statsample/regression/multiple/gslengine.rb
index ad6492c..5dc57ca 100644
--- a/lib/statsample/regression/multiple/gslengine.rb
+++ b/lib/statsample/regression/multiple/gslengine.rb
@@ -19,33 +19,34 @@ module Multiple
         class GslEngine < BaseEngine
           def initialize(ds,y_var, opts=Hash.new)
             super
-            @ds=ds.dup_only_valid
-            @ds_valid=@ds
-            @valid_cases=@ds_valid.cases
-            @dy=@ds[@y_var]
-            @ds_indep=ds.dup(ds.fields-[y_var])
+            @ds          = ds.dup_only_valid
+            @ds_valid    = @ds
+            @valid_cases = @ds_valid.nrows
+            @dy          = @ds[@y_var]
+            @ds_indep    = ds.dup(ds.vectors.to_a - [y_var])
             # Create a custom matrix
             columns=[]
             @fields=[]
-            max_deps = GSL::Matrix.alloc(@ds.cases, @ds.fields.size)
-            constant_col=@ds.fields.size-1
-            for i in 0...@ds.cases
+            max_deps = GSL::Matrix.alloc(@ds.nrows, @ds.vectors.size)
+            constant_col=@ds.vectors.size-1
+            for i in 0...@ds.nrows
               max_deps.set(i,constant_col,1)
             end
-            j=0
-            @ds.fields.each{|f|
-              if f!=@y_var
-                @ds[f].each_index{|i1|
+            j = 0
+            @ds.vectors.each do |f|
+              if f != @y_var
+                @ds[f].each_index do |i1|
                   max_deps.set(i1,j,@ds[f][i1])
-                }
+                end
+
                 columns.push(@ds[f].to_a)
                 @fields.push(f)
-                j+=1
+                j += 1
               end
-            }
-            @dep_columns=columns.dup
-            @lr_s=nil
-            c, @cov, @chisq, @status = GSL::MultiFit.linear(max_deps, @dy.gsl)
+            end
+            @dep_columns = columns.dup
+            @lr_s        = nil
+            c, @cov, @chisq, @status = GSL::MultiFit.linear(max_deps, @dy.to_gsl)
             @constant=c[constant_col]
             @coeffs_a=c.to_a.slice(0...constant_col)
             @coeffs=assign_names(@coeffs_a)
@@ -97,7 +98,7 @@ def lr_s
             @lr_s
           end
           def build_standarized
-            @ds_s=@ds.standarize
+            @ds_s=@ds.standardize
             @lr_s=GslEngine.new(@ds_s,@y_var)
           end
           def process_s(v)
@@ -114,17 +115,15 @@ def standarized_residuals
 
           # Standard error for coeffs
           def coeffs_se
-            out={}
-            evcm=estimated_variance_covariance_matrix
-            @ds_valid.fields.each_with_index do |f,i|
-
-              mi=i+1
-              next if f==@y_var
-              out[f]=evcm[mi,mi]
+            out  = {}
+            evcm = estimated_variance_covariance_matrix
+            @ds_valid.vectors.to_a.each_with_index do |f,i|
+              mi = i+1
+              next if f == @y_var
+              out[f] = evcm[mi,mi]
             end
             out
           end
-
         end
       end
     end
diff --git a/lib/statsample/regression/multiple/matrixengine.rb b/lib/statsample/regression/multiple/matrixengine.rb
index 86ddc52..9c780f3 100644
--- a/lib/statsample/regression/multiple/matrixengine.rb
+++ b/lib/statsample/regression/multiple/matrixengine.rb
@@ -59,8 +59,6 @@ def initialize(matrix,y_var, opts=Hash.new)
     @matrix_y = @matrix_cor.submatrix(@fields, [y_var])
     @matrix_y_cov = @matrix_cov.submatrix(@fields, [y_var])
     
-
-    
     @y_sd=Math::sqrt(@matrix_cov.submatrix([y_var])[0,0])
     
     @x_sd=@n_predictors.times.inject({}) {|ac,i|
@@ -77,14 +75,14 @@ def initialize(matrix,y_var, opts=Hash.new)
     @y_mean=0.0
     @name=_("Multiple reggresion of %s on %s") % [@fields.join(","), @y_var]
     
-    opts_default={:digits=>3}
-    opts=opts_default.merge opts
+    opts_default = {:digits=>3}
+    opts         = opts_default.merge opts
     opts.each{|k,v|
         self.send("#{k}=",v) if self.respond_to? k
     }
       result_matrix=@matrix_x_cov.inverse * @matrix_y_cov
 
-    if matrix._type==:covariance
+    if matrix._type == :covariance
       @coeffs=result_matrix.column(0).to_a
       @coeffs_stan=coeffs.collect {|k,v|
         coeffs[k]*@x_sd[k].quo(@y_sd)
@@ -116,12 +114,12 @@ def r
   end
   # Value of constant
   def constant
-    c=coeffs
-    @y_mean - @fields.inject(0){|a,k| a + (c[k] * @x_mean[k])}
+    c = coeffs
+    @y_mean - @fields.inject(0) { |a,k| a + (c[k] * @x_mean[k])}
   end
   # Hash of b or raw coefficients
   def coeffs
-    assign_names(@coeffs)    
+    assign_names(@coeffs)
   end
   # Hash of beta or standarized coefficients
 
@@ -185,7 +183,7 @@ def constant_se
     sd[:constant]=0
     fields=[:constant]+@matrix_cov.fields-[@y_var]
     # Recreate X'X using the variance-covariance matrix
-    xt_x=Matrix.rows(fields.collect {|i|
+    xt_x=::Matrix.rows(fields.collect {|i|
       fields.collect {|j|
         if i==:constant or j==:constant
           cov=0
diff --git a/lib/statsample/regression/multiple/rubyengine.rb b/lib/statsample/regression/multiple/rubyengine.rb
index 13c1718..f856aad 100644
--- a/lib/statsample/regression/multiple/rubyengine.rb
+++ b/lib/statsample/regression/multiple/rubyengine.rb
@@ -17,67 +17,64 @@ module Multiple
 
 class RubyEngine < MatrixEngine
   def initialize(ds,y_var, opts=Hash.new)
-    matrix=ds.correlation_matrix
-    fields_indep=ds.fields-[y_var]
-    default={
-      :y_mean=>ds[y_var].mean,
-      :x_mean=>fields_indep.inject({}) {|ac,f|  ac[f]=ds[f].mean; ac},
-      :y_sd=>ds[y_var].sd,
-      :x_sd=>fields_indep.inject({}) {|ac,f|  ac[f]=ds[f].sd; ac},
-      :cases=>Statsample::Bivariate.min_n_valid(ds)
+    matrix = Statsample::Bivariate.correlation_matrix ds
+    fields_indep=ds.vectors.to_a - [y_var]
+    default= {
+      :y_mean => ds[y_var].mean,
+      :x_mean => fields_indep.inject({}) {|ac,f|  ac[f]=ds[f].mean; ac},
+      :y_sd   => ds[y_var].sd,
+      :x_sd   => fields_indep.inject({}) {|ac,f|  ac[f]=ds[f].sd; ac},
+      :cases  => Statsample::Bivariate.min_n_valid(ds)
     }
-    opts=opts.merge(default)
+    opts = opts.merge(default)
     super(matrix, y_var, opts)
-    @ds=ds
-    @dy=ds[@y_var]
-    @ds_valid=ds.dup_only_valid
-    @total_cases=@ds.cases
-    @valid_cases=@ds_valid.cases
-    @ds_indep = ds.dup(ds.fields-[y_var])
+    @ds = ds
+    @dy = ds[@y_var]
+    @ds_valid = ds.dup_only_valid
+    @total_cases = @ds.nrows
+    @valid_cases = @ds_valid.nrows
+    @ds_indep    = ds.dup(ds.vectors.to_a - [y_var])
     set_dep_columns
   end
   
   def set_dep_columns
-    @dep_columns=[]
-    @ds_indep.each_vector{|k,v|
-      @dep_columns.push(v.data_with_nils)
-    }                
+    @dep_columns = []
+    @ds_indep.each_vector { |v| @dep_columns.push(v.to_a) }                
   end
 
   def fix_with_mean
     i=0
-    @ds_indep.each do |row|
+    @ds_indep.each(:row) do |row|
       empty=[]
       row.each do |k,v|
         empty.push(k) if v.nil?
       end
+
       if empty.size==1
         @ds_indep[empty[0]][i]=@ds[empty[0]].mean
       end
-      i+=1
+      i += 1
     end
-    @ds_indep.update_valid_data
+    @ds_indep.update
     set_dep_columns
   end
   def fix_with_regression
-    i=0
-    @ds_indep.each{|row|
-      empty=[]
-      row.each{|k,v|
-          empty.push(k) if v.nil?
-      }
+    i = 0
+    @ds_indep.each(:row) do |row|
+      empty = []
+      row.each { |k,v| empty.push(k) if v.nil? }
       if empty.size==1
-        field=empty[0]
-        lr=MultipleRegression.new(@ds_indep,field)
-        fields=[]
+        field  = empty[0]
+        lr     = MultipleRegression.new(@ds_indep,field)
+        fields = []
         @ds_indep.fields.each{|f|
-            fields.push(row[f]) unless f==field
+          fields.push(row[f]) unless f == field
         }
         @ds_indep[field][i]=lr.process(fields)
       end
       i+=1
-    }
-    @ds_indep.update_valid_data
+    end
+    @ds_indep.update
     set_dep_columns
   end
   # Standard error for constant
diff --git a/test/test_multiset.rb b/test/test_multiset.rb
index 7543ac0..0e47477 100644
--- a/test/test_multiset.rb
+++ b/test/test_multiset.rb
@@ -127,7 +127,6 @@ def test_each
     }
     xp, yp, zp = {}, {}, {}
     @ms.each {|k, ds|
-      # puts "k #{k} ds #{ds}"
       xp[k] = ds[:x]
       yp[k] = ds[:y]
       zp[k] = ds[:z]
diff --git a/test/test_regression.rb b/test/test_regression.rb
index d973e79..8c23bc0 100644
--- a/test/test_regression.rb
+++ b/test/test_regression.rb
@@ -3,21 +3,21 @@
 class StatsampleRegressionTestCase < Minitest::Test
   context 'Example with missing data' do
     setup do
-      @x = [0.285714285714286, 0.114285714285714, 0.314285714285714, 0.2, 0.2, 0.228571428571429, 0.2, 0.4, 0.714285714285714, 0.285714285714286, 0.285714285714286, 0.228571428571429, 0.485714285714286, 0.457142857142857, 0.257142857142857, 0.228571428571429, 0.285714285714286, 0.285714285714286, 0.285714285714286, 0.142857142857143, 0.285714285714286, 0.514285714285714, 0.485714285714286, 0.228571428571429, 0.285714285714286, 0.342857142857143, 0.285714285714286, 0.0857142857142857].to_numeric
+      @x = Daru::Vector.new([0.285714285714286, 0.114285714285714, 0.314285714285714, 0.2, 0.2, 0.228571428571429, 0.2, 0.4, 0.714285714285714, 0.285714285714286, 0.285714285714286, 0.228571428571429, 0.485714285714286, 0.457142857142857, 0.257142857142857, 0.228571428571429, 0.285714285714286, 0.285714285714286, 0.285714285714286, 0.142857142857143, 0.285714285714286, 0.514285714285714, 0.485714285714286, 0.228571428571429, 0.285714285714286, 0.342857142857143, 0.285714285714286, 0.0857142857142857])
 
-      @y = [nil, 0.233333333333333, nil, 0.266666666666667, 0.366666666666667, nil, 0.333333333333333, 0.3, 0.666666666666667, 0.0333333333333333, 0.333333333333333, nil, nil, 0.533333333333333, 0.433333333333333, 0.4, 0.4, 0.5, 0.4, 0.266666666666667, 0.166666666666667, 0.666666666666667, 0.433333333333333, 0.166666666666667, nil, 0.4, 0.366666666666667, nil].to_numeric
-      @ds = { 'x' => @x, 'y' => @y }.to_dataset
-      @lr = Statsample::Regression::Multiple::RubyEngine.new(@ds, 'y')
+      @y = Daru::Vector.new([nil, 0.233333333333333, nil, 0.266666666666667, 0.366666666666667, nil, 0.333333333333333, 0.3, 0.666666666666667, 0.0333333333333333, 0.333333333333333, nil, nil, 0.533333333333333, 0.433333333333333, 0.4, 0.4, 0.5, 0.4, 0.266666666666667, 0.166666666666667, 0.666666666666667, 0.433333333333333, 0.166666666666667, nil, 0.4, 0.366666666666667, nil])
+      @ds = Daru::DataFrame.new({ :x => @x, :y => @y })
+      @lr = Statsample::Regression::Multiple::RubyEngine.new(@ds, :y)
     end
     should 'have correct values' do
       assert_in_delta(0.455, @lr.r2, 0.001)
       assert_in_delta(0.427, @lr.r2_adjusted, 0.001)
       assert_in_delta(0.1165, @lr.se_estimate, 0.001)
       assert_in_delta(15.925, @lr.f, 0.0001)
-      assert_in_delta(0.675, @lr.standarized_coeffs['x'], 0.001)
-      assert_in_delta(0.778, @lr.coeffs['x'], 0.001, 'coeff x')
+      assert_in_delta(0.675, @lr.standarized_coeffs[:x], 0.001)
+      assert_in_delta(0.778, @lr.coeffs[:x], 0.001, 'coeff x')
       assert_in_delta(0.132, @lr.constant, 0.001, 'constant')
-      assert_in_delta(0.195, @lr.coeffs_se['x'], 0.001, 'coeff x se')
+      assert_in_delta(0.195, @lr.coeffs_se[:x], 0.001, 'coeff x se')
       assert_in_delta(0.064, @lr.constant_se, 0.001, 'constant se')
     end
   end
@@ -26,24 +26,24 @@ class StatsampleRegressionTestCase < Minitest::Test
 
     a, b = rand, rand
 
-    x1 = samples.times.map { rand }.to_numeric
-    x2 = samples.times.map { rand }.to_numeric
-    x3 = samples.times.map { |i| x1[i] * (1 + a) + x2[i] * (1 + b) }.to_numeric
-    y = samples.times.map { |i| x1[i] + x2[i] + x3[i] + rand }.to_numeric
+    x1 = Daru::Vector.new(samples.times.map { rand })
+    x2 = Daru::Vector.new(samples.times.map { rand })
+    x3 = Daru::Vector.new(samples.times.map { |i| x1[i] * (1 + a) + x2[i] * (1 + b) })
+    y  = Daru::Vector.new(samples.times.map { |i| x1[i] + x2[i] + x3[i] + rand })
 
-    ds = { 'x1' => x1, 'x2' => x2, 'x3' => x3, 'y' => y }.to_dataset
+    ds = Daru::DataFrame.new({ :x1 => x1, :x2 => x2, :x3 => x3, :y => y })
 
     assert_raise(Statsample::Regression::LinearDependency) {
-      Statsample::Regression::Multiple::RubyEngine.new(ds, 'y')
+      Statsample::Regression::Multiple::RubyEngine.new(ds, :y)
     }
   end
   def test_parameters
-    @x = [13, 20, 10, 33, 15].to_vector(:numeric)
-    @y = [23, 18, 35, 10, 27].to_vector(:numeric)
+    @x =Daru::Vector.new([13, 20, 10, 33, 15])
+    @y =Daru::Vector.new([23, 18, 35, 10, 27])
     reg = Statsample::Regression::Simple.new_from_vectors(@x, @y)
     _test_simple_regression(reg)
-    ds = { 'x' => @x, 'y' => @y }.to_dataset
-    reg = Statsample::Regression::Simple.new_from_dataset(ds, 'x', 'y')
+    ds = Daru::DataFrame.new({ :x => @x, :y => @y })
+    reg = Statsample::Regression::Simple.new_from_dataset(ds, :x, :y)
     _test_simple_regression(reg)
     reg = Statsample::Regression.simple(@x, @y)
     _test_simple_regression(reg)
@@ -57,11 +57,11 @@ def _test_simple_regression(reg)
   end
 
   def test_summaries
-    a = 10.times.map { rand(100) }.to_numeric
-    b = 10.times.map { rand(100) }.to_numeric
-    y = 10.times.map { rand(100) }.to_numeric
-    ds = { 'a' => a, 'b' => b, 'y' => y }.to_dataset
-    lr = Statsample::Regression::Multiple::RubyEngine.new(ds, 'y')
+    a = Daru::Vector.new(10.times.map { rand(100) })
+    b = Daru::Vector.new(10.times.map { rand(100) })
+    y = Daru::Vector.new(10.times.map { rand(100) })
+    ds = Daru::DataFrame.new({ :a => a, :b => b, :y => y })
+    lr = Statsample::Regression::Multiple::RubyEngine.new(ds, :y)
     assert(lr.summary.size > 0)
   end
 
@@ -87,12 +87,12 @@ def test_multiple_dependent
   end
 
   def test_multiple_regression_pairwise_2
-    @a = [1, 3, 2, 4, 3, 5, 4, 6, 5, 7, 3, nil, 3, nil, 3].to_vector(:numeric)
-    @b = [3, 3, 4, 4, 5, 5, 6, 6, 4, 4, 2, 2, nil, 6, 2].to_vector(:numeric)
-    @c = [11, 22, 30, 40, 50, 65, 78, 79, 99, 100, nil, 3, 7, nil, 7].to_vector(:numeric)
-    @y = [3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 30, 40, nil, 50, nil].to_vector(:numeric)
-    ds = { 'a' => @a, 'b' => @b, 'c' => @c, 'y' => @y }.to_dataset
-    lr = Statsample::Regression::Multiple::RubyEngine.new(ds, 'y')
+    @a =Daru::Vector.new( [1, 3, 2, 4, 3, 5, 4, 6, 5, 7, 3, nil, 3, nil, 3])
+    @b =Daru::Vector.new( [3, 3, 4, 4, 5, 5, 6, 6, 4, 4, 2, 2, nil, 6, 2])
+    @c =Daru::Vector.new( [11, 22, 30, 40, 50, 65, 78, 79, 99, 100, nil, 3, 7, nil, 7])
+    @y =Daru::Vector.new( [3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 30, 40, nil, 50, nil])
+    ds = Daru::DataFrame.new({ :a => @a, :b => @b, :c => @c, :y => @y })
+    lr = Statsample::Regression::Multiple::RubyEngine.new(ds, :y)
     assert_in_delta(2407.436, lr.sst, 0.001)
     assert_in_delta(0.752, lr.r, 0.001, 'pairwise r')
     assert_in_delta(0.565, lr.r2, 0.001)
@@ -103,12 +103,12 @@ def test_multiple_regression_pairwise_2
 
   def test_multiple_regression_gsl
     if Statsample.has_gsl?
-      @a = [1, 3, 2, 4, 3, 5, 4, 6, 5, 7].to_vector(:numeric)
-      @b = [3, 3, 4, 4, 5, 5, 6, 6, 4, 4].to_vector(:numeric)
-      @c = [11, 22, 30, 40, 50, 65, 78, 79, 99, 100].to_vector(:numeric)
-      @y = [3, 4, 5, 6, 7, 8, 9, 10, 20, 30].to_vector(:numeric)
-      ds = { 'a' => @a, 'b' => @b, 'c' => @c, 'y' => @y }.to_dataset
-      lr = Statsample::Regression::Multiple::GslEngine.new(ds, 'y')
+      @a =Daru::Vector.new( [1, 3, 2, 4, 3, 5, 4, 6, 5, 7])
+      @b =Daru::Vector.new( [3, 3, 4, 4, 5, 5, 6, 6, 4, 4])
+      @c =Daru::Vector.new( [11, 22, 30, 40, 50, 65, 78, 79, 99, 100])
+      @y =Daru::Vector.new( [3, 4, 5, 6, 7, 8, 9, 10, 20, 30])
+      ds = Daru::DataFrame.new({ :a => @a, :b => @b, :c => @c, :y => @y })
+      lr = Statsample::Regression::Multiple::GslEngine.new(ds, :y)
       assert(lr.summary.size > 0)
       model_test(lr, 'gsl')
       predicted = [1.7857, 6.0989, 3.2433, 7.2908, 4.9667, 10.3428, 8.8158, 10.4717, 23.6639, 25.3198]
@@ -127,8 +127,8 @@ def test_multiple_regression_gsl
   end
 
   def model_test_matrix(lr, name = 'undefined')
-    stan_coeffs = { 'a' => 0.151, 'b' => -0.547, 'c' => 0.997 }
-    unstan_coeffs = { 'a' => 0.695, 'b' => -4.286, 'c' => 0.266 }
+    stan_coeffs = { :a => 0.151, :b => -0.547, :c => 0.997 }
+    unstan_coeffs = { :a => 0.695, :b => -4.286, :c => 0.266 }
 
     unstan_coeffs.each_key{|k|
       assert_in_delta(unstan_coeffs[k], lr.coeffs[k], 0.001, "b coeffs - #{name}")
@@ -145,15 +145,15 @@ def model_test_matrix(lr, name = 'undefined')
 
     assert_in_delta(20.908, lr.f, 0.001)
     assert_in_delta(0.001, lr.probability, 0.001)
-    assert_in_delta(0.226, lr.tolerance('a'), 0.001)
+    assert_in_delta(0.226, lr.tolerance(:a), 0.001)
 
-    coeffs_se = { 'a' => 1.171, 'b' => 1.129, 'c' => 0.072 }
+    coeffs_se = { :a => 1.171, :b => 1.129, :c => 0.072 }
 
     ccoeffs_se = lr.coeffs_se
     coeffs_se.each_key{|k|
       assert_in_delta(coeffs_se[k], ccoeffs_se[k], 0.001)
     }
-    coeffs_t = { 'a' => 0.594, 'b' => -3.796, 'c' => 3.703 }
+    coeffs_t = { :a => 0.594, :b => -3.796, :c => 3.703 }
     ccoeffs_t = lr.coeffs_t
     coeffs_t.each_key{|k|
       assert_in_delta(coeffs_t[k], ccoeffs_t[k], 0.001)
@@ -174,32 +174,37 @@ def model_test(lr, name = 'undefined')
   end
 
   def test_regression_matrix
-    @a = [1, 3, 2, 4, 3, 5, 4, 6, 5, 7].to_vector(:numeric)
-    @b = [3, 3, 4, 4, 5, 5, 6, 6, 4, 4].to_vector(:numeric)
-    @c = [11, 22, 30, 40, 50, 65, 78, 79, 99, 100].to_vector(:numeric)
-    @y = [3, 4, 5, 6, 7, 8, 9, 10, 20, 30].to_vector(:numeric)
-    ds = { 'a' => @a, 'b' => @b, 'c' => @c, 'y' => @y }.to_dataset
+    @a = Daru::Vector.new([1, 3, 2, 4, 3, 5, 4, 6, 5, 7])
+    @b = Daru::Vector.new([3, 3, 4, 4, 5, 5, 6, 6, 4, 4])
+    @c = Daru::Vector.new([11, 22, 30, 40, 50, 65, 78, 79, 99, 100])
+    @y = Daru::Vector.new([3, 4, 5, 6, 7, 8, 9, 10, 20, 30])
+    ds = Daru::DataFrame.new({ :a => @a, :b => @b, :c => @c, :y => @y })
     cor = Statsample::Bivariate.correlation_matrix(ds)
 
-    lr = Statsample::Regression::Multiple::MatrixEngine.new(cor, 'y', y_mean: @y.mean, x_mean: { 'a' => ds['a'].mean, 'b' => ds['b'].mean, 'c' => ds['c'].mean }, cases: @a.size, y_sd: @y.sd, x_sd: { 'a' => @a.sd, 'b' => @b.sd, 'c' => @c.sd })
+    lr = Statsample::Regression::Multiple::MatrixEngine.new(
+      cor, :y, y_mean: @y.mean, 
+      x_mean: { :a => ds[:a].mean, :b => ds[:b].mean, :c => ds[:c].mean }, 
+      cases: @a.size, y_sd: @y.sd, x_sd: { :a => @a.sd, :b => @b.sd, :c => @c.sd })
     assert_nil(lr.constant_se)
     assert_nil(lr.constant_t)
     model_test_matrix(lr, 'correlation matrix')
 
     covariance = Statsample::Bivariate.covariance_matrix(ds)
-    lr = Statsample::Regression::Multiple::MatrixEngine.new(covariance, 'y', y_mean: @y.mean, x_mean: { 'a' => ds['a'].mean, 'b' => ds['b'].mean, 'c' => ds['c'].mean }, cases: @a.size)
+    lr = Statsample::Regression::Multiple::MatrixEngine.new(
+      covariance, :y, y_mean: @y.mean, 
+      x_mean: { :a => ds[:a].mean, :b => ds[:b].mean, :c => ds[:c].mean }, cases: @a.size)
     assert(lr.summary.size > 0)
 
     model_test(lr, 'covariance matrix')
   end
 
   def test_regression_rubyengine
-    @a = [nil, 1, 3, 2, 4, 3, 5, 4, 6, 5, 7].to_vector(:numeric)
-    @b = [nil, 3, 3, 4, 4, 5, 5, 6, 6, 4, 4].to_vector(:numeric)
-    @c = [nil, 11, 22, 30, 40, 50, 65, 78, 79, 99, 100].to_vector(:numeric)
-    @y = [nil, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30].to_vector(:numeric)
-    ds = { 'a' => @a, 'b' => @b, 'c' => @c, 'y' => @y }.to_dataset
-    lr = Statsample::Regression::Multiple::RubyEngine.new(ds, 'y')
+    @a = Daru::Vector.new([nil, 1, 3, 2, 4, 3, 5, 4, 6, 5, 7])
+    @b = Daru::Vector.new([nil, 3, 3, 4, 4, 5, 5, 6, 6, 4, 4])
+    @c = Daru::Vector.new([nil, 11, 22, 30, 40, 50, 65, 78, 79, 99, 100])
+    @y = Daru::Vector.new([nil, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30])
+    ds = Daru::DataFrame.new({ :a => @a, :b => @b, :c => @c, :y => @y })
+    lr = Statsample::Regression::Multiple::RubyEngine.new(ds, :y)
     assert_equal(11, lr.total_cases)
     assert_equal(10, lr.valid_cases)
     model_test(lr, 'rubyengine with missing data')