version 0.3.1

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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,17 +1,17 @@
 Package: designmatch
 Type: Package
 Title: Matched Samples that are Balanced and Representative by Design
-Version: 0.3.0
-Date: 2017-05-17
-Author: Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <[email protected]>, Juan P. Vielma <[email protected]>
-Maintainer: Jose R. Zubizarreta <zubizarreta@columbia.edu>
+Version: 0.3.1
+Date: 2018-06-18
+Author: Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <[email protected]>, Juan P. Vielma <[email protected]>
+Maintainer: Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>
 Depends: R (>= 3.2), lattice, MASS, slam, Rglpk
-Suggests: gurobi, Rcplex, Rsymphony
+Enhances: gurobi, Rcplex, Rmosek, Rsymphony
 SystemRequirements: GLPK library package (e.g., libglpk-dev on
         Debian/Ubuntu)
 License: GPL-2 | GPL-3
 Description: Includes functions for the construction of matched samples that are balanced and representative by design.  Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs.  Also, they can be used for the design of randomized experiments, for example, for matching before randomization.  By default, 'designmatch' uses the 'GLPK' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface.  For their installation, please follow the instructions at <http://user.gurobi.com/download/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>.  We have also included directions in the gurobi_installation file in the inst folder.
 NeedsCompilation: no
-Packaged: 2017-05-18 01:39:38 UTC; jrz
+Packaged: 2018-06-18 14:12:15 UTC; jrz
 Repository: CRAN
-Date/Publication: 2017-05-18 03:48:36 UTC
+Date/Publication: 2018-06-18 18:24:05 UTC
diff --git a/MD5 b/MD5
@@ -1,9 +1,9 @@
-e6ccf902847f31ee3b7a20750bbc8815 *DESCRIPTION
+bd3c4c85871ceff1cc5f2f0f9cdb44a5 *DESCRIPTION
 a84e461889448bab6f00dc036a173876 *NAMESPACE
 4035589e4087aa8c0dfc97e90db340f8 *R/absstddif.r
 7d1a85944bcd2594e3ab13ffdd80bd1e *R/addcalip.r
 06d2cede573309b27980bbb8869ad01e *R/bmatch.r
-932c4f0f192b4f4901220dcb169c4d50 *R/cardmatch.r
+53910a26ffdd46067f0ff93d9694b58d *R/cardmatch.r
 1ccbbc3d8e654fcd4366a8c93350178b *R/constraintmatrix.r
 ddd36bec26100d3064a1bf106c36740c *R/distmat.r
 692b745a194a270376faf4842b958a02 *R/distmatch.r
@@ -15,25 +15,25 @@ d3df5386a7329cd2c93dedac69a83db4 *R/meantab.r
 4a4e30c24c6373c9118b32245b3f29fc *R/nmatch.r
 98f291ae461a0b1c18ea5d8858b3eb22 *R/pairsplot.r
 36d380e4bd2fb8bbd551af173cc12056 *R/problemparameters.r
-3fc3b874e31fb6965591687cfc0481c9 *R/problemparameters_cardmatch.r
+6c4827264e52d44760c865663f2e6c2d *R/problemparameters_cardmatch.r
 faaeb63e221a183bf338d8005151bb03 *R/relaxation_b.r
 1ee8331d65f0dab43a41bd5a4740fcdf *R/relaxation_n.r
 8a6974d5a5f551af3ca1e1be7d555be6 *R/smahal.r
 0187616874e4a366b4ba006ded775e2d *data/germancities.rda
 7c8f4dadefa6fd57a06a23067605dda0 *data/lalonde.rda
 fca1c5c2ab15ec2207f7f77919cca494 *inst/gurobi_installation.txt
 8b5ab702a3ca62c67934d34220c09ab6 *inst/symphony_installation.txt
-a3432aa9ad196973c884751e3a7e0fb4 *man/absstddif.Rd
-e19dd3ffc9a2b389b9ad4c0a7a082600 *man/bmatch.Rd
-2f242dd6c23bbb2cf7b56ab900a74572 *man/cardmatch.Rd
-70d7c69f620facbd7955281a7d6bbf1b *man/designmatch-package.Rd
+a8685672a86c1c9e7c630b430a193f5f *man/absstddif.Rd
+6ec4ccf8e22c37bac38e74e20aedfa84 *man/bmatch.Rd
+d26a6354e52f7b5a8d74a5fec2f4c4df *man/cardmatch.Rd
+f57efa7f8b3a56947c842360a6be4c4b *man/designmatch-package.Rd
 9d510e34095ba20a26ae6ed0e3b7807a *man/distmat.Rd
-90779867557e9b66310864ca76f825f5 *man/distmatch.Rd
-60560ddae0cc24fb6cedc7785379fd3d *man/ecdfplot.Rd
-093455696515de415adc6c394e839715 *man/finetab.Rd
+3562e9d06db45009486c574740e620a4 *man/distmatch.Rd
+1d59c6df9d58dd6a7d6d7e94a0a535b8 *man/ecdfplot.Rd
+e7f0f69f8b70ba4d92f21b1d93d60bec *man/finetab.Rd
 3e98c15e211274459bbe5239f7342766 *man/germancities.Rd
 d956e94f68ff0196119800ab2e887d19 *man/lalonde.Rd
-178925f2ba0ec6cb0cf7c88bd5f936c2 *man/loveplot.Rd
+e6792c32417b2ef50459d550ed90b83f *man/loveplot.Rd
 1616542174f147bf821c0ca73161fb10 *man/meantab.Rd
-50a270039e2ddaa4d21bd7dcbc668c9a *man/nmatch.Rd
-3a8a8aafd46dc6a04a0f186139fe43f9 *man/pairsplot.Rd
+294e9c9f7e988b764df5ea51f1d36ddd *man/nmatch.Rd
+54aa121631d4cc210c88409123f0cbec *man/pairsplot.Rd
diff --git a/R/cardmatch.r b/R/cardmatch.r
@@ -8,8 +8,13 @@ cardmatch = function(t_ind, mom = NULL, fine = NULL, solver = NULL) {
   } else {
     mom_covs = mom$covs
     mom_tols = mom$tols
-    mom_targets = mom$targets
-  }    
+    if (is.null(mom$targets)) {
+    	mom_targets = NULL
+    }
+    if (!is.null(mom$targets)) {
+    	mom_targets = mom$targets
+    }
+  }  
   if (is.null(fine)) {
     fine_covs = NULL
   } else {

diff --git a/R/problemparameters_cardmatch.r b/R/problemparameters_cardmatch.r
@@ -20,25 +20,39 @@
     vals_mom = NULL
     n_mom_covs = ncol(mom_covs)
     k = 1
-    for (i in 1:n_mom_covs) {
-      #! Treated
-      rows_mom_plus = rep(row_ind_cur+k, n_t)
-      rows_mom_minus = rep(row_ind_cur+k+1, n_t)  
-      rows_mom = c(rows_mom, rows_mom_plus, rows_mom_minus)
-      cols_mom = c(cols_mom, rep(1:n_t, 2))
-      vals_plus = c(mom_covs[t_ind==1, i]-mom_targets[i]-mom_tols[i])
-      vals_minus = c(mom_covs[t_ind==1, i]-mom_targets[i]+mom_tols[i])
-      vals_mom = c(vals_mom, c(vals_plus, vals_minus)) 
-      #! Controls
-      rows_mom_plus = rep(row_ind_cur+k+2, n_c)
-      rows_mom_minus = rep(row_ind_cur+k+3, n_c)  
-      rows_mom = c(rows_mom, rows_mom_plus, rows_mom_minus)
-      cols_mom = c(cols_mom, rep(n_t+(1:n_c), 2))
-      vals_plus = c(mom_covs[t_ind==0, i]-mom_targets[i]-mom_tols[i])
-      vals_minus = c(mom_covs[t_ind==0, i]-mom_targets[i]+mom_tols[i])
-      vals_mom = c(vals_mom, c(vals_plus, vals_minus))       
-      k = k+4
+    if (!is.null(mom_targets)) {
+      for (i in 1:n_mom_covs) {
+        #! Treated
+        rows_mom_plus = rep(row_ind_cur+k, n_t)
+        rows_mom_minus = rep(row_ind_cur+k+1, n_t)  
+        rows_mom = c(rows_mom, rows_mom_plus, rows_mom_minus)
+        cols_mom = c(cols_mom, rep(1:n_t, 2))
+        vals_plus = c(mom_covs[t_ind==1, i]-mom_targets[i]-mom_tols[i])
+        vals_minus = c(mom_covs[t_ind==1, i]-mom_targets[i]+mom_tols[i])
+        vals_mom = c(vals_mom, c(vals_plus, vals_minus)) 
+        #! Controls
+        rows_mom_plus = rep(row_ind_cur+k+2, n_c)
+        rows_mom_minus = rep(row_ind_cur+k+3, n_c)  
+        rows_mom = c(rows_mom, rows_mom_plus, rows_mom_minus)
+        cols_mom = c(cols_mom, rep(n_t+(1:n_c), 2))
+        vals_plus = c(mom_covs[t_ind==0, i]-mom_targets[i]-mom_tols[i])
+        vals_minus = c(mom_covs[t_ind==0, i]-mom_targets[i]+mom_tols[i])
+        vals_mom = c(vals_mom, c(vals_plus, vals_minus))       
+        k = k+4
+      }
     }
+    if (is.null(mom_targets)) {
+      for (i in 1:n_mom_covs) {
+        rows_mom_plus = rep(row_ind_cur+k, n_t+n_c)
+        rows_mom_minus = rep(row_ind_cur+k+1, n_t+n_c)  
+        rows_mom = c(rows_mom, rows_mom_plus, rows_mom_minus)
+        cols_mom = c(cols_mom, rep(1:(n_t+n_c), 2))
+        vals_plus = c(mom_covs[t_ind==1, i]-mom_tols[i], -mom_covs[t_ind==0, i])
+        vals_minus = c(mom_covs[t_ind==1, i]+mom_tols[i], -mom_covs[t_ind==0, i])
+        vals_mom = c(vals_mom, c(vals_plus, vals_minus))     
+        k = k+2
+      }
+    }    
     row_ind_cur = max(rows_mom)
   }
   #! Fine balance    
@@ -113,8 +127,14 @@
   sense = NULL
   #! Mom balance
   if (!is.null(mom_covs)) {
-  	sense_covs = rep(c("L", "G", "L", "G"), length(unique(rows_mom))/4)
-    sense = c(sense, sense_covs) 
+  	if (!is.null(mom_targets)) {
+  	  sense_covs = rep(c("L", "G", "L", "G"), length(unique(rows_mom))/4)
+      sense = c(sense, sense_covs)
+    }   	
+  	if (is.null(mom_targets)) {
+  	  sense_covs = rep(c("L", "G"), length(unique(rows_mom))/2)
+      sense = c(sense, sense_covs)
+    } 
   }
   #! Fine balance
   if (!is.null(fine_covs)) {

diff --git a/man/absstddif.Rd b/man/absstddif.Rd
@@ -30,7 +30,7 @@
 }
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 
 \examples{

diff --git a/man/bmatch.Rd b/man/bmatch.Rd
@@ -17,7 +17,7 @@
 
 \arguments{
 
-\item{t_ind}{treatment indicator: a vector of zeros and ones indicating treatment (1 = treated; 0 = control).}
+\item{t_ind}{treatment indicator: a vector of zeros and ones indicating treatment (1 = treated; 0 = control).  Please note that the data needs to be sorted in decreasing order according to this treatment indicator.}
 
 \item{dist_mat}{distance matrix: a matrix of positive distances between treated units (rows) and controls (columns).  If \code{dist_mat = NULL} and \code{subset_weight = 1}, then bmatch will solve the cardinality matching problem in Zubizarreta et al. (2014).}
 
@@ -134,7 +134,7 @@ tional studies," \emph{Journal of Computational and Graphical Statistics}, 21, 5
 }
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 
 \seealso{
@@ -143,8 +143,9 @@ tional studies," \emph{Journal of Computational and Graphical Statistics}, 21, 5
 
 \examples{
 
-# Load and attach data
+# Load, sort, and attach data
 data(lalonde)
+lalonde = lalonde[order(lalonde$treatment, decreasing = TRUE), ]
 attach(lalonde)
 
 ################################# 
@@ -156,8 +157,10 @@ attach(lalonde)
 # exact matching for different covariates. The solver used is glpk with the 
 # approximate option.
 
-# Treatment indicator
+# Treatment indicator; note that the data needs to be sorted in decreasing order
+# according to this treatment indicator
 t_ind = treatment
+t_ind 
 
 # Distance matrix
 dist_mat = NULL 

diff --git a/man/cardmatch.Rd b/man/cardmatch.Rd
@@ -14,7 +14,7 @@
 
 \arguments{
 
-\item{t_ind}{treatment indicator: a vector of zeros and ones indicating treatment (1 = treated; 0 = control).}
+\item{t_ind}{treatment indicator: a vector of zeros and ones indicating treatment (1 = treated; 0 = control).  Please note that the data needs to be sorted in decreasing order according to this treatment indicator.}
 
 \item{mom}{moment balance parameters: a list with three arguments, 
 
@@ -73,7 +73,7 @@ Optimization solver parameters: a list with four objects,
 }
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>, Juan P. Vielma <jvielma@mit.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>, Juan P. Vielma <jvielma@mit.edu>.
 }
 
 \seealso{
@@ -82,8 +82,9 @@ Optimization solver parameters: a list with four objects,
 
 \examples{
 
-# Load and attach data
+# Load, sort, and attach data
 data(lalonde)
+lalonde = lalonde[order(lalonde$treatment, decreasing = TRUE), ]
 attach(lalonde)
 
 ################################# 
@@ -107,8 +108,10 @@ education_5 = quantiles(education, 5)
 re74_5 = quantiles(re74, 5)
 re75_5 = quantiles(re75, 5)
 
-# Treatment indicator
+# Treatment indicator; note that the data needs to be sorted in decreasing order
+# according to this treatment indicator
 t_ind = treatment
+t_ind 
 
 # Fine balance
 fine_covs = cbind(black, hispanic, married, nodegree, age_5, education_5, re74_5, re75_5)

diff --git a/man/designmatch-package.Rd b/man/designmatch-package.Rd
@@ -18,9 +18,9 @@ License: \tab GPL-2 | GPL-3\cr
 }
 }
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 
-Maintainer: Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+Maintainer: Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 \references{
 	Greevy, R., Lu, B., Silber, J. H., and Rosenbaum, P. R. (2004), "Optimal Multivariate

diff --git a/man/distmatch.Rd b/man/distmatch.Rd
@@ -14,7 +14,7 @@
 
 \arguments{
 
-\item{t_ind}{treatment indicator: a vector of zeros and ones indicating treatment (1 = treated; 0 = control).}
+\item{t_ind}{treatment indicator: a vector of zeros and ones indicating treatment (1 = treated; 0 = control).  Please note that the data needs to be sorted in decreasing order according to this treatment indicator.}
 
 \item{dist_mat}{distance matrix: a matrix of positive distances between treated units (rows) and controls (columns).  If \code{dist_mat = NULL} and \code{subset_weight = 1}, then bmatch will solve the cardinality matching problem in Zubizarreta et al. (2014).}
 
@@ -58,7 +58,7 @@ Optimization solver parameters: a list with four objects,
 }
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 
 \seealso{
@@ -67,8 +67,9 @@ Optimization solver parameters: a list with four objects,
 
 \examples{
 
-# Load and attach data
+# Load, sort, and attach data
 data(lalonde)
+lalonde = lalonde[order(lalonde$treatment, decreasing = TRUE), ]
 attach(lalonde)
 
 ################################# 
@@ -92,8 +93,10 @@ education_5 = quantiles(education, 5)
 re74_5 = quantiles(re74, 5)
 re75_5 = quantiles(re75, 5)
 
-# Treatment indicator
+# Treatment indicator; note that the data needs to be sorted in decreasing order
+# according to this treatment indicator
 t_ind = treatment
+t_ind 
 
 # Fine balance
 fine_covs = cbind(black, hispanic, married, nodegree, age_5, education_5, re74_5, re75_5)

diff --git a/man/ecdfplot.Rd b/man/ecdfplot.Rd
@@ -33,7 +33,7 @@
 }
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 
 \examples{

diff --git a/man/finetab.Rd b/man/finetab.Rd
@@ -30,7 +30,7 @@
 }	
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 
 \examples{	

diff --git a/man/loveplot.Rd b/man/loveplot.Rd
@@ -36,7 +36,7 @@
 }
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 
 \examples{

diff --git a/man/nmatch.Rd b/man/nmatch.Rd
@@ -124,7 +124,7 @@ tional studies," \emph{Journal of Computational and Graphical Statistics}, 21, 5
 }
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 
 %\seealso{

diff --git a/man/pairsplot.Rd b/man/pairsplot.Rd
@@ -40,7 +40,7 @@
 }
 
 \author{
-	Jose R. Zubizarreta <zubizarreta@columbia.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
+	Jose R. Zubizarreta <zubizarreta@hcp.med.harvard.edu>, Cinar Kilcioglu <ckilcioglu16@gsb.columbia.edu>.
 }
 
 \examples{