version 1.2-22

DESCRIPTION

@@ -1,19 +1,19 @@
 Package: PrivateLR
 Type: Package
 Title: Differentially Private Regularized Logistic Regression
-Version: 1.2-21
-Date: 2014-10-30
-Author: Staal A. Vinterbo <sav@ucsd.edu>
-Maintainer: Staal A. Vinterbo <sav@ucsd.edu>
-Description: PrivateLR implements two differentially private algorithms for 
+Version: 1.2-22
+Date: 2018-03-19
+Author: Staal A. Vinterbo <Staal.Vinterbo@ntnu.no>
+Maintainer: Staal A. Vinterbo <Staal.Vinterbo@ntnu.no>
+Description: Implements two differentially private algorithms for 
   estimating L2-regularized logistic regression coefficients. A randomized
   algorithm F is epsilon-differentially private (C. Dwork, Differential
-  Privacy, ICALP 2006), if 
+  Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if 
      |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
-  for any pair D, D' of datasets that differ in exactly one element, any
-  set S, and the randomness is taken over the choices F makes. 
+  for any pair D, D' of datasets that differ in exactly one record, any
+  measurable set S, and the randomness is taken over the choices F makes. 
 License: GPL (>= 2)
-Packaged: 2014-10-31 05:05:11 UTC; staal
 NeedsCompilation: no
+Packaged: 2018-03-19 21:41:39 UTC; staal
 Repository: CRAN
-Date/Publication: 2014-10-31 16:16:00
+Date/Publication: 2018-03-20 13:48:35 UTC

MD5

@@ -1,4 +1,4 @@
-453146e489da36f241a05c811d09c10b *DESCRIPTION
-4dce9a2e923a228182d6b85fe5b5d22d *NAMESPACE
+e96bf4ccb68a24e4d00e2520149ed385 *DESCRIPTION
+8eaaef223f9afe672ccb8c2eecb6f58d *NAMESPACE
 4949d8430551e2dd216063344ffd6652 *R/dplr.r
 fac86d90a7c6e283c90732226525539f *man/dplr.Rd

NAMESPACE

@@ -10,4 +10,20 @@ export(dplr,
        summary.dplr,
        print.summary.dplr,       
        scaled)
-
+
+S3method(dplr, formula)
+S3method(dplr, numeric)
+S3method(dplr, factor)
+S3method(dplr, logical)
+S3method(dplr, data.frame)
+S3method(dplr, matrix)
+
+
+S3method(predict, dplr)
+S3method(print, dplr)
+S3method(print, summary.dplr)
+S3method(summary, dplr)
+
+importFrom("stats", "as.formula", "delete.response", "formula",
+             "model.frame", "model.matrix", "model.response", "optim",
+             "predict", "rexp", "rnorm", "terms")