version 0.6.1

Author: Steven L. Scott

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: bsts
 Date: 2014-12-03
-Title: Bayesian structural time series
+Title: Bayesian Structural Time Series
 Author: Steven L. Scott <stevescott@google.com>
 Maintainer: Steven L. Scott <stevescott@google.com>
 Description: Time series regression using dynamic linear models fit using MCMC.
 Depends: BoomSpikeSlab (>= 0.5.1), zoo, xts, Boom (>= 0.2), R(>= 3.1.0)
 LinkingTo: Boom, BH (>= 1.15.0-2)
-Version: 0.6.0
+Version: 0.6.1
 License: LGPL-2.1 | file LICENSE
-Packaged: 2014-12-03 15:08:04 UTC; stevescott
+Packaged: 2014-12-04 17:36:40 UTC; stevescott
 NeedsCompilation: yes
 Repository: CRAN
-Date/Publication: 2014-12-03 17:02:04
+Date/Publication: 2014-12-05 01:22:51

MD5

@@ -1,4 +1,4 @@
-685a1e634082209eab685fc578e1bc6d *DESCRIPTION
+7347e81053bb36cf4b795cdb77e97105 *DESCRIPTION
 7eb09e6fd83eb49ed22911c3b2f06744 *LICENSE
 77a1cc0e604e69b9fbe98f647d945258 *NAMESPACE
 3c5c0ab86b65f589f216f5b00493af8c *R/add.ar.R
@@ -64,7 +64,7 @@ ba703f81ad4be6d3ac29a85e9c8e397c *man/timeseries.boxplot.Rd
 57af8d3de323423befb55d02ed034ada *man/week.ends.Rd
 5442c109b338e0fe1e159ab48a2bf06d *src/Makevars
 1eeee71e5c9b8d40de50d83b9660fa59 *src/aggregate_time_series.cc
-b869b6b69d427bb121d7f7ff73c26a2c *src/btlm.cc
+991425c35941fec9aa0e5e4c563cb4fe *src/btlm.cc
 e5610cf3eb282eda96082c201d9898cf *src/mixed_frequency.cc
 454d267829019300866ac3ef1c33478d *src/state_space.cc
 84f1ec8d86f3f7b3d56930b0b4731fe0 *src/utils.h

src/btlm.cc

@@ -93,9 +93,12 @@ class RegressionStateContributionCallback : public BOOM::MatrixIoCallback {
   // row for the regression effects.  The fact that the regression is
   // implemented differently than the other elements of state is an
   // unfortunate implementation detail.
-  int nrow() const override {return 1 + model_->nstate();}
-  int ncol() const override {return model_->time_dimension();}
-  BOOM::Matrix get_matrix() const override {
+  //
+  // TODO(stevescott): Change the leading 'virtual' to a trailing
+  // 'override' once CRAN supports C++11 on windows.
+  virtual int nrow() const {return 1 + model_->nstate();}
+  virtual int ncol() const {return model_->time_dimension();}
+  virtual BOOM::Matrix get_matrix() const {
     BOOM::Matrix ans(nrow(), ncol());
     for (int state = 0; state < model_->nstate(); ++state) {
       ans.row(state) = model_->state_contribution(state);