version 0.6.4

DESCRIPTION

@@ -1,15 +1,15 @@
 Package: bsts
-Date: 2016-03-10
+Date: 2016-08-02
 Title: Bayesian Structural Time Series
 Author: Steven L. Scott <[email protected]>
 Maintainer: Steven L. Scott <[email protected]>
 Description: Time series regression using dynamic linear models fit using MCMC.
 Depends: BoomSpikeSlab (>= 0.5.3), zoo, xts, Boom (>= 0.3), R(>= 3.1.0)
 LinkingTo: Boom, BH (>= 1.15.0-2)
-Version: 0.6.3
+Version: 0.6.4
 License: LGPL-2.1 | file LICENSE
 Encoding: UTF-8
 NeedsCompilation: yes
-Packaged: 2016-03-11 16:00:47 UTC; stevescott
+Packaged: 2016-08-02 16:47:46 UTC; stevescott
 Repository: CRAN
-Date/Publication: 2016-03-15 16:10:32
+Date/Publication: 2016-08-03 10:09:37

MD5

@@ -1,4 +1,4 @@
-db489ee2926cd564ad6ea09bf0a41e07 *DESCRIPTION
+b36acbcd3679bf49aaf8e8713bcc7339 *DESCRIPTION
 7eb09e6fd83eb49ed22911c3b2f06744 *LICENSE
 77b0e5e553b77ad75d4e4a27e6a709c9 *NAMESPACE
 3c5c0ab86b65f589f216f5b00493af8c *R/add.ar.R
@@ -72,16 +72,16 @@ d57163e548d8d3d98c73c076206cd4e2 *man/summary.bsts.Rd
 ba703f81ad4be6d3ac29a85e9c8e397c *man/timeseries.boxplot.Rd
 57af8d3de323423befb55d02ed034ada *man/week.ends.Rd
 5442c109b338e0fe1e159ab48a2bf06d *src/Makevars
-1eeee71e5c9b8d40de50d83b9660fa59 *src/aggregate_time_series.cc
-6e67221598474c74ae2647663d084b53 *src/bsts.cc
-78b9d1a324db8777ec461d6c23654880 *src/mixed_frequency.cc
+329f5c2a4b884556265575b56e7c0cc7 *src/aggregate_time_series.cc
+b9e63a061f7a09bc7a0ef13e660f1600 *src/bsts.cc
+f246aff45481d5394c45b65d58d1dfba *src/mixed_frequency.cc
 a018b30ec484c5246b0d8f4f5022eab3 *src/model_manager.cc
 be96d943440bcad54510a233ebda6ed8 *src/model_manager.h
 1fd9ab4d4c7476c1552b3bd2809ce92e *src/state_space_gaussian_model_manager.cc
 d94a7f45f987cd281697ad728b8358c8 *src/state_space_gaussian_model_manager.h
 78669ac7c6707568653f9178be79f222 *src/state_space_logit_model_manager.cc
 f4f89c92a9a1ec5d47525613351c7b68 *src/state_space_logit_model_manager.h
-b01c649ff283cee42350461cd0a440e3 *src/state_space_poisson_model_manager.cc
+0979c1dd250e7864245f800935e39a4e *src/state_space_poisson_model_manager.cc
 36526e9c92a816247aa58ca5ef5a2b47 *src/state_space_poisson_model_manager.h
 84529a90869531b8bb986ef9d140d1f5 *src/state_space_regression_model_manager.cc
 80e15c96df989cd2fde323ca89a7b515 *src/state_space_regression_model_manager.h

src/aggregate_time_series.cc

@@ -7,6 +7,7 @@
 #include "LinAlg/SubMatrix.hpp"
 #include "LinAlg/Types.hpp"
 #include "Rinternals.h"
+#include "r_interface/boom_r_tools.hpp"
 
 extern "C" {
  // Sum the results of a fine-scale time series to a coarser scale.
@@ -83,13 +84,13 @@ extern "C" {
  }
  }
 
- SEXP r_ans;
- PROTECT(r_ans = Rf_isMatrix(r_fine_series)
- ? Rf_allocMatrix(REALSXP, num_fine_rows, num_coarse_time_points)
- : Rf_allocVector(REALSXP, num_coarse_time_points));
+ BOOM::RMemoryProtector protector;
+ SEXP r_ans = protector.protect(
+ Rf_isMatrix(r_fine_series)
+ ? Rf_allocMatrix(REALSXP, num_fine_rows, num_coarse_time_points)
+ : Rf_allocVector(REALSXP, num_coarse_time_points));
  double *ans = REAL(r_ans);
  std::copy(coarse_series.begin(), coarse_series.end(), ans);
- UNPROTECT(1);
  return r_ans;
  }
 }

src/bsts.cc

@@ -1,3 +1,5 @@
+#include <ctime>
+
 #include "utils.h"
 #include "model_manager.h"
 
@@ -34,6 +36,7 @@ SEXP fit_bsts_model_(
  SEXP r_timeout_in_seconds,
  SEXP r_seed) {
  BOOM::RErrorReporter error_reporter;
+ BOOM::RMemoryProtector protector;
  try {
  BOOM::RInterface::seed_rng_from_R(r_seed);
  BOOM::RListIoManager io_manager;
@@ -67,7 +70,7 @@ SEXP fit_bsts_model_(
  int niter = lround(Rf_asReal(r_niter));
  int ping = lround(Rf_asReal(r_ping));
  double timeout_threshold_seconds = Rf_asReal(r_timeout_in_seconds);
- SEXP ans = PROTECT(io_manager.prepare_to_write(niter));
+ SEXP ans = protector.protect(io_manager.prepare_to_write(niter));
  clock_t start_time = clock();
  double time_threshold = CLOCKS_PER_SEC * timeout_threshold_seconds;
  for (int i = 0; i < niter; ++i) {
@@ -90,7 +93,6 @@ SEXP fit_bsts_model_(
  << double(current_time - start_time) / CLOCKS_PER_SEC
  << " seconds.";
  Rf_warning(warning.str().c_str());
- UNPROTECT(1);
  return BOOM::appendListElement(
  ans,
  ToRVector(BOOM::Vector(1, i + 1)),
@@ -103,13 +105,11 @@ SEXP fit_bsts_model_(
  << "iteration " << i << ". Aborting." << std::endl
  << e.what() << std::endl;
  error_reporter.SetError(err.str());
- UNPROTECT(1);
  return BOOM::appendListElement(ans,
  ToRVector(Vector(1, i)),
  "ngood");
  }
  }
- UNPROTECT(1);
  return ans;
  } catch (std::exception &e) {
  BOOM::RInterface::handle_exception(e);

src/mixed_frequency.cc

@@ -339,6 +339,7 @@ extern "C" {
  SEXP r_seed,
  SEXP r_truth) {
  RErrorReporter error_reporter;
+ BOOM::RMemoryProtector protector;
  try {
  BOOM::RInterface::seed_rng_from_R(r_seed);
 
@@ -378,7 +379,7 @@ extern "C" {
 
  int niter = Rf_asInteger(r_niter);
  int ping = Rf_asInteger(r_ping);
- SEXP ans = PROTECT(io_manager.prepare_to_write(niter));
+ SEXP ans = protector.protect(io_manager.prepare_to_write(niter));
 
  for (int i = 0; i < niter; ++i) {
  if (RCheckInterrupt()) {
@@ -411,7 +412,6 @@ extern "C" {
  return R_NilValue;
  }
  }
- UNPROTECT(1);
  return ans;
  } catch(std::exception &e) {
  handle_exception(e);

src/state_space_poisson_model_manager.cc

@@ -3,6 +3,8 @@
 
 #include "r_interface/prior_specification.hpp"
 
+#include "cpputil/math_utils.hpp"
+
 #include "Models/StateSpace/PosteriorSamplers/StateSpacePoissonPosteriorSampler.hpp"
 #include "Models/Glm/PosteriorSamplers/PoissonRegressionSpikeSlabSampler.hpp"
 #include "Models/Glm/PosteriorSamplers/PoissonDataImputer.hpp"
@@ -13,8 +15,18 @@ namespace BOOM {
 namespace bsts {
 
 namespace {
-typedef StateSpacePoissonModelManager SSPMM;
-}
+ typedef StateSpacePoissonModelManager SSPMM;
+ void zero_missing_values(
+ Vector *counts,
+ Vector *exposure,
+ const std::vector<bool> &observed) {
+ for (size_t i = 0; i < counts->size(); ++i) {
+ if (!observed[i]) {
+ (*counts)[i] = (*exposure)[i] = 0;
+ }
+ }
+ }
+} // namespace
 
 SSPMM::StateSpacePoissonModelManager()
  : predictor_dimension_(-1) {}
@@ -37,6 +49,7 @@ StateSpacePoissonModel * SSPMM::CreateObservationModel(
  Matrix(counts.size(), 1.0);
  std::vector<bool> response_is_observed(ToVectorBool(getListElement(
  r_data_list, "response.is.observed")));
+ zero_missing_values(&counts, &exposure, response_is_observed);
  model_.reset(
  new StateSpacePoissonModel(
  counts,
@@ -165,18 +178,18 @@ void SSPMM::AddData(const Vector &counts,
  const Matrix &predictors,
  const std::vector<bool> &is_observed) {
  for (int i = 0; i < counts.size(); ++i) {
+ bool missing = (!is_observed.empty() && !is_observed[i]);
  Ptr<StateSpace::AugmentedPoissonRegressionData> data_point(
  new StateSpace::AugmentedPoissonRegressionData(
- counts[i],
- exposure[i],
+ missing ? 0 : counts[i],
+ missing ? 0 : exposure[i],
  predictors.row(i)));
- if (!is_observed.empty() && !is_observed[i]) {
+ if (missing) {
  data_point->set_missing_status(Data::missing_status::completely_missing);
  }
  model_->add_data(data_point);
  }
 }
 
-
 } // namespace bsts
 } // namespace BOOM