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cran · Jun 7, 2019 · 58ed142 · 58ed142
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,18 +1,19 @@
 Package: bsts
-Date: 2019-05-13
+Date: 2019-06-03
 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. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many
  other sources.
-Depends: BoomSpikeSlab (>= 1.1.0), zoo, xts, Boom (>= 0.9), R(>= 3.4.0)
+Depends: BoomSpikeSlab (>= 1.1.1), zoo, xts, Boom (>= 0.9.1), R(>=
+ 3.4.0)
 Suggests: testthat
-LinkingTo: Boom (>= 0.9), BH (>= 1.65.0)
-Version: 0.9.0
+LinkingTo: Boom (>= 0.9.1), BH (>= 1.65.0)
+Version: 0.9.1
 License: LGPL-2.1 | file LICENSE
 Encoding: UTF-8
 NeedsCompilation: yes
-Packaged: 2019-05-21 17:07:09 UTC; stevescott
+Packaged: 2019-06-04 01:04:56 UTC; stevescott
 Repository: CRAN
-Date/Publication: 2019-05-21 22:20:15 UTC
+Date/Publication: 2019-06-07 17:40:03 UTC
diff --git a/MD5 b/MD5
@@ -1,10 +1,10 @@
-902ad3f6647f58dc4e2e1b9e21763e29 *DESCRIPTION
+2bb25dbc29c60add4f0ef6e371a47a87 *DESCRIPTION
 7eb09e6fd83eb49ed22911c3b2f06744 *LICENSE
-6099cca41a284ee8f5e462516399ee68 *NAMESPACE
+f37f71bbbb4895f02ea12ce0d71b23cf *NAMESPACE
 ceb07fe9db975f5c42496dfaa33a5d14 *R/add.ar.R
-47dd0950697038f716870139c7d970e8 *R/add.dynamic.regression.R
+67f4924e8412933001ff4b890dd18252 *R/add.dynamic.regression.R
 a2207f7a3e1753f1f3add83cc6b9dd7f *R/add.generalized.local.linear.trend.R
-b774c0069c3d41ef59b59a7b4af2a651 *R/add.local.level.R
+14576ffbd475455ed2de73f12a331a67 *R/add.local.level.R
 f9d3fe80f95e42e2cc4c2444c22bfbfd *R/add.local.linear.trend.R
 f0c5dc316a9e9472ea4073a526f93ac0 *R/add.monthly.annual.cycle.R
 c55b4c89ff05f1a9c4e9fd18b7fdcc54 *R/add.random.walk.holiday.R
@@ -14,22 +14,21 @@ d4f5df46c6a15e0d4e47ede074775446 *R/add.semilocal.linear.trend.R
 f965dd362d2321286bea83acd0b40c2d *R/add.static.intercept.R
 c69a20b8a59408f1c7e47c51c180b36b *R/add.student.local.linear.trend.R
 f39fba9e5105da6b1189fe08005204fe *R/add.trig.R
-b0f2197a3dbc4136b892e4aba110a690 *R/bsts.R
+dd0291e65ee3c1c9bceac80241bffec1 *R/bsts.R
 642f82708acd5ce362948999df6aaf2d *R/compare.bsts.models.R
 994c84adfa23228d724050d98c6a560e *R/date.functions.R
 6fe231fe1d4debe9e9edeb92eb269869 *R/diagnostics.R
-8b70727bed0ab3d005b08cfa9331a81a *R/dirm.R
-a71117be537cbee5a0abf977a519c15f *R/format.learning.data.R
-87e02726a155e84008705f0514948c35 *R/format.prediction.data.R
-eb20c475a1279437c2e7fceafb6a17cb *R/format.timestamps.R
+c73dd1a226eb5c0d9010c99f53b47dea *R/dirm.R
+f9fd8b0d488304fedbf69e65cfa9917b *R/format.learning.data.R
+b0f8f253bab664965b5efc3cafc9754a *R/format.timestamps.R
 769da1aee699d16ed0209b0f050c111e *R/holiday.R
-3574f7ba37b5f28360e7e2b84af1c66c *R/mbsts.R
-008c684c4b718a7829ee87102e8e3045 *R/mbsts.plots.R
+958b7e37110dd2b656fa7d8eef0c023d *R/mbsts.R
+0df474b37c5da953e054d8052274afa7 *R/mbsts.plots.R
 c2a346a0d66201f93f85d29b01575365 *R/mixed.frequency.R
 63bc374c03e9d515b6aca12da8729631 *R/plot_seasonal_effect.R
-5ae1e3f33d0906f21f1d951cb2c5ff3f *R/plots.R
-212f29e09d0b49f16c2bb309f4202d19 *R/predict.bsts.R
-ee2defc601d1bf213a13c42190763215 *R/predict.mbsts.R
+7818d79bd2fd7af3a2d38580b03fd63e *R/plots.R
+ebf99543ab66195b57c4274274846d72 *R/predict.bsts.R
+b7024ffed0c513c81afa41d821ac74fd *R/predict.mbsts.R
 09187c9baabe7cdb4a0301f38b5648f1 *R/summary.bsts.R
 04dc0efa0ef403afd48e252eba7e7271 *R/utils.R
 e707d635a33700be517ad31745786dc7 *data/gdp.RData
@@ -39,30 +38,19 @@ e96e68697531b9dbeffbded63b4ee8a2 *data/iclaims.RData
 371264a117afb763845fd25ac0492c60 *data/rsxfs.RData
 38a19670e134d95d2cc0ed134abe3539 *data/shark.RData
 761daf2618eaf1014ccc6500f3c82e61 *data/turkish.RData
-0465bac211a2e128119a6fbd17f19c56 *inst/tests/tests/testthat.R
-172df9e68c0f7876dcaade3768c2d5f9 *inst/tests/tests/testthat/test-date-range.R
-b4f9faed5df357aa818bca3b48598ffe *inst/tests/tests/testthat/test-dirm.R
-7698ef8c1adbeeaeca4036538cf2aeae *inst/tests/tests/testthat/test-dynamic-regression.R
-8e8e34dbc5a1d1735ec663091bcd03a9 *inst/tests/tests/testthat/test-holidays.R
-751344cdc3af1f935812c63ac2c29450 *inst/tests/tests/testthat/test-multivariate.R
-b8cf1c7b59eede2825f50fea5feac4c1 *inst/tests/tests/testthat/test-plot-components.R
-65a01ee43c3a462d7877d10e59dfd923 *inst/tests/tests/testthat/test-poisson.R
-905121aa9af3a048070013df7197dff6 *inst/tests/tests/testthat/test-prediction-errors.R
-c9cdb7f00e18460278079a78b6becc39 *inst/tests/tests/testthat/test-prediction.R
-6d0e435f602c43c1c921f580edef8b39 *inst/tests/tests/testthat/test-regressionholiday.R
-317f5cbb4b0b875a7cc3b7010ab004e8 *inst/tests/tests/testthat/test-seasonal.R
 0465bac211a2e128119a6fbd17f19c56 *inst/tests/testthat.R
 172df9e68c0f7876dcaade3768c2d5f9 *inst/tests/testthat/test-date-range.R
-b4f9faed5df357aa818bca3b48598ffe *inst/tests/testthat/test-dirm.R
+1caabb1def19e5a88b94e2d6ae0cb760 *inst/tests/testthat/test-dirm.R
 7698ef8c1adbeeaeca4036538cf2aeae *inst/tests/testthat/test-dynamic-regression.R
+236c11dceb0ea37b1966a706f7ac3a62 *inst/tests/testthat/test-goog.R
 8e8e34dbc5a1d1735ec663091bcd03a9 *inst/tests/testthat/test-holidays.R
-751344cdc3af1f935812c63ac2c29450 *inst/tests/testthat/test-multivariate.R
+8ab76fcd982a9ba00fd8b18dc9c0ae1b *inst/tests/testthat/test-multivariate.R
 b8cf1c7b59eede2825f50fea5feac4c1 *inst/tests/testthat/test-plot-components.R
 65a01ee43c3a462d7877d10e59dfd923 *inst/tests/testthat/test-poisson.R
 905121aa9af3a048070013df7197dff6 *inst/tests/testthat/test-prediction-errors.R
 c9cdb7f00e18460278079a78b6becc39 *inst/tests/testthat/test-prediction.R
 6d0e435f602c43c1c921f580edef8b39 *inst/tests/testthat/test-regressionholiday.R
-317f5cbb4b0b875a7cc3b7010ab004e8 *inst/tests/testthat/test-seasonal.R
+ebe710afd159f82025b7c8b9d6e6d237 *inst/tests/testthat/test-seasonal.R
 65ad45f30d10d63352989bcab06c428f *man/HarveyCumulator.Rd
 7d1eef9eb650d72d09f64620466950eb *man/MATCH.NumericTimestamps.Rd
 a96603175b3b0a7b3dca2bfbe5ec5e50 *man/StateSpecification.Rd
@@ -75,7 +63,7 @@ c6aeacc25ac38af7be02246a9994d21f *man/add.monthly.annual.cycle.Rd
 3168e8a6a9d823b0ef79dd27f7caf8ef *man/add.random.walk.holiday.Rd
 3860412aa5279acee97dc3bbf9e73502 *man/add.seasonal.Rd
 ec2b5eb7a98faf7b6651e4de4bbaa9ca *man/add.semilocal.linear.trend.Rd
-fef17e124129f0e3ee01965b5e436074 *man/add.shared.local.level.Rd
+ff357b94cc6095840d6fe1da30e1359f *man/add.shared.local.level.Rd
 253bb74766c2d284a73fb56ec7b33fa9 *man/add.static.intercept.Rd
 b02be1b8b61962090baccf3f454fa372 *man/add.student.local.linear.trend.Rd
 dd0f4a731a23a55e13853e991fd64d2c *man/add.trig.Rd
@@ -102,6 +90,7 @@ e7b9351ff2f8b0c117c1b2377303603d *man/geometric.sequence.Rd
 cf7b7d18e7252f5eccd0341eb26a6d82 *man/last.day.in.month.Rd
 d001026a7f503a1d7e4c3139b193bfe5 *man/match.week.to.month.Rd
 5c321a7f4189941f9d151f71627da8d2 *man/max.window.width.Rd
+9ddd53fb98bff70c7eb8dbeb5452e2d1 *man/mbsts.Rd
 88248ff37e7a3fa6f5ba97fb96e691a2 *man/mixed.frequency.Rd
 4c9639c7bfb85396081677060adc4541 *man/month.distance.Rd
 129cf408079a8ce4c6fea7e5e1c54515 *man/named.holidays.Rd
@@ -110,9 +99,10 @@ d001026a7f503a1d7e4c3139b193bfe5 *man/match.week.to.month.Rd
 58b0055a15f00de18cc481186c9de0f7 *man/plot.bsts.Rd
 4e091cfd04d4455c947e49959083e8a1 *man/plot.bsts.mixed.Rd
 6cab6053d05412eda8ea76b96595e315 *man/plot.bsts.prediction.Rd
-aebb0b01edc6c47838b60b4d211f8aed *man/plot.bsts.predictors.Rd
+9f639c8f4318529cf31ccc7b62ef84de *man/plot.bsts.predictors.Rd
 e00a3f3667c8ac2aec7154523fba3211 *man/plot.holiday.Rd
-c78ef0bf0a92aca42073a55eec89652b *man/predict.bsts.Rd
+c161911b74294d07949b76e552436682 *man/plot.mbsts.Rd
+1dc97ef1111f0cc768eac3d92ccca506 *man/predict.bsts.Rd
 e619692503d8decbd92a7c39b9d68327 *man/quarter.Rd
 abbc96c6de587b36dc1af2113a5845ce *man/regression.holiday.Rd
 c5b649ab31e29840874340433e251ccf *man/regularize.timestamps.Rd
@@ -132,24 +122,24 @@ cd4f3fcff595de3ac83d8e85ce89e627 *man/wide.to.long.Rd
 9ea4e3dffb7b8799efe134d1d9371acb *src/Makevars
 d99ce4ef93988dc2e1b7279ab92be31e *src/aggregate_time_series.cc
 14a5789b717b958f3e2bc746f491b57d *src/bsts.cc
-bff6def51621c46b8f5024c8149e93c8 *src/bsts_init.cc
+8c5caf631398cd06050d059691d443e3 *src/bsts_init.cc
 604673026495a954489f5e86602bc48f *src/create_dynamic_intercept_state_model.cpp
 2d60cd8ba33ef34e0238517e07bf7c9c *src/create_dynamic_intercept_state_model.h
-d236648c46dbd208e54602d31214a67f *src/create_shared_state_model.cpp
+ff29e23d5ee4a48e85701d174decedc5 *src/create_shared_state_model.cpp
 031ec35337c11a613f10c17a91068d39 *src/create_shared_state_model.h
 08bed54fdcb67b94a118ffcfd1f45ac7 *src/create_state_model.cpp
 17f6c061340e704c7cb578fcb9c4444f *src/create_state_model.h
 4a0ab82799effea8a0f2998cfb9302f7 *src/dirm.cc
 74b446972e23f99dd11f43f73a134757 *src/dynamic_intercept_model_manager.cc
 f4b47e0c03ea817b60f652813b16fb06 *src/dynamic_intercept_model_manager.h
 e6f88ebc8a3c65c29fd14268e57fe4d9 *src/get_date_ranges.cc
-fb1fc1eb0caf37cc16d259bd6fa51688 *src/mbsts.cc
+24b148a40167cebf2b6f8642338c28c4 *src/mbsts.cc
 a739fc5cf1bbccb1ae83860c48992b06 *src/mixed_frequency.cc
-85796decbb76e176790f094f15337c2e *src/model_manager.cc
-40ff9e892e2c9c938555c56faa3f80de *src/model_manager.h
-5c8511a5844aed9ce31ec22c693c99df *src/multivariate_gaussian_model_manager.cc
-625e926ea16e39badcb9ad340d02aac1 *src/multivariate_gaussian_model_manager.h
-db277610eab7021c898a361d6da99b7f *src/state_space_gaussian_model_manager.cc
+c307a1f0c70a9051add6e5c57cdc4e6d *src/model_manager.cc
+97f9897acbd70f8f4ed958bb51ea4051 *src/model_manager.h
+7916fd1c9323fb225663cc70aa92f0ae *src/multivariate_gaussian_model_manager.cc
+93eae1ab5fe99f38d0c643687ce02683 *src/multivariate_gaussian_model_manager.h
+82d4ded6a46e394a9e9bfabeb59d9621 *src/state_space_gaussian_model_manager.cc
 2bedea75559c6e5f0a0bd7d3af6090e8 *src/state_space_gaussian_model_manager.h
 a432aebec2404eb926fe13e9bcb5fb25 *src/state_space_logit_model_manager.cc
 3f80d659d96c0f9d08cab47a32a2a9b7 *src/state_space_logit_model_manager.h
@@ -160,16 +150,4 @@ d12a59708f82f866ed02944f5431bb59 *src/state_space_regression_model_manager.cc
 ca0a7d4075cfcfaa3c975feae0d96249 *src/state_space_student_model_manager.cc
 70d7c3d65ab85979568d692b4a217f9f *src/state_space_student_model_manager.h
 758355f181bebfed943c020342250e5e *src/utils.cc
-fc372ceb51d515b3683b55248972d590 *src/utils.h
-0465bac211a2e128119a6fbd17f19c56 *tests/testthat.R
-172df9e68c0f7876dcaade3768c2d5f9 *tests/testthat/test-date-range.R
-fd8ee9ad4b55f92739f91279bb808452 *tests/testthat/test-dirm.R
-7698ef8c1adbeeaeca4036538cf2aeae *tests/testthat/test-dynamic-regression.R
-8e8e34dbc5a1d1735ec663091bcd03a9 *tests/testthat/test-holidays.R
-8ab76fcd982a9ba00fd8b18dc9c0ae1b *tests/testthat/test-multivariate.R
-b8cf1c7b59eede2825f50fea5feac4c1 *tests/testthat/test-plot-components.R
-65a01ee43c3a462d7877d10e59dfd923 *tests/testthat/test-poisson.R
-905121aa9af3a048070013df7197dff6 *tests/testthat/test-prediction-errors.R
-c9cdb7f00e18460278079a78b6becc39 *tests/testthat/test-prediction.R
-6d0e435f602c43c1c921f580edef8b39 *tests/testthat/test-regressionholiday.R
-8282070b888b834e69ca9ba187ff864b *tests/testthat/test-seasonal.R
+9b299499f0d977e24d5e96e61872bf62 *src/utils.h
diff --git a/NAMESPACE b/NAMESPACE
@@ -65,7 +65,7 @@ export(AcfDist,
  MaxWindowWidth,
  MaxWindowWidth.default,
  MaxWindowWidth.DateRangeHoliday,
-# mbsts,
+ mbsts,
  MonthDistance,
  MonthPlot,
  NamedHoliday,
@@ -119,8 +119,9 @@ S3method(residuals, bsts)
 S3method(plot, bsts.prediction)
 S3method(plot, bsts.mixed)
 
-#S3method(plot, mbsts)
-#S3method(predict, mbsts)
+S3method(plot, mbsts)
+S3method(predict, mbsts)
+S3method(plot, mbsts.prediction)
 
 # Plot methods for specific state components
 S3method(plot, StateModel)

diff --git a/R/add.dynamic.regression.R b/R/add.dynamic.regression.R
@@ -34,6 +34,7 @@ DynamicRegressionRandomWalkOptions <- function(
  ## Returns:
  ## An object that can be passed to AddDynamicRegression as the model.options argument. 
  if (!is.null(sigma.prior)) {
+ # sigma.prior must either be an SdPrior or a list of SdPrior objects.
  stopifnot(inherits(sigma.prior, "SdPrior")
  || is.list(sigma.prior) && all(sapply(sigma.prior, inherits, "SdPrior")))
  } else {

diff --git a/R/add.local.level.R b/R/add.local.level.R
@@ -17,23 +17,22 @@
 AddSharedLocalLevel <- function(state.specification,
  response,
  nfactors, 
- sigma.prior = NULL,
  coefficient.prior = NULL,
  initial.state.prior = NULL,
  timestamps = NULL,
  series.id = NULL,
- sdy) {
+ sdy,
+ ...) {
  ## A local level model for multivariate time series. If the state of the
  ## model is alpha[t] then the state equation is
  ##
  ## y[t] = Z * alpha[t] + epsilon[t]
  ## alpha[t + 1] = alpha[t] + eta[t]
  ##
- ## The variance of eta[t] is diagonal, with elements sigma_eta^2[k]. The
- ## coefficient matrix Z is not time varying. It is zero above the diagonal in
- ## order to preserve identifiability. This means that the first time series
- ## is only affected by the first factor. The second is affected by the first
- ## and second factors, etc.
+ ## For identification purposes, the variance of eta[t] is the identity matrix,
+ ## and the coefficient matrix Z It is zero above the diagonal. This means
+ ## that the first time series is only affected by the first factor. The
+ ## second is affected by the first and second factors, etc. 
  ##
  ## Args:
  ## state.specification: A list of state components to which a shared local
@@ -42,16 +41,15 @@ AddSharedLocalLevel <- function(state.specification,
  ## time points and columns are variables. This argument can be omitted if
  ## sdy is provided.
  ## nfactors: An integer giving the number of latent factors in the model.
- ## sigma.prior: The prior distribution for the innovation standard deviation
- ## (sigma_eta) in the local level model. It can be specified in one of
- ## three ways.
- ## - It can be an object created by SdPrior, in which case the same prior
- ## is used for all factors.
- ## - It can be a list of SdPrior objects, one for each factor.
- ## - It can be NULL, in which case a default prior will be used, based on
- ## the sample mean and variance of the observed data.
- ## coefficient.prior: Matrix normal prior, or NULL. Spike and slab version
- ## coming soon.
+ ## coefficient.prior: An object (or a list of objects) inheriting from
+ ## SpikeSlabPriorBase. If a list is passed it must have 'nseries'
+ ## elements, where 'nseries' is the number of time series being modeled.
+ ## If a single object is passed it will be copied into a list of 'nseries'
+ ## identical prior objects. List element i specifies the prior
+ ## distribution on the set of observation coefficients for time series i.
+ ## Note that identifiability constriants will be imposed by underlying
+ ## code, so that if series k < 'nfactors', only the first 'k' factors will
+ ## have positive prior probability for series k.
  ## initial.state.prior: An object created by MvnPrior giving the prior
  ## distribution on the values of the initial state (i.e. the state as of
  ## the first observation).
@@ -63,10 +61,12 @@ AddSharedLocalLevel <- function(state.specification,
  ## sdy: A vector giving the sample standard deviation for each column in y.
  ## This will be ignored if y is provided, or if both sigma.prior and
  ## initial.state.prior are supplied directly.
+ ## ...: Extra arguments passed to ConditionalZellnerPrior, as a prior on the
+ ## observation coefficients.
  ##
  ## Returns:
  ## state.specification, after appending the information necessary
- ## to define a local level model
+ ## to define a shared local level model.
  if (missing(state.specification)) state.specification <- list()
  stopifnot(is.list(state.specification))
  stopifnot(is.numeric(nfactors), length(nfactors) == 1, nfactors >= 1)
@@ -80,49 +80,40 @@ AddSharedLocalLevel <- function(state.specification,
  }
  stopifnot(is.matrix(response.matrix))
  sdy <- apply(response.matrix, 2, sd, na.rm = TRUE)
+ series.names <- colnames(response.matrix)
  }
  stopifnot(is.numeric(sdy), all(sdy > 0))
- ydim <- length(sdy)
+ nseries <- length(sdy)
+ if (nseries < 1) {
+ stop("There are no time series to model.")
+ }
 
  nfactors <- as.integer(nfactors)
  stopifnot(length(nfactors) == 1)
 
- ##----------------------------------------------------------------------
- ## Set the prior on the innovation variances.
- ##----------------------------------------------------------------------
- if (is.null(sigma.prior)) {
- ## The prior distribution says that sigma is small, and can be no
- ## larger than the sample standard deviation of the time series
- ## being modeled.
- sigma.prior <- list()
- for (i in 1:nfactors) {
- sigma.prior[[i]] <- SdPrior(sigma.guess = .01 * sdy[i],
- sample.size = 1,
- upper.limit = sdy[i])
- }
- }
- if (inherits(sigma.prior, "SdPrior")) {
- sigma.prior <- Boom::RepList(sigma.prior, nfactors)
- }
- # Ensure that sigma.prior is a list of length 'nfactors' containing SdPrior
- # elements.
- stopifnot(is.list(sigma.prior),
- length(sigma.prior) == nfactors,
- all(sapply(sigma.prior, inherits, "SdPrior")))
-
-
  ##----------------------------------------------------------------------
  # Set the prior on the observation coefficients.
  ##----------------------------------------------------------------------
- # The coefficients Z satisfy Y[t] = Z * alpha, so the coefficients ydim rows
- # and nfactors columns.
+ # The coefficients Z satisfy Y[t] = Z * alpha, so the coefficients have
+ # 'nseries' rows and 'nfactors' columns.
  if (is.null(coefficient.prior)) {
- #### TBD
- coefficient.prior <- ScaledMatrixNormalPrior(
- mean = matrix(0, nrow = ydim, ncol = nfactors),
- nu = 1.0)
+ coefficient.prior <- list()
+ for (i in 1:nseries) {
+ ## Normally regression coefficients are centered around zero. In this
+ ## case it makes sense to center around 1. Really this should be a
+ ## hierarchical prior where it makes sense to center the hyperprior around
+ ## 1. TODO(steve)
+ coefficient.prior[[i]] <- ConditionalZellnerPrior(
+ nfactors,
+ optional.coefficient.estimate = rep(1, nfactors),
+ ...)
+ }
+ }
+ if (inherits(coefficient.prior, "ConditionalZellnerPrior")) {
+ coefficient.prior <- RepList(coefficient.prior, nseries)
  }
- stopifnot(inherits(coefficient.prior, "ScaledMatrixNormalPrior"))
+ stopifnot(is.list(coefficient.prior),
+ all(sapply(coefficient.prior, inherits, "ConditionalZellnerPrior")))
 
  ##----------------------------------------------------------------------
  ## Set the prior on the initial state.
@@ -140,9 +131,9 @@ AddSharedLocalLevel <- function(state.specification,
  length(initial.state.prior$mean) == nfactors)
 
  level <- list(name = "trend",
- innovation.precision.priors = sigma.prior,
- coefficient.prior = coefficient.prior,
+ coefficient.priors = coefficient.prior,
  initial.state.prior = initial.state.prior,
+ series.names = colnames(response.matrix),
  size = nfactors)
  class(level) <- c("SharedLocalLevel", "SharedStateModel")
 

diff --git a/R/bsts.R b/R/bsts.R
@@ -201,7 +201,7 @@ bsts <- function(formula,
  ## section above.
  data <- NULL
  }
- timestamp.info <- .ComputeTimestampInfo(response, data, timestamps)
+ timestamp.info <- TimestampInfo(response, data, timestamps)
  formatted.data.and.options <- .FormatBstsDataAndOptions(
  family, response, predictors, model.options, timestamp.info)
  data.list <- formatted.data.and.options$data.list

diff --git a/R/dirm.R b/R/dirm.R
@@ -116,7 +116,7 @@ dirm <- function(formula,
  ## section above.
  data <- NULL
  }
- timestamp.info <- .ComputeTimestampInfo(response, data, timestamps)
+ timestamp.info <- TimestampInfo(response, data, timestamps)
 
  data.list <- list(response = as.numeric(response),
  predictors = predictors,