## dgo 0.2.14

• Avoid an error during testing, on R built --without-long-double.

## dgo 0.2.13

• Fix an issue introduced in v0.2.12 that led to an unexpected error in shape() when 1) at least two group_names are specified in an order other than alphabetic and 2) geographic modifier_data is used.

## dgo 0.2.12

• Allow modeling of unobserved groups using aggregated data. The previous behavior was to drop rows in aggregate_data indicating zero trials. (They don’t represent item responses.) Preserving them has the effect that unobserved groups, defined partially or entirely by the values of the grouping variables in zero-trial rows in aggregate_data, can be included in a model.
• Fix an unexpected error when 1) aggregate_data is used without item_data, 2) no demographic groups are specified via group_names, and 3) geographic modifier_data is used.
• Fix the check for missing modifier_data. Geographic modifier_data must cover all combinations of the geo and time variables in the item response data (individual or aggregated), but because of a bug in the validation of the geographic data, this requirement was not always enforced. In some cases a warning would appear instead of an error.

## dgo 0.2.11

• Add poststratification over posterior samples (closes #21).
• shape() now accepts aggregated item response data unaccompanied by individual-level item response data. The item_data and item_names arguments are no longer required.
• Add a max_raked_weight argument to shape() for trimming raked weights. Note that trimming occurs before raked weights are rescaled to have mean 1, and the rescaled weights can be larger than max_raked_weight.
• Remove the unused function expand_rownames().
• Bugfixes.

## dgo 0.2.10

• Remove Rcpp dependency by rewriting dichotomize() in R.
• Avoid estimating models (using RStan) during tests, with the goal of rendering moot variation in build environments. This addresses a test failure during CRAN’s r-release-osx-x86_64 build.

## dgo 0.2.9

• Switch from compiling Stan models at install time to compiling them at runtime, avoiding an Rcpp module issue.
• Add model argument to dgirt() and dgmrp() taking for reuse a previously compiled Stan model, as found in the @stanmodel slot of a dgirt_fit- or dgmrp_fit-class object.
• The version argument to dgirt() and dgmrp() can be used to specify arbitrary .stan files on the disk in addition to those included with the package.
• Argument by to get_n() and get_item_n() methods properly accepts a vector of variable names when combined with aggregate arguments.

## dgo 0.2.8

• Improve Stan models for shorter run times
• Add dgmrp() for fitting single-issue MRP models with hierarchical covariates
• Add class dgmrp_fit for models fitted with dgmrp(), inheriting from a new virtual class dgo_fit
• dgirt() now returns a dgirt_fit-class object that also inherits from dgo_fit class
• Bugfixes

## dgo 0.2.7

• Package renamed dgo: Dynamic Estimation of Group-level Opinion
• Tweaks to pass CRAN checks: clean up examples and docs
• Use roxygen2 for classes, methods, and NAMESPACE
• Fix checks on P, S related to group_names change in 0.2.5
• Fix Rcpp module issue from 0.2.6 (Error in .doLoadActions(where, attach))
• Export expand_rownames()

## dgo 0.2.6

• Fix error in dgirt_plot
• Fix path in tools/make_cpp.R

## dgo 0.2.5

• group_names is no longer required. If omitted, the geographic variable given by geo_name will define groups.
• aggregate_item_names is no longer required. It defaults to the observed values of the item column in aggregate_data.
• raking argument to shape() replaces strata_names. It takes a formula or list of formulas and allows more complicated preweighting.
• id_vars argument to shape() specifies variables to be kept in item_data.
• aggregate_data may include geographic areas, demographics, or time periods that don’t appear in item_data.
• Fix: use a smaller epsilon than the default in survey::rake() for convergence with non-frequency weights.
• New dgirtfit methods rhats() and plot_rhats() for model checking.
• New dgirtfit method get_time_elapsed gives model run times. These also appear in summary output.