- Modified example for
`augment()`

so it runs faster - Reduced size of
`divorces`

dataset

- Added first data model. New function is
`set_datamod_outcome_rr3()`

, which deals with the case where the outcome variable has been randomly rounded to base 3. `augment()`

now creates a new version of the outcome variable if (i) the outcome variable has`NA`

s, or (ii) a data model is being applied to the outcome variable. The name of the new variable is created by added a`.`

to the start of the name of the outcome variable.- A help page summarising available data models

- There are now three choices for the
`standardization`

argument:`"terms"`

,`"anova"`

, and`"none"`

. With`"terms"`

, all effects, plus assoicated SVD coefficients, and trend, cyclical, and seasonal terms, are centered independently. With`"anova"`

, the type of standardization descibed in Section 15.6 of Gelman et al (2014) Bayesian Data Analysis, is applied to the effects.

- Further simplification of standardization, but likely in future to split into two types of standardization: one that gives an ANOVA-style decomposition of effects, and one that helps with understanding the dynamics of each term.

- Added Makevars file.

- Stopped referring to second-order walks as equivalent to random walks with drift. (A second-order random walk differs from a random walk in that the implied drift term in a second-order random walk can vary over time.)

- Changed standardization of forecasts so that forecasts are standardized along the ‘along’ dimension by choosing the values that makes them consistent with time trends in the estimation period, and then standardizing within each value of the along dimensions.

- Removed
`SVDS()`

,`SVDS_AR()`

,`SVDS_AR1()`

,`SVDS_RW()`

, and`SVDS_RW2()`

priors. Added`indep`

argument to corresponding`SVD`

priors.`SVD`

priors now choose between ‘total’, ‘independent’ and ‘joint’ models based on (1) the value of`indep`

argument, (2) the value of`var_sexgender`

and the name of the term.

- Object
`HMD`

now contains 5 components, rather than 10.

- Fixed problems with standardization of forecast
- Added an intercept term to
`Lin()`

and`LinAR()`

priors

- Standardization of forecasts not working correctly.

- Added priors
`SVD_AR()`

,`SVDS_AR()`

,`SVD_AR1()`

,`SVDS_AR1()`

,`SVD_RW()`

,`SVDS_RW()`

,`SVD_RW2()`

,`SVDS_RW2()`

- Changed values that are stored in object: removed
`draws_linpred`

, added`draws_effectfree`

,`draws_spline`

, and`draws_svd`

. Modified/added downstream functions. - Calculation of ‘along_by’ and ‘agesex’ matrices pushed downwards into lower-level functions.

- Moved HMD code to package
**bssvd**.

- Combined interaction (eg ELin) and main effect (eg Lin) versions of priors
- Removed function
`compose_time()`

- Added priors RWSeas and RW2Seas
- Improved
`report_sim()`

- Tidying of online help (not yet complete).

- Added ‘bage_ssvd’ method for
`components()`

.

`augment()`

method for`bage_mod`

objects now calculated value for`.fitted`

in cases where the outcome or exposure/size is NA, rather than setting the value of`.fitted`

to`NA`

.

- Standardization of effects only done if
`components()`

is called.`augment()`

uses the linear predictor (which does not need standardization.) - Internally, draws for the linear predictor, the hyper-parameters and
(if included in model)
`disp`

are stored, rather than the full standardized components. - Standardization algorithm repeats up to 100 times, or until all residuals are less than 0.0001.
- With the new configuration, calculations for large matrices that previously failed with error message “Internal error: Final residual not 0” are now running.

- When drawing from the prior, the intercept is always set to 0. Terms with SVD or Known priors are not touched. All other terms are centered.

- Move most functions for creating ‘bage_ssvd’ objects to package ‘bssvd’.
- Allowed number of components of a ‘bage_ssvd’ object to differ from

- Corrected error in calculation of logit in
`ssvd_comp()`

.

`forecast.bage_mod()`

Forecasting. Interface not yet finalised.

- Corrected error in C++ template for Lin and ELin priors (due to use of integer arithmetic.)

`generate.bage_ssvd()`

Generate random age-sex profiles from SVD.

- Internal function
`draw_vals_effect_mod()`

was malfunctioning on models that contained SVD priors.