bage 0.9.8

Changes to internal calculations

• The update of the TMB to version 1.9.18 appears to have changed outputs, which caused a test involving replicate data to fail. The test has been make more robust, by generating more draws.

Bug fixes

• Function unfit() was not previously removing information on the "inner-outer" fitting method, making it appear that a model had been fit using this method, when it had in fact been re-fit using the standard method. This has been corrected.

bage 0.9.7

Changes to internal calculations

• The process for carrying out the Cholesky factorization and then drawing from the multivariate normal has been made more robust. When carrying out the Cholesky decomposition, small values are added to the diagonal at each failure, up to a limit that can be controlled by the user. Fallbacks (eg to using dense matrix calculations) have also been added to the function for generating multiple draws.

bage 0.9.6

Data models

• Added data models, also known as measurement error models. The main user-visible functions are set_datamod_exposure(), set_datamod_miscount(), set_datamod_noise(), set_datamod_over(), and set_datamod_under(). In introduction to data models is given in vignette 10.

Interface

• Extra checking of inputs for model constructor functions, eg checking that factors have at least two levels.

Changes to internal calculations

• Improved efficiency of some C++ code in the TMB template, through greater use of call by reference.

bage 0.9.5

Interface

• Added method n_draw() for querying the n_draw value of a model object.
• Deprecated the use of formulas for specifying exposure, size, or weights. Formulas complicate measurement error models and forecasts. Formulas will be completely removed in future.
• In replicate_data(), changed the default for condition_on to "fitted" in cases where dispersion is zero, since the "expected" option is not permitted in these cases.
• Extra checking of inputs for model constructor functions, eg checking that factors have at least two levels.

Documentation

• Extended documentation of Lin() prior in Mathematical Details vignette, and on online help, to describe case where s = 0. Also updated description of prior in online help to match current implementation.

Bug fixes

• Fixed bug in RR3, where values for rates were being calculated from confidentialized values of the outcome variable, rather than (imputed) true values.
• Fixed bug in aggregation of exposure for Poisson where exposure = 1, and aggregation of weights in normal models where weights = 1.

bage 0.9.4

Interface

• Modified help for mod_pois(), mod_binom(), mod_norm() to clarify prior for dispersion. Added default value (of 1) to set_disp(). This does not affect behaviour, but is a bit clearer for users. (#94)
• In set_covariates(), added paragraph in online documentation to note that calling set_covariates() on a model that already has covariates deletes the existing covariates. Also added a warning message to set_covariates() when covariates being overwritten. (#95).
• mod_pois() emits message (not warning) if one or more rates are suspiciously high (which is often a sympton of inaccurate exposures.) (#96)
• Added a guarded version of rpois_rvec(), which sets x[i] equal to lambda[i] for lambda[i] > 1e8. This avoids numeric problems which can lead to valgrind errors. The guarded version is called by augment() and by replicate_data(). The user is warned when the threshold of 1e8 is exceeded.

Bug fix

• Fixed bug in set_n_draw(), which was failing to thin covariate coefficient draws. (#97)

bage 0.9.3

Interface

• In the online help for mod_norm() and in the Mathematical Details vignette (vignette 2), give a new parameterisation of the mod_norm() model, expressed on the original scale, not the transformed scale (and closer to the paramterisation used by mod_pois() and mod_binom().)

Bug fix

• Modify the bage_mod_norm method for replicate_data() so that it returns results on the original scale.
• Modify the bage_mod_norm methods for the helper functions for augment() so that they properly incorporate weights.

bage 0.9.2

Interface

• Added original_scale argument to components(), to be used with normal models. Also added message remining users that, with normal models, components were on a log scale (#88)
• Added more information on progress.
• Tidied printing of model objects.
• Added new checks for outcome variable: no NaN and no Inf permitted.

Bug fixes

• Fixed bug introducted when fit_default() refactored in 0.9.1. Bug meant that when optimizer switched from nlminb() to optim() on non-convergence, optim() was not starting from old parameter values.

Documentation

• Added entry for RW2_Infant() to priors table. Hat-tip to Luke Morris for noticing that entry was missing. (#87)

bage 0.9.1

Changes to interface

• Added function set_covariates(). Models can now include covariates. Covariates are predictors other than the cross-classifying dimensions such as age, sex, or time – though covariates can be formed from these dimensions.
• Added variables gdp_pc_2023 and dens_2020 to dataset kor_births.
• Added prt_deaths dataset.
• Added set_seeds() function, allowing users to reset random seeds (though this would be uncommon in normal use.)

Changes to documentation

• Added covariates vignette.

Changes to internal calculations

• More careful handling of NAs in offset and predictor variables.

bage 0.9.0

• From 0.9.0 onwards we will use a formal deprecation make any breaking changes
• Tweaks to printing of "bage_mod" objects
• Started vignette replicating analyses from the book Bayesian Demographic Estimation and Forecasting

bage 0.8.6

Changes to interface

• Added "multi" option for optimizer argument to fit(). With "multi", the fit() function first tries nlminb() and if that fails switches to optim() with method "BFGS".
• Added a warning if the calculations do not converge
• Modified the printout for "bage_mod" objects to show the time spent by TMB::sdreport rather than the time spent by drawing from the multivariate normal (which, since bage started using sparseMVN, is very short).
• Finished vignette 1.
• Added covariates
• Added gdp_pc_2023 and dens_2020 variables to kor_births

bage 0.8.5

Changes to internal calculations

• report_sim() excludes comparisons of "hyper" parameters (eg standard deviations) if the simulation model and estimation model use different priors with different classes for that term. For instance if the simulation model uses a RW() prior for age and the estimation model uses a RW2() prior for age, then report_sim() will not report on the standard deviation parameter for age.
• Added warning to documentation for report_sim() stating that the interface is still under development.

bage 0.8.4

Changes to interface

• Changed zero_sum argument to con (short for “constraint”). con = "none" corresponds to zero_sum = FALSE, and con = "by" corresponds to zero_sum = TRUE. Additional options will be added in future.
• Added sd argument to RW(), RW2(), SVD_RW() and SVD_RW2(). The initial value of the random walks are drawn from a N(0, sd^2) prior. By default sd equals 1, but it can be set to
• Loosened restrictions in AR() and Lin_AR() priors so that the coefficients no longer need to be consistent with stationarity. The Stan user guide recommends against building in stationarity: https://mc-stan.org/docs/stan-users-guide/time-series.html#autoregressive.section Also, testing for stationarity often causes numerical problems.

bage 0.8.3

Changes to internal calculations

• Corrected bug in forecasting of AR() and Lin_AR() priors.
• Modified prior for coefficients of AR() and Lin_AR() priors, so that partical autocorrelation function (PACF), rather than the AR coefficients themselves, are restricted to (-1, 1). Restricting the PACF to (-1,1) ensures stationarity.

bage 0.8.2

Changes to interface

• Check to see that model object was created using current version of ‘bage’.
• Added optimizer argument to fit(), giving choice between three ways of optimizing
• Modifed behaviour of quiet argument to fit() so that when it is TRUE, trace output from the optimizer is shown.
• Added start_oldpar argument to fit(), to allow calculations to be restarted on a model that has already been fitted.
• Modified printing of "bage_mod" object.

bage 0.8.1

Changes to interface

• Modified construction of computations part of models so that it works with models fitted using the “inner-outer” method. Extended the print() method for "bage_mod" so that it shows extra output for models fitted using the "inner-outer" method.

bage 0.8.0

Changes to interface

• Added ‘along’ column to tidy and print methods for "bage_mod" objects. (Thank you to Andrew Taylor for suggesting this.)
• Allow s = 0 in Lin() priors
• Added zero_sum argument to priors with an along dimension. When zero_sum is TRUE, values for each combination of a by variable and the along variable are constrained to sum to zero. This can allow better identification of higher-level terms in complicated models. It can also slow computations, and has virtually no effect on estimates of the lowest-level rates, probabilities, and means.
• Removed post-estimation standardization. We now rely on explicit constraints instead to give interpretable values for main effects and interactions.
• Added RW2_Infant() prior for modelling age-patterns of mortality rates.
• The s_seas parameter in RW_Seas() and RW2_Seas() now defaults to 0, rather than 1, so that seasonal effects are by default fixed over time rather than varying. Using varying seasonal effects can greatly increase computation times.
• Moved rvec from Imports to Depends, so that it loads when bage is loaded. Manipulating results from bage models without rvec loaded can lead to strange errors.
• Added information on computations to printout from fitted model objects.
• Added function computations(), which can be used to extract this information from fitted model objects.
• Added quiet argument to fit(). When quiet is TRUE (the default), warnings generated by nlminb() are suppressed. (These warnings are virtually always about NAs early in the optimization process and are nothing to worry about.)

Changes to internal calculations

• Removed some unnecessary coercion of sparse matrices to dense matrices (which could sometimes cause memory problems)
• Added extra constraints to some priors - eg the first element of random walks is now zero. This often (but not always) helps make raw estimates of main effects and interactions more interpretable, and can speed up computations slightly.
• In the normal model, we now rescale the weights so that they have a mean of 1. This allows us to use the same default prior for dispersion (an exponential prior with mean 1), regardless of the original weights. The rescaling of the weights affects the estimated value for dispersion, but does not affect the estimates for any other parameters.
• Generation of posterior sample now using fast methods from package sparseMVN where possible.

bage 0.7.8

Datasets

• Added HFD, a scaled SVD object holding data from the Human Fertiltiy Database
• Changed names of data objects:
  • deaths –> isl_deaths
  • expenditure –> nld_expenditure
  • divorces –> nzl_divorces
  • injuries –> nzl_injuries
  • us_acc_deaths –> usa_deaths
• Added new data object kor_births, births in South Korea

bage 0.7.7

Bug fixes

• report_sim() now works on fitted models. Thank you to Ollie Pike for pointing out that it previously did not.
• Removed redundant levels from age variable in divorces.

Changes to internal calculations

• Removed internal bage function rr3(). Call poputils function rr3() instead.

bage 0.7.6

Changes to interface

• Added newdata argument to forecast().
• Added minimum version numbers for rvec and poputils.

bage 0.7.5.1

Bug fixes

• Fixed bug in code for simulating from Lin() and Lin_AR() priors.

bage 0.7.5

Changes to interface

• Added arguments method and vars_inner to fit(). When method is "standard" (the default) fit() uses the existing calculation methods. When method is "inner-outer", fit() uses a new, somewhat experimental calculation method that involves fitting an inner model using a subset of variables, and then an outer model using the remaining variables. With big datasets, "inner-outer" can be faster, and use less memory, but give very similar results.
• Added information on numbers of parameters, and standard deviations to output for print. Thank you to Duncan Elliot for suggesting printing numbers of parameters.

Changes to calculations

• fit() now internally aggregates input data before fitting, so that cells with the same combinations of predictor variables are combined. This increases speed and reduces memory usage.

Changes to documentation

• Added help for print.bage_mod

bage 0.7.4

Changes to interface

• Function ssvd() no longer exported. Will export once package bssvd matures.
• bage released on to CRAN

bage 0.7.3

Changes to data and examples

• Modified example for augment() so it runs faster
• Reduced size of divorces dataset

bage 0.7.2

Changes to interface

• 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 NAs, 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

bage 0.7.1

Changes to interface

• 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.

bage 0.7.0

Changes to calculations

• 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.

Changes to infrastructure

• Added Makevars file.

Changes to documentation

• 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.)

bage 0.6.3

Changes to calculations

• 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.

bage 0.6.2

Changes to interface

• 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.

Changes to data

• Object HMD now contains 5 components, rather than 10.

bage 0.6.1

Changes to calculations

• Fixed problems with standardization of forecast
• Added an intercept term to Lin() and LinAR() priors

bage 0.6.0

Issues

• Standardization of forecasts not working correctly.

Changes to interface

• Added priors SVD_AR(), SVDS_AR(), SVD_AR1(), SVDS_AR1(), SVD_RW(), SVDS_RW(), SVD_RW2(), SVDS_RW2()

Internal calculations

• 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.

bage 0.5.1

Changes to interface

• Moved HMD code to package bssvd.

bage 0.5.0

Changes to interface

• Combined interaction (eg ELin) and main effect (eg Lin) versions of priors
• Removed function compose_time()
• Added priors RWSeas and RW2Seas
• Improved report_sim()

bage 0.4.2

Changes to interface

• Tidying of online help (not yet complete).

bage 0.4.1

New functions

• Added ‘bage_ssvd’ method for components().

Changes to interface

• 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.

Internal calculations

• 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.

Simulations

• 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.

bage 0.4.0

Changes to back-end for SVD priors

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

Bug fixes

• Corrected error in calculation of logit in ssvd_comp().

bage 0.3.2

New functions

• forecast.bage_mod() Forecasting. Interface not yet finalised.

Bug fixes

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

bage 0.2.2

New functions

• generate.bage_ssvd() Generate random age-sex profiles from SVD.

Bug fixes

• Internal function draw_vals_effect_mod() was malfunctioning on models that contained SVD priors.