bage-package | R Documentation |
Bayesian estimation and forecasting of age-specific rates. Estimation uses TMB, and is fast.
Specify model using mod_pois()
Fit model using fit()
Extract results using augment()
Check model using replicate_data()
Specify model
mod_pois()
Specify a Poisson model
mod_binom()
Specify a binomial model
mod_norm()
Specify a normal model
set_prior()
Specify prior for main effect or interaction
priors List of available priors for main effects or interactions
set_disp()
Specify prior for dispersion/variance
set_var_age()
Identify age variable in data
set_var_sexgender()
Identify sex or gender variable in data
set_var_time()
Identify time variable in data
Fit model
fit()
Derive posterior distribution
is_fitted()
See if model has been fitted
Extract results
augment()
Add cell-level estimates to data
components() Hyper-parameters
tidy()
One-line summary
set_n_draw()
Specify number of prior or posterior draws
Forecast
forecast()
Use model to obtain estimates for future periods
Check model
replicate_data()
Compare real and simulated data
report_sim()
Simulation study of model
SVD-based modelling of age profiles
components() Matrices and offsets from scaled SVD
generate() Random profiles
HFD Scaled SVD of fertility rates from Human Fertility Database
HMD Scaled SVD of mortality rates from Human Mortality Database
LFP Scaled SVD of labor force participation rates from OECD
Data
isl_deaths Deaths in Iceland
kor_births Births in South Korea
nld_expenditure Health expenditure in the Netherlands
nzl_divorces Divorces in New Zealand
nzl_households One-person households in New Zealand
nzl_injuries Fatal injuries in New Zealand
swe_infant Infant mortality in Sweden
usa_deaths Accidental deaths in the USA
Maintainer: John Bryant john@bayesiandemography.com
Authors:
Junni Zhang junnizhang@163.com
Other contributors:
Bayesian Demography Limited [copyright holder]
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