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bage

Fast Bayesian estimation and forecasting of age-specific rates.

Installation

install.packages("bage") ## CRAN version
devtools::install_github("bayesiandemography/bage") ## development version

Example

Fit Poisson model to data on injuries

library(bage)
mod <- mod_pois(injuries ~ age:sex + ethnicity + year,
                data = nzl_injuries,
                exposure = popn) |>
  fit()
mod
#> 
#>     ------ Fitted Poisson model ------
#> 
#>    injuries ~ age:sex + ethnicity + year
#> 
#>   exposure = popn
#> 
#>         term  prior along n_par n_par_free std_dev
#>  (Intercept) NFix()     -     1          1       -
#>    ethnicity NFix()     -     2          2    0.45
#>         year   RW()  year    19         19    0.09
#>      age:sex   RW()   age    24         24    0.88
#> 
#>  disp: mean = 1
#> 
#>  n_draw var_time var_age var_sexgender optimizer
#>    1000     year     age           sex     multi
#> 
#>  time_total time_optim time_report iter converged                    message
#>        1.04       0.31        0.30   11      TRUE   relative convergence (4)

Extract model-based and direct estimates

augment(mod)
#> # A tibble: 912 × 9
#>    age   sex    ethnicity  year injuries  popn .observed
#>    <fct> <chr>  <chr>     <int>    <int> <int>     <dbl>
#>  1 0-4   Female Maori      2000       12 35830 0.000335 
#>  2 5-9   Female Maori      2000        6 35120 0.000171 
#>  3 10-14 Female Maori      2000        3 32830 0.0000914
#>  4 15-19 Female Maori      2000        6 27130 0.000221 
#>  5 20-24 Female Maori      2000        6 24380 0.000246 
#>  6 25-29 Female Maori      2000        6 24160 0.000248 
#>  7 30-34 Female Maori      2000       12 22560 0.000532 
#>  8 35-39 Female Maori      2000        3 22230 0.000135 
#>  9 40-44 Female Maori      2000        6 18130 0.000331 
#> 10 45-49 Female Maori      2000        6 13770 0.000436 
#> # ℹ 902 more rows
#> # ℹ 2 more variables: .fitted <rdbl<1000>>, .expected <rdbl<1000>>

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Version

Install

install.packages('bage')

Monthly Downloads

155

Version

0.9.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

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Maintainer

John Bryant

Last Published

January 8th, 2025

Functions in bage (0.9.0)

RW2_Infant

Second-Order Random Walk Prior with 'Infant' Indicator
RW2

Second-Order Random Walk Prior
NFix

Normal Prior with Fixed Variance
RW2_Seas

Second-Order Random Walk Prior with Seasonal Effect
augment.bage_mod

Extract Data and Modelled Values
Sp

P-Spline Prior
RW

Random Walk Prior
SVD

SVD-Based Prior for Age or Age-Sex Profiles
SVD_AR

Dynamic SVD-Based Priors for Age Profiles or Age-Sex Profiles
RW_Seas

Random Walk Prior with Seasonal Effect
datamods

Data Models
forecast.bage_mod

Use a Model to Make a Forecast
fit.bage_mod

Fit a Model
is_fitted

Test Whether a Model has Been Fitted
bage-package

Package 'bage'
generate.bage_ssvd

Generate Random Age or Age-Sex Profiles
mod_norm

Specify a Normal Model
print.bage_mod

Printing a Model
isl_deaths

Deaths in Iceland
kor_births

Births in South Korea
components.bage_mod

Extract Values for Hyper-Parameters
generate.bage_prior_ar

Generate Values from Priors
mod_binom

Specify a Binomial Model
computations

Information on Computations Performed Duration Model Fitting
nzl_injuries

Fatal Injuries in New Zealand
nzl_divorces

Divorces in New Zealand
components.bage_ssvd

Extract Components used by SVD Summary
nzl_households

People in One-Person Households in New Zealand
nld_expenditure

Per Capita Health Expenditure in the Netherlands, 2003-2011
mod_pois

Specify a Poisson Model
set_var_age

Specify Age Variable
replicate_data

Create Replicate Data
report_sim

Simulation Study of a Model
set_var_sexgender

Specify Sex or Gender Variable
usa_deaths

Accidental Deaths in the USA
set_prior

Specify Prior for Model Term
set_n_draw

Specify Number of Draws from Prior or Posterior Distribution
reexports

Objects exported from other packages
unfit

Unfit a Model
priors

Priors for Intercept, Main Effects, Interactions
tidy.bage_mod

Summarize Terms from a Fitted Model
set_datamod_outcome_rr3

Specify RR3 Data Model
set_disp

Specify Prior for Dispersion or Standard Deviation
swe_infant

Infant Mortality in Sweden
set_var_time

Specify Time Variable
Lin_AR

Linear Prior with Autoregressive Errors
AR1

Autoregressive Prior of Order 1
HMD

Components from Human Mortality Database
HFD

Components from Human Fertility Database
N

Normal Prior
Lin_AR1

Linear Prior with Autoregressive Errors of Order 1
AR

Autoregressive Prior
Lin

Linear Prior with Independent Normal Errors
Known

Known Prior
LFP

Components from OECD Labor Force Participation Data