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Title:

Bayesian Estimation of Naloxone Kit Number Under-Reporting

Version:

0.3.2

Description:

Bayesian model and associated tools for generating estimates of total naloxone kit numbers distributed and used from naloxone kit orders data. Provides functions for generating simulated data of naloxone kit use and functions for generating samples from the posterior.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.3

Biarch:

true

Depends:

R (≥ 3.4.0)

Imports:

dplyr, generics, ggplot2, glue, lifecycle, magrittr, methods,Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rlang, rstan (≥2.26.0), rstantools (≥ 2.5.0), scales, tidybayes, tidyr

LinkingTo:

BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0),RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥2.26.0)

SystemRequirements:

GNU make

Suggests:

bayesplot, covr, knitr, latex2exp, posterior, progress,rmarkdown, stringr, testthat (≥ 3.0.0)

Config/testthat/edition:

URL:

https://sempwn.github.io/bennu/

BugReports:

https://github.com/sempwn/bennu/issues

VignetteBuilder:

knitr

NeedsCompilation:

yes

Packaged:

2025-10-09 17:28:23 UTC; rstudio

Author:

Mike Irvine

[aut, cre, cph], Samantha Bardwell [ctb], Andrew Johnson [ctb]

Maintainer:

Mike Irvine <mike.irvine@bccdc.ca>

Repository:

CRAN

Date/Publication:

2025-10-09 17:50:02 UTC

The 'bennu' package.

Description

Bayesian Estimation of Naloxone use Number Under-reporting

Author(s)

Maintainer: Mike Irvinemike.irvine@bccdc.ca (ORCID) [copyright holder]

Other contributors:

Samantha Bardwell [contributor]
Andrew Johnson [contributor]

References

Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org

Pipe operator

Description

Seemagrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of callingrhs(lhs).

Run Bayesian estimation of naloxone number under-reporting

Description

Samples from Bayesian model using input from data frame

Usage

est_naloxone(  d,  psi_vec = c(0.7, 0.2, 0.1),  max_delays = 3,  delay_alpha = 2,  delay_beta = 1,  priors = the$default_priors,  run_estimation = TRUE,  rw_type = 1,  chains = 4,  iter = 2000,  seed = 42,  adapt_delta = 0.85,  pars = the$default_outputs,  include = TRUE,  ...)

Arguments

d

data frame with format

regions: unique id for region
times: time in months
Orders: Kits ordered
Reported_Used: Kits reported as used
Reported_Distributed: Kits reported as distributed
region_name: Optional label for region

psi_vec

reporting delay distribution

max_delays

maximum delay from kit ordered to kit distributed

delay_alpha

shape parameter for order to distributed delaydistribution

delay_beta

shape parameter for order to distributed delay distribution

priors

list of prior values including their mean (mu) and standarddeviation (sigma)

run_estimation

ifTRUE will sample from posterior otherwise willsample from prior only

rw_type

1 - random walk of order one.2 - random walk of order 2.

chains

A positive integer specifying the number of Markov chains.The default is 4.

iter

A positive integer specifying the number of iterationsfor each chain (including warmup). The default is 2000.

seed

Seed for random number generation

adapt_delta

(double, between 0 and 1, defaults to 0.8)

pars

A vector of character strings specifying parameters of interest. The default isNA indicating all parameters in the model. Ifinclude = TRUE, only samples for parameters named inpars are stored in the fitted results. Conversely, ifinclude = FALSE, samples for all parametersexcept those named inpars are stored in the fitted results.

include

Logical scalar defaulting toTRUE indicatingwhether to include or exclude the parameters given by thepars argument. IfFALSE, only entire multidimensionalparameters can be excluded, rather than particular elements of them.

...

other parameters to pass torstan::sampling

Value

An S4rstan::stanfit class object containing the fitted model

Examples

## Not run: library(rstan)library(bayesplot)rstan_options(auto_write = TRUE)options(mc.cores = parallel::detectCores(logical = FALSE))d <- generate_model_data()priors <- list(  c = list(mu = 0, sigma = 1),  ct0 = list(mu = 0, sigma = 1),  zeta = list(mu = 0, sigma = 1),  mu0 = list(mu = 0, sigma = 1),  sigma = list(mu = 0, sigma = 1)  )fit <- est_naloxone(d, priors = priors, iter = 100, chains = 1)mcmc_pairs(fit,  pars = c("sigma", "mu0"),  off_diag_args = list(size = 1, alpha = 0.5))## End(Not run)

Run Bayesian estimation of naloxone number under-reporting

Description

Samples from Bayesian model

Usage

est_naloxone_vec(  N_region,  N_t,  N_distributed,  regions,  times,  Orders2D,  Reported_Distributed,  Reported_Used,  region_name,  psi_vec = c(0.7, 0.2, 0.1),  max_delays = 3,  delay_alpha = 2,  delay_beta = 1,  priors = the$default_priors,  run_estimation = TRUE,  rw_type = 1,  chains = 4,  iter = 2000,  seed = 42,  adapt_delta = 0.85,  pars = the$default_outputs,  include = TRUE,  ...)

Arguments

N_region

Number of regions

N_t

number of time steps

N_distributed

Number of samples of reporting for distribution of kits

regions

vector (time, region) of regions (coded 1 to N_region)

times

vector (time, region) of regions (coded 1 to N_t)

Orders2D

vector (time, region) of orders

Reported_Distributed

vector (time, region) reported as distributed

Reported_Used

vector (time, region) reported as used

region_name

bring in region names

psi_vec

reporting delay distribution

max_delays

maximum delay from kit ordered to kit distributed

delay_alpha

shape parameter for order to distributed delaydistribution

delay_beta

shape parameter for order to distributed delay distribution

priors

list of prior values including their mean (mu) and standarddeviation (sigma)

run_estimation

ifTRUE will sample from posterior otherwise willsample from prior only

rw_type

1 - random walk of order one.2 - random walk of order 2.

chains

A positive integer specifying the number of Markov chains.The default is 4.

iter

A positive integer specifying the number of iterationsfor each chain (including warmup). The default is 2000.

seed

Seed for random number generation

adapt_delta

(double, between 0 and 1, defaults to 0.8)

pars

include

...

other parameters to pass torstan::sampling

Value

An S4rstan::stanfit class object containing the fitted model

Experimental validation results

Description

Generated data from validation experiments of simulated data

Usage

experimental_validation_data

Format

`experimental_validation_data`

A data frame with 200 rows and 8 columns:

.variable: Model variable
p50: Median of the posterior
p25, p75: 2nd and 3rd quartiles of the posterior
p05, p95: 1st and 19th ventiles of the posterior
true_value: The value used to generate the simulation
experiment: Experiment number index

generate model data for testing purposes

Description

Simulate kits ordered and kits distributed for a set number of regions andtime-points.

The kits ordered simulation is a simple square-term multiplied byregion_coeffs.For example ifregion_coeffs = c(1,2) then the number of kits ordered atmonth 12 arec(1,2) * 12^2 = c(144,288).

The probability of kit use in time is assumed to increase linearly in inverselogit space at a constant rate0.1.The probability of reporting for each month and region is iid distributedlogit^{-1}(p) \sim N(2,5) which produces a mean reporting rateof approximately 88%

Usage

generate_model_data(  N_t = 24,  region_coeffs = c(5, 0.5),  c_region = c(-1, 2),  reporting_freq = NULL)

Arguments

N_t

number of time-points

region_coeffs

vector of coefficients for regions determining kit orders

c_region

logit probability of kit use per region

reporting_freq

The frequency that distribution data is provided.IfNULL distribution frequency matches orders frequency

Value

Atibble::tibble()

Orders: Kit orders per time and region
regions: Numeric index indicating region of orders and distributions
Reported_Used: Number of kits reported as used
Reported_Distributed: Number of kits reported as distributed
p_use: Probability that a kit was used
p_reported: Probability that a distributed kit was reported
times: Index for time
region_name: String index for the region

Summarize model fit

Description

Provides a summary of:

Estimated kits distributed
Percentage of kits distributed that are reported
Estimated kits used
percentage of kits used that are reported
percentage of kits orders that are used
probability kit used if distributed

Usage

kit_summary_table(  fit,  ...,  data = NULL,  accuracy = 0.01,  cri_range = 0.95,  ndraws = NULL)

Arguments

fit

rstan::stanfit object

...

variables to group by in estimate

data

data used for model fitting. Can also includep_use columnwhich can be used to plot true values if derived from simulated data.

accuracy

A number to round to. Use (e.g.) 0.01 to show 2 decimalplaces of precision. If NULL, the default, uses a heuristic that shouldensure breaks have the minimum number of digits needed to show thedifference between adjacent values.

cri_range

The range of the credible interval e.g. 0.95

ndraws

Number of draws to use in estimate

Value

Atibble::tibble

Probability of kit use if distributed
Estimated as distributed
Proportion kits distributed that are reported
Estimated kits used
Proportion kits used that are reported
Proportion kits ordered that are used

Examples

## Not run:   fit <- est_naloxone(d)  kit_summary_table(fit,regions,data = d)## End(Not run)

Missing data experimental validation results

Description

Generated data from validation experiments of simulated data

Usage

missing_data_validation

Format

`missing_data_validation`

A data frame with 10 rows and 6 columns:

p50: Median of the posterior
p25, p75: 2nd and 3rd quartiles of the posterior
p05, p95: 1st and 19th ventiles of the posterior
reporting_freq: The reporting frequency in months

generate model data for testing purposes

Description

Model generating process using random walk to match data generating modelin Bayesian framework

Usage

model_random_walk_data(  N_t = 24,  region_coeffs = c(5, 0.5),  c_region = c(-1, 2),  sigma = 2,  zeta = 0.5,  mu0 = -1,  Orders = NULL,  reporting_freq = NULL)

Arguments

N_t

number of time-points

region_coeffs

vector of coefficients for regions determining kit orders

c_region

logit probability of kit use per region

sigma

standard deviation of error in logit probability of kit use

zeta

standard deviation of random walk in logit space

mu0

initial condition of random walk in logit space

Orders

A 2D matrix of shapelength(region_coeffs) byN_t

reporting_freq

The frequency that distribution data is provided.IfNULL distribution frequency matches orders frequency

Value

A tibble

Orders: Kit orders per time and region
regions: Numeric index indicating region of orders and distributions
Reported_Used: Number of kits reported as used
Reported_Distributed: Number of kits reported as distributed
p_use: Probability that a kit was used
p_reported: Probability that a distributed kit was reported
times: Index for time
region_name: String index for the region

Plot of probability of naloxone kit use

Description

plot can compare between two different model fits or a singlemodel fit by region. If data are simulated then can also include in plot.For more details see the introduction vignette:vignette("Introduction", package = "bennu")

Usage

plot_kit_use(..., data = NULL, reported = FALSE, regions_to_plot = NULL)

Arguments

...

named list ofrstan::stanfit objects

data

data used for model fitting. Can also includep_use columnwhich can be used to plot true values if derived from simulated data.

reported

ifTRUE then produces a plot of the reported kitswhich is equivalent to the predictive check.

regions_to_plot

Optional list to filter which regions areplotted

Value

ggplot2::ggplot object

Movatterモバイル変換

The 'bennu' package.

Description

Author(s)

References

See Also

Pipe operator

Description

Usage

Arguments

Value

Run Bayesian estimation of naloxone number under-reporting

Description

Usage

Arguments

Value

See Also

Examples

Run Bayesian estimation of naloxone number under-reporting

Description

Usage

Arguments

Value

See Also

Experimental validation results

Description

Usage

Format

experimental_validation_data

See Also

generate model data for testing purposes

Description

Usage

Arguments

Value

See Also

Summarize model fit

Description

Usage

Arguments

Value

See Also

Examples

Missing data experimental validation results

Description

Usage

Format

missing_data_validation

See Also

generate model data for testing purposes

Description

Usage

Arguments

Value

See Also

Plot of probability of naloxone kit use

Description

Usage

Arguments

Value

See Also

`experimental_validation_data`

`missing_data_validation`