Package: bws 0.1.0

Phuc H. Nguyen

bws: Bayesian Weighted Sums

An interface to the Bayesian Weighted Sums model implemented in 'RStan'. It estimates the summed effect of multiple, often moderately to highly correlated, continuous predictors. Its applications can be found in analysis of exposure mixtures. The model was proposed by Hamra, Maclehose, Croen, Kauffman, and Newschaffer (2021) <doi:10.3390/ijerph18041373>. This implementation includes an extension to model binary outcome.

Authors:Phuc H. Nguyen [aut, cre]

bws_0.1.0.tar.gz
bws_0.1.0.tar.gz(r-4.5-noble)bws_0.1.0.tar.gz(r-4.4-noble)
bws.pdf |bws.html✨
bws/json (API)
NEWS

# Install 'bws' in R:

install.packages('bws', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.70 score 247 downloads 1 exports 53 dependencies

Last updated 3 years agofrom:773d3062ba. Checks:1 OK, 2 NOTE. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 25 2025
R-4.5-linux-x86_64	NOTE	Mar 25 2025
R-4.4-linux-x86_64	NOTE	Mar 25 2025

Exports:bws

Dependencies:abind backports BH callr checkmate cli colorspace desc distributional fansi farver generics ggplot2 glue gridExtra gtable inline isoband labeling lattice lifecycle loo magrittr MASS Matrix matrixStats mgcv munsell nlme numDeriv pillar pkgbuild pkgconfig posterior processx ps QuickJSR R6 RColorBrewer Rcpp RcppEigen RcppParallel rlang rstan rstantools scales StanHeaders tensorA tibble utf8 vctrs viridisLite withr

Citation

To cite package ‘bws’ in publications use:

Nguyen P (2022). bws: Bayesian Weighted Sums. R package version 0.1.0, https://CRAN.R-project.org/package=bws.

Corresponding BibTeX entry:

  @Manual{,
    title = {bws: Bayesian Weighted Sums},
    author = {Phuc H. Nguyen},
    year = {2022},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=bws},
  }

Readme and manuals

bws

The goal of bws is to provide a user-friendly and efficient implementation of the Bayesian Weighted Sums (BWS) described by Hamra, Maclehose, Croen, Kauffman, and Newschaffer (2021) with some extensions to work with binary and count response data.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("phuchonguyen/bws")

Example

This is a basic example which shows you how to fit BWS:

## We first need to simulate some data
set.seed(123)
N <- 100
P <- 3
K <- 2
X <- matrix(rnorm(N*P), N, P)
Z <- matrix(rnorm(N*K), N, K)  # confounders
w <- c(0.3, 0.2, 0.5)
theta0 <- 2
theta1 <- 3
beta <- runif(K, 0.5, 1.5)
y <- theta0 + theta1*theta1*(X%*%w) + Z%*%beta + rnorm(N)
## Fitting BWS is simple
fit <- bws::bws(iter = 2000, y = y, X = X, Z = Z,
         # additional arguments for rstan::sampling
         chains = 4, cores = 2, show_messages = FALSE)
#> Warning: replacing previous import 'lifecycle::last_warnings' by
#> 'rlang::last_warnings' when loading 'tibble'
#> Warning: replacing previous import 'lifecycle::last_warnings' by
#> 'rlang::last_warnings' when loading 'pillar'

Since the implementation uses Stan and returns an rstanfit object, users can enjoy all the functionalities provided in rstan to analyze the fitted model:

rstan::traceplot(fit, pars = c("w", "theta1"), inc_warmup = TRUE, nrow = 2)

print(fit, pars = c("w", "theta1"))
#> Inference for Stan model: bws.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
#>        mean se_mean   sd 2.5%  25%  50%  75% 97.5% n_eff Rhat
#> w[1]   0.32       0 0.01 0.30 0.31 0.32 0.33  0.34  5292    1
#> w[2]   0.19       0 0.01 0.17 0.18 0.19 0.19  0.21  5750    1
#> w[3]   0.49       0 0.01 0.47 0.49 0.49 0.50  0.51  6125    1
#> theta1 8.71       0 0.19 8.34 8.59 8.71 8.84  9.08  5388    1
#> 
#> Samples were drawn using NUTS(diag_e) at Mon Jun 13 00:06:38 2022.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at 
#> convergence, Rhat=1).
rstan::plot(fit)
#> ci_level: 0.8 (80% intervals)
#> outer_level: 0.95 (95% intervals)

The model inferred the correct weights, which are set to 0.3, 0.2, 0.5 in the simulation.

Reference:

Hamra, G.B.; Maclehose, R.F.; Croen, L.; Kauffman, E.M.; Newschaffer, C. Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects. International Journal of Environmental Research and Public Health 2021, 18, 1373. link

Help Manual

Help page	Topics
The 'bws' package.	bws-package bwspackage
Bayesian Weighted Sums	bws