dreamer

An R package to fit Bayesian model averaging of (possibly longitudinal) dose-response models.

https://github.com/rich-payne/dreamer

Science Score: 13.0%

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Low similarity (18.4%) to scientific vocabulary

Keywords

bayesian dose-response-modeling r-package

Last synced: 6 months ago · JSON representation

Repository

An R package to fit Bayesian model averaging of (possibly longitudinal) dose-response models.

Basic Info

Host: GitHub
Owner: rich-payne
License: other
Language: R
Default Branch: main
Homepage: https://rich-payne.github.io/dreamer
Size: 15.9 MB

Statistics

Stars: 9
Watchers: 3
Forks: 2
Open Issues: 3
Releases: 2

Topics

bayesian dose-response-modeling r-package

Created over 4 years ago · Last pushed about 1 year ago

Metadata Files

Readme Changelog License

README.Rmd

---
output: github_document
editor_options: 
  chunk_output_type: console
---


[![R-CMD-check](https://github.com/rich-payne/dreamer/actions/workflows/check.yaml/badge.svg)](https://github.com/rich-payne/dreamer/actions/workflows/check.yaml)
[![cover](https://github.com/rich-payne/dreamer/actions/workflows/cover.yaml/badge.svg)](https://github.com/rich-payne/dreamer/actions/workflows/cover.yaml)
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[![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/grand-total/dreamer?color=brightgreen)](https://r-pkg.org/pkg/dreamer)
[![CRAN Release](https://www.r-pkg.org/badges/version/dreamer)](https://r-pkg.org/pkg/dreamer)


```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# dreamer 

The goal of dreamer (Dose REsponse bAyesian Model avERaging) is to flexibly model (longitudinal) dose-response relationships.  This is accomplished using Bayesian model averaging of parametric dose-response models (see Gould (2019), Ando & Tsay (2010)).

dreamer supports a number of dose-response models including linear, quadratic, 
log-linear, log-quadratic, EMAX, exponential, for use as models that
can be included in the model averaging approach.  In addition, several
longitudinal models are also supported (see the vignette).  All of the above
models are available for both continuous and binary endpoints.

# Installation

dreamer is available on CRAN and can be installed with `install.packages("dreamer")`.  Note that dreamer depends on [rjags](https://cran.r-project.org/package=rjags) which
itself depends on an installation of JAGS.

The development version of dreamer can be installed directly from github: 
`devtools::install_github("rich-payne/dreamer")`.

For feature requests and to report bugs, please submit an issue to the [dreamer github](https://github.com/rich-payne/dreamer/issues).

# Vignettes

See the "dreamer_method" vignette for a high-level overview of Bayesian model averaging and/or read Gould (2019) for the approach used by dreamer.

For a larger set of examples, see the "dreamer" vignette.

# Example

With dreamer, it is easy to generate data, fit models, and visualize model
fits.

```{r example}
library(dreamer)
# generate data from a quadratic dose response
set.seed(888)
data <- dreamer_data_quad(
  n_cohorts = c(10, 10, 10, 10), # number of subjects in each cohort
  dose = c(.25, .5, .75, 1.5), # dose administered to each cohort
  b1 = 0,
  b2 = 2,
  b3 = -1,
  sigma = .5 # standard deviation
)

# Bayesian model averaging
output <- dreamer_mcmc(
  data = data,
  # mcmc information
  n_adapt = 1e3,
  n_burn = 1e3,
  n_iter = 1e4,
  n_chains = 2,
  silent = TRUE, # make rjags be quiet
  # model definitions
  mod_linear = model_linear(
    mu_b1 = 0,
    sigma_b1 = 1,
    mu_b2 = 0,
    sigma_b2 = 1,
    shape = 1,
    rate = .001,
    w_prior = 1 / 3 # prior probability of the model
  ),
  mod_quad = model_quad(
    mu_b1 = 0,
    sigma_b1 = 1,
    mu_b2 = 0,
    sigma_b2 = 1,
    mu_b3 = 0,
    sigma_b3 = 1,
    shape = 1,
    rate = .001,
    w_prior = 1 / 3
  ),
  mod_emax = model_emax(
    mu_b1 = 0,
    sigma_b1 = 1,
    mu_b2 = 0,
    sigma_b2 = 1,
    mu_b3 = 0,
    sigma_b3 = 1,
    mu_b4 = 0,
    sigma_b4 = 1,
    shape = 1,
    rate = .001,
    w_prior = 1 / 3
  )
)

output

# plot Bayesian model averaging fit
plot(output, data = data)

# plot individual model fit
plot(output$mod_emax, data = data)

# posterior summary for model parameters
summary(output)

# posterior summary on dose-response curve
posterior(output)
```

## Reference

Ando, T., & Tsay, R. (2010). Predictive likelihood for Bayesian model selection and averaging. International Journal of Forecasting, 26(4), 744-763.

Gould, A. Lawrence. "BMA‐Mod: A Bayesian model averaging strategy for determining dose‐response relationships in the presence of model uncertainty." *Biometrical Journal* 61.5 (2019): 1141-1159.

Owner

Name: Richard Payne
Login: rich-payne
Kind: user
Company: @EliLillyCo

Repositories: 2
Profile: https://github.com/rich-payne

Statistician specializing in Bayesian methods and R-package development.

GitHub Events

Total

Create event: 6
Release event: 1
Issues event: 9
Watch event: 1
Delete event: 3
Issue comment event: 2
Push event: 18
Pull request event: 9
Fork event: 1

Last Year

Create event: 6
Release event: 1
Issues event: 9
Watch event: 1
Delete event: 3
Issue comment event: 2
Push event: 18
Pull request event: 9
Fork event: 1

Committers

Last synced: over 2 years ago

All Time

Total Commits: 113
Total Committers: 2
Avg Commits per committer: 56.5
Development Distribution Score (DDS): 0.062

Past Year

Commits: 14
Committers: 2
Avg Commits per committer: 7.0
Development Distribution Score (DDS): 0.071

Top Committers

Name	Email	Commits
Richard Payne	r**e@l**m	106
Richard Payne	3****e	7

Committer Domains (Top 20 + Academic)

lilly.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 29
Total pull requests: 28
Average time to close issues: about 1 month
Average time to close pull requests: 18 days
Total issue authors: 3
Total pull request authors: 3
Average comments per issue: 0.41
Average comments per pull request: 0.21
Merged pull requests: 26
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 6
Pull requests: 5
Average time to close issues: about 1 month
Average time to close pull requests: about 4 hours
Issue authors: 1
Pull request authors: 1
Average comments per issue: 0.33
Average comments per pull request: 0.0
Merged pull requests: 5
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

rich-payne (26)
wlandau (2)
mitchell-thomann (1)

Pull Request Authors

rich-payne (29)
mitchell-thomann (5)
olivroy (2)

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Packages

Total packages: 1
Total downloads:
- cran 302 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 3
Total maintainers: 1

cran.r-project.org: dreamer

Dose Response Models for Bayesian Model Averaging

Homepage: https://rich-payne.github.io/dreamer/
Documentation: http://cran.r-project.org/web/packages/dreamer/dreamer.pdf
License: MIT + file LICENSE
Latest release: 3.2.0
published about 1 year ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 302 Last month

Rankings

Stargazers count: 26.2%

Forks count: 28.8%

Dependent packages count: 29.8%

Dependent repos count: 35.5%

Average: 35.5%

Downloads: 57.3%

Maintainers (1)

paynestatistics@gmail.com

Last synced: 6 months ago

Dependencies

DESCRIPTION cran

coda * imports
dplyr >= 1.0.0 imports
ellipsis >= 0.3 imports
ggplot2 * imports
graphics * imports
purrr * imports
rjags >= 4 imports
rlang >= 0.4.5 imports
rootSolve * imports
stats * imports
tidyr >= 1.0.2 imports
tidyselect >= 1.1 imports
fs >= 1.5 suggests
knitr * suggests
rmarkdown * suggests
spelling * suggests
testthat >= 3.0 suggests
tibble * suggests

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actions/cache v2 composite
actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
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actions/cache v2 composite
actions/checkout v2 composite
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actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

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dreamer

Science Score: 13.0%

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