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dreamer (version 3.2.0)

Dose Response Models for Bayesian Model Averaging

Description

Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) . Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019).

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Install

install.packages('dreamer')

Monthly Downloads

299

Version

3.2.0

License

MIT + file LICENSE

Issues

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Maintainer

Richard Payne

Last Published

December 19th, 2024

Functions in dreamer (3.2.0)

dreamer_mcmc

Bayesian Model Averaging of Dose Response Models
summary.dreamer_mcmc

Summarize Model Output
dreamer_plot_prior

Plot Prior
post_perc_effect

Calculate Posterior of a Dose's Percentage Effect
posterior

Posterior Quantities from Bayesian Model Averaging
post_medx

Posterior Distribution of Minimum X% Effective Dose
pr_medx

Probability of minimum X% effective dose
summary.dreamer_bma

Summarize Bayesian Model Averaging MCMC Output
plot_trace

Traceplots
model

Model Creation
dreamerplot

Posterior Plot of Bayesian Model Averaging
pr_eoi

Calculate Probability of Meeting Effect of Interest (EOI)
model_longitudinal

Model Creation: Longitudinal Models
pr_med

Pr(minimum efficacious dose)
plot_comparison

Compare Posterior Fits
diagnostics

Calculate MCMC Diagnostics for Parameters
dreamer_data

Generate Data from Dose Response Models