A simple introduction to using the concurve R package.
A simple introduction to using the concurve R package for logistic regression.
A vignette on how to produce consonance functions for results obtained from a survival analysis.
This guide walks through how to construct consonance functions from meta-analysis data using the metafor R package. Both simple and complex data structures are examined.
This vignette walks through how to construct and graph profile likelihood functions.
Building exact likelihood functions from first principles for common designs – proportions, odds ratios, rate ratios, means, and variances – and coercing them into concurve objects.
Any tool that returns a (profile) log-likelihood – optim, maxLik, bbmle, ProfileLikelihood, cond – can feed a concurve likelihood object through one small adapter.
Constructing a profile likelihood function for a correlation coefficient and pairing it with the consonance (P-value) function from curve_corr().
Constructing a profile likelihood function for a Poisson rate ratio with curve_lik(), then pairing it with the consonance (P-value) function from curve_gen().
This guide discusses the bootstrap resampling method and its close connection to the consonance distribution and how to use certain iterations of the bootstrap to construct a consonance distribution.
This vignette discusses how consonance distributions and Bayesian posterior distributions differ in interpretation but how they can also often converge. This is shown with several examples of how to calculate both distributions in a range of scenarios.
Consonance functions for linear mixed-effects models with curve_lmer().
Constructing consonance functions from Stata output, using Statamarkdown to run Stata code inline.
A guide on how to customize the plots obtained from concurve via ggplot2 arguments and with other packages.
This vignette walks through how to construct tables from the curve functions.
A guide to dealing with common errors when using the concurve R package including slow loading of the graphs, warnings, and distorted plots.
Background information on S-values along with a Shiny app that converts observed P-values to S-values.
A list of papers discussing confidence distributions and their theory.
A list of features that could potentially be added to the package.