concurve-package: A description of the 'concurve' 'R' package

In concurve: Computes & Plots Compatibility (Confidence), Surprisal, & Likelihood Distributions

Description

Allows one to compute compatibility (confidence) intervals for various statistical tests along with their corresponding P-values, S-values, and likelihoods. The intervals can be plotted to create consonance, surprisal, and likelihood functions allowing one to see what effect sizes are compatible with the test model at various compatibility levels rather than being limited to one interval estimate such as 95%.

Package:	concurve
Logo:
Type:	Package
Version:	2.7.7
Date:	2020-10-07
License:	GLP-3

Details

Accepts most modeling functions that produce confidence intervals to construct distributions.

See the following articles:

• Comparison to Bayesian Posterior Distributions

• The Bootstrap and Consonance Functions

• Background Literature

• Customizing Plots

• Examples in R

• Logistic Regression in R

• Profile Likelihoods

• Meta-Analysis Examples

• Survival Modeling

• S-values

• Generating Tables

• Troubleshooting

• Consonance Functions for Linear Mixed-Effects Models

• Wish List

Author(s)

Zad Rafi, Andrew D. Vigotsky

References

Rafi, Z., and Greenland, S. (2020), “Semantic and Cognitive Tools to Aid Statistical Science: Replace Confidence and Significance by Compatibility and Surprise" BMC Medical Research Methodology https://doi.org/10.1186/s12874-020-01105-9

Fraser DAS. The P-value function and statistical inference. The American Statistician. 2019;73(sup1):135-147. doi:10.1080/00031305.2018.1556735 https://doi.org/10.1080/00031305.2018.1556735

Fraser DAS. P-Values: The Insight to Modern Statistical Inference. Annual Review of Statistics and Its Application. 2017;4(1):1-14. https://doi.org/10.1146/annurev-statistics-060116-054139

Poole C. Beyond the confidence interval. American Journal of Public Health. 1987;77(2):195-199. doi:10.2105/AJPH.77.2.195 https://doi.org/10.1002/jrsm.1410

Poole C. Confidence intervals exclude nothing. American Journal of Public Health. 1987;77(4):492-493. doi:10.2105/ajph.77.4.492 https://doi.org/10.2105/ajph.77.4.492

Schweder T, Hjort NL. Confidence and Likelihood*. Scandinavian Journal of Statistics. 2002;29(2):309-332. doi:10.1111/1467-9469.00285 https://doi.org/10.1111/1467-9469.00285

Schweder T, Hjort NL. Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions. Cambridge University Press; 2016. https://books.google.com/books/about/Confidence_Likelihood_Probability.html?id=t7KzCwAAQBAJ

Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – distribution estimator of a parameter. arXiv. August 2007. https://arxiv.org/abs/0708.0976

Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. doi:10.1097/00001648-199001000-00009 https://doi.org/10.1097/00001648-199001000-00009

Whitehead J. The case for frequentism in clinical trials. Statistics in Medicine. 1993;12(15-16):1405-1413. doi:10.1002/sim.4780121506 https://doi.org/10.1002/sim.4780121506

Xie M-g, Singh K. Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review. International Statistical Review. 2013;81(1):3-39. doi:10.1111/insr.12000 https://doi.org/10.1111/insr.12000

Rothman KJ, Greenland S, Lash TL. Precision and statistics in epidemiologic studies. In: Rothman KJ, Greenland S, Lash TL, eds. Modern Epidemiology. 3rd ed. Lippincott Williams & Wilkins; 2008:148-167.

Rücker G, Schwarzer G. Beyond the forest plot: The drapery plot. Research Synthesis Methods. April 2020. doi:10.1002/jrsm.1410 https://doi.org/10.1002/jrsm.1410

Cox DR. Discussion. International Statistical Review. 2013;81(1):40-41. doi:10/gg9s2f https://onlinelibrary.wiley.com/doi/abs/10.1111/insr.12007

See Also

curve_gen, ggcurve, curve_table

concurve documentation built on Jan. 13, 2021, 11:20 p.m.