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dcurves
View on CRAN: Click
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Download and install dcurves package within the R console
Install from CRAN:
install.packages("dcurves")
Install from Github:
library("remotes")
install_github("cran/dcurves") Install by package version:
library("remotes")
install_version("dcurves", "0.5.1") Attach the package and use:
library("dcurves")
Maintained by
Daniel D. Sjoberg
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-07-20
Latest Update: 2024-07-23
Description:
Diagnostic and prognostic models are typically evaluated with
measures of accuracy that do not address clinical consequences.
Decision-analytic techniques allow assessment of clinical outcomes,
but often require collection of additional information may be
cumbersome to apply to models that yield a continuous result. Decision
curve analysis is a method for evaluating and comparing prediction
models that incorporates clinical consequences, requires only the data
set on which the models are tested, and can be applied to models that
have either continuous or dichotomous results. See the following references
for details on the methods: Vickers (2006) ,
Vickers (2008) ,
and Pfeiffer (2020) .
How to cite:
Daniel D. Sjoberg (2021). dcurves: Decision Curve Analysis for Model Evaluation. R package version 0.5.1, https://cran.r-project.org/web/packages/dcurves. Accessed 15 Jul. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for dcurves
Functions, R codes and Examples using
the dcurves R package
Some associated functions: as_tibble.dca . dca . dcurves-package . df_binary . df_case_control . df_surv . net_intervention_avoided . plot.dca . print.dca . reexports . standardized_net_benefit . test_consequences .
Some associated R codes: as_tibble.R . dcurves-package.R . net_intervention_avoided.R . plot.R . print.R . reexport.R . standardize_net_benefit.R . test_consequences.R . Full dcurves package functions and examples
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