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predieval

Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit

v0.1.1 · Apr 19, 2022 · GPL (>= 2)

Description

Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

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Maintainer: ‘Orestis Efthimiou <oremiou@gmail.com>’

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                    role = c("aut", "cre"),
                    email = "oremiou@gmail.com")
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CRAN incoming feasibility

Maintainer: ‘Orestis Efthimiou <oremiou@gmail.com>’

No Authors@R field in DESCRIPTION.
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  Authors@R: person(given = "Orestis",
                    family = "Efthimiou",
                    role = c("aut", "cre"),
                    email = "oremiou@gmail.com")
as necessary.
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Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-devel-linux-x86_64-debian-clang

CRAN incoming feasibility

Maintainer: ‘Orestis Efthimiou <oremiou@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Orestis",
                    family = "Efthimiou",
                    role = c("aut", "cre"),
                    email = "oremiou@gmail.com")
as necessary.
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Orestis Efthimiou <oremiou@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Orestis",
                    family = "Efthimiou",
                    role = c("aut", "cre"),
                    email = "oremiou@gmail.com")
as necessary.

Dependency Network

Dependencies Reverse dependencies Hmisc ggplot2 MASS Matching predieval

Version History

new 0.1.1 Mar 9, 2026