Other packages > Find by keyword >

clinicalsignificance  

A Toolbox for Clinical Significance Analyses in Intervention Studies
View on CRAN: Click here


Download and install clinicalsignificance package within the R console
Install from CRAN:
install.packages("clinicalsignificance")

Install from Github:
library("remotes")
install_github("cran/clinicalsignificance")

Install by package version:
library("remotes")
install_version("clinicalsignificance", "3.0.0")



Attach the package and use:
library("clinicalsignificance")
Maintained by
Benedikt Claus
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-06-03
Latest Update: 2024-12-02
Description:
A clinical significance analysis can be used to determine if an intervention has a meaningful or practical effect for patients. You provide a tidy data set plus a few more metrics and this package will take care of it to make your results publication ready.
How to cite:
Benedikt Claus (2022). clinicalsignificance: A Toolbox for Clinical Significance Analyses in Intervention Studies. R package version 3.0.0, https://cran.r-project.org/web/packages/clinicalsignificance. Accessed 07 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:26), 1.0.0 (2022-06-03 09:30), 1.2.0 (2022-12-08 13:50), 2.0.0 (2023-11-16 16:44), 2.1.0 (2024-12-02 16:40)
Other packages that cited clinicalsignificance R package
View clinicalsignificance citation profile
Other R packages that clinicalsignificance depends, imports, suggests or enhances
Complete documentation for clinicalsignificance
Functions, R codes and Examples using the clinicalsignificance R package
Some associated functions: antidepressants . anxiety . anxiety_complete . augmented_data . calc_anchor.cs_anchor_group_between . calc_anchor.cs_anchor_group_within . calc_anchor.cs_anchor_individual_within . calc_anchor . calc_cutoff_from_data.cs_ha . calc_cutoff_from_data.default . calc_cutoff_from_data . calc_percentage . calc_rci.cs_en . calc_rci.cs_gln . calc_rci.cs_ha . calc_rci.cs_hll . calc_rci.cs_hlm . calc_rci.cs_jt . calc_rci.cs_nk . calc_rci . claus_2020 . create_summary_table.cs_anchor_individual_within . create_summary_table.cs_combined . create_summary_table.cs_distribution . create_summary_table.cs_percentage . create_summary_table.cs_statistical . create_summary_table . cs_anchor . cs_combined . cs_distribution . cs_get_cutoff . cs_get_cutoff_descriptives . cs_get_data . cs_get_model . cs_get_n . cs_get_reliability . cs_percentage . cs_statistical . generate_plotting_band.cs_anchor_individual_within . generate_plotting_band.cs_en . generate_plotting_band.cs_gln . generate_plotting_band.cs_ha . generate_plotting_band.cs_hll . generate_plotting_band.cs_jt . generate_plotting_band.cs_nk . generate_plotting_band.cs_percentage . generate_plotting_band . hechler_2014 . jacobson_1989 . plot.cs_anchor_group_between . plot.cs_anchor_group_within . plot.cs_anchor_individual_within . plot.cs_combined . plot.cs_distribution . plot.cs_percentage . plot.cs_statistical . print.cs_anchor_group_between . print.cs_anchor_group_within . print.cs_anchor_individual_within . print.cs_combined . print.cs_distribution . print.cs_percentage . print.cs_statistical . summary.cs_anchor_group_between . summary.cs_anchor_group_within . summary.cs_anchor_individual_within . summary.cs_combined . summary.cs_distribution . summary.cs_percentage . summary.cs_statistical . summary_table . 
Some associated R codes: calc_anchor.R . calc_cutoff.R . calc_percentage.R . calc_rci.R . calc_recovered.R . create_summary_table.R . cs_anchor.R . cs_combined.R . cs_distribution.R . cs_get_augmented_data.R . cs_get_cutoff.R . cs_get_cutoff_descriptives.R . cs_get_data.R . cs_get_model.R . cs_get_n.R . cs_get_reliability.R . cs_get_summary.R . cs_percentage.R . cs_statistical.R . datasets.R . generate_plotting_band.R . globals.R . package-utils.R . plot.R . prep_data.R .  Full clinicalsignificance package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

splm  
Econometric Models for Spatial Panel Data
ML and GM estimation and diagnostic testing of econometric models for spatial panel data. ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
PairedData  
Paired Data Analysis
Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis test ...
Download / Learn more Package Citations See dependency  
skewlmm  
Scale Mixture of Skew-Normal Linear Mixed Models
It fits scale mixture of skew-normal linear mixed models using an expectation–maximization (EM) ty ...
Download / Learn more Package Citations See dependency  
blandr  
Bland-Altman Method Comparison
Carries out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by JM B ...
Download / Learn more Package Citations See dependency  
ggTimeSeries  
Time Series Visualisations Using the Grammar of Graphics
Provides additional display mediums for time series visualisations. ...
Download / Learn more Package Citations See dependency  

28,905

R Packages

247,686

Dependencies

76,495

Author Associations

28,906

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA