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R software environment in version 4

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R is an open-source software environment for statistical computing and graphics. Version 4.0.2 provides a stable and reliable platform for data analysis, modeling, and visualization. The core function of R is to provide a comprehensive set of tools and libraries for statistical and numerical computing, enabling users to perform a wide range of data-related tasks.

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Lab products found in correlation

Microsoft excel 2016, by IBM (1 mentions) Spss statistics 25, by IBM (1 mentions)

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R software (3634 citations) R version 4.0.2 (2821 citations) R software version 4.0.3 (870 citations) R software 4.0.3 (111 citations) R software environment (38 citations) R environment (33 citations) R software (version (27 citations) R environment, version 4.0.3 (2 citations)

2 protocols using r software environment in version 4

1

Comparative Analysis of Intervention Outcomes

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All results as well as the socio-demographic data were reported descriptively (mean and standard deviation, or median and minimum/maximum). The primary outcome was compared between GI_with and GI_without using Poisson regression; incidence rate ratios (IRR) were derived as exponentiated parameter estimates from the regression model. All other outcomes and socio-demographic data were analyzed using nonparametric tests (i.e. χ2-test, Kruskal–Wallis test, Mann–Whitney U test). P-values less than 0.05 were considered as formally significant, yet all p-values should be regarded as exploratory. All analyses were carried out using Microsoft Excel 2016, IBM SPSS Statistics 25 and the R software environment in version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria).
Wurmbach V.S., Schmidt S.J., Lampert A., Bernard S., Meid A.D., Frick E., Metzner M., Wilm S., Mortsiefer A., Bücker B., Altiner A., Sparenberg L., Szecsenyi J., Peters-Klimm F., Kaufmann-Kolle P., Thürmann P.A., Seidling H.M, & Haefeli W.E. (2022). HIOPP-6 – a pilot study on the evaluation of an electronic tool to assess and reduce the complexity of drug treatment considering patients’ views. BMC Primary Care, 23, 164.
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2

Personalized Machine Learning for Biomedical Research

Cited 1 time
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Statistical analyses were conducted with the R software environment in
version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria)
using the key packages personalized,28
ranger,37
tuneranger,31
and SimBaCo.36 (link)
Meid A.D., Wirbka L., Groll A, & Haefeli W.E. (2021). Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants. Medical Decision Making, 42(5), 587-598.
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