The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
InsuSensCalc 0.0.1 (2024-04-02)
- Initial release of the
InsuSensCalc
, featuring the isi_calculator
function.
New Features
isi_calculator
Function: A comprehensive tool for calculating surrogate insulin sensitivity indices based on various measurements, including fasting, Oral Glucose Tolerance Test (OGTT), and lipid (adipose) values. This function supports a wide range of indices calculations, making it a versatile tool for research in metabolic health and diabetes.
Capabilities
- Calculates indices using fasting glucose and insulin levels, including:
- Fasting Insulin Sensitivity
- HOMA-IR (and its inverse)
- QUICKI
- And several other fasting-related indices.
- Incorporates OGTT (0 min, 30 min, 120 min post-glucose load) values for:
- Gutt Index
- Matsuda Index
- Insulin Sensitivity Index at 120 min
- And more, adapting calculations based on available time points.
- Utilizes lipid (adipo) measurements like triglycerides, free fatty acids and HDL cholesterol for indices such as:
- Visceral Adiposity Index (VAI) for Men and Women (inversed)
- Lipid Accumulation Product (LAP)
- ATIRI_inv
- Utilizes tracer and dxa based data measurements like rate of glycerol, palmitate and dxa based fat mass for indices such as:
- LIRI_inv
- Lipo_inv
- TyG Index (inversed)
- And other adipose-related indices.
- The function accepts a dataframe containing the necessary variables for calculation and a character vector specifying the categories of indices to calculate (
"fasting"
, "ogtt"
, "adipo"
), allowing users to customize the scope of their analysis.
User-friendly
- Includes comprehensive documentation and examples to facilitate easy use and integration into research workflows.
This release lays the foundation for robust and flexible insulin sensitivity analysis within the R ecosystem, catering to a wide array of research needs in metabolic health.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.