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insightrx

clinPK:Clinical Pharmacokinetics Toolkit

Provides equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.

Maintained by Ron Keizer. Last updated 3 months ago.

clinical-researchpharmacokinetics

31 stars 6.66 score 55 scripts

rcurtin

mlpack:'Rcpp' Integration for the 'mlpack' Library

A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) <doi:10.21105/joss.05026>.

Maintained by Ryan Curtin. Last updated 4 months ago.

openblascppopenmp

3.71 score 20 scripts 8 dependents

jfwambaugh

invivoPKfit:Fits Toxicokinetic Models to In Vivo PK Data Sets

Takes in vivo toxicokinetic concentration-time data and fits parameters of 1-compartment and 2-compartment models for each chemical. These methods are described in detail in "Informatics for Toxicokinetics" (submitted).

Maintained by John Wambaugh. Last updated 10 days ago.

3.48 score 4 scripts

tjaki

PK:Basic Non-Compartmental Pharmacokinetics

Estimation of pharmacokinetic parameters using non-compartmental theory.

Maintained by Thomas Jaki. Last updated 2 years ago.

2.59 score 13 scripts 1 dependents

govertbuijs

NCA:Necessary Condition Analysis

Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52) <doi:10.1177/1094428115584005>. NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations. (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output). A quick start guide for using this package can be found here: <https://repub.eur.nl/pub/78323/> or <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2624981>.

Maintained by Govert Buijs. Last updated 5 months ago.

2.36 score 17 scripts

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