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renz
View on CRAN: Click
here
Download and install renz package within the R console
Install from CRAN:
install.packages("renz")
Install from Github:
library("remotes")
install_github("cran/renz")
Install by package version:
library("remotes")
install_version("renz", "0.2.1")
Attach the package and use:
library("renz")
Maintained by
Juan Carlos Aledo
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-12-02
Latest Update: 2021-12-02
Description:
Contains utilities for the analysis of Michaelian kinetic data. Beside the classical linearization
methods (Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf and Eisenthal-Cornish-Bowden), features include the
ability to carry out weighted regression analysis that, in most cases, substantially improves the estimation
of kinetic parameters (Aledo (2021) ). To avoid data transformation and the potential
biases introduced by them, the package also offers functions to directly fitting data to the Michaelis-Menten
equation, either using ([S], v) or (time, [S]) data. Utilities to simulate substrate progress-curves (making
use of the Lambert W function) are also provided. The package is accompanied of vignettes that aim to orientate
the user in the choice of the most suitable method to estimate the kinetic parameter of an Michaelian enzyme.
How to cite:
Juan Carlos Aledo (2021). renz: R-Enzymology. R package version 0.2.1, https://cran.r-project.org/web/packages/renz. Accessed 21 Dec. 2024.
Previous versions and publish date:
0.1.1 (2021-12-02 14:50)
Other packages that cited renz R package
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Other R packages that renz depends,
imports, suggests or enhances
Complete documentation for renz
Functions, R codes and Examples using
the renz R package
Some associated functions: ONPG . TcTS . bibi . dir.MM . ecb . eh . fE.progress . hk . hw . int.MM . lb . sE.progress .
Some associated R codes: BiBi.R . LintMM.R . Ltransf.R . ONPG.R . Progress.R . TcTS.R . dirMM.R . hk.R . Full renz package functions and examples
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