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renz  

R-Enzymology
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]
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) <doi:10.1002/bmb.21522>). 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
Previous versions and publish date:
0.1.1 (2021-12-02 14:50)
Other packages that cited renz R package
View renz citation profile
Other R packages that renz depends, imports, suggests or enhances
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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