README.md
In growthrates: Estimate Growth Rates from Experimental Data

R package growthrates

The population growth rate is the main indicator of population fitness. This R package provides a collection of methods to determine growth rates from experimental data, in particular from batch experiments and microwell plate reader trials.

Release of version 0.8.4 to CRAN
improved robustness and error checking of the "easylinear"" method
Corrected parametrization of Gompertz models (0.8.2)
Simplified handling of log-transformed parametric models (v. 0.8.1)
Several small changes and improvements
Added predict-methods
Presentation at the useR!2017 conference in Brussels

The package contains basically three methods:

fit a linear regression to a subset of data with the steepest log-linear increase (a method, similar to Hall et al., 2014),
fit parametric nonlinear models to the complete data set, where the model functions can be given either in closed form or as numerically solved (system of) differential equation(s),
use maximum of the 1st derivative of a smoothing spline with log-transformed y-values (similar to Kahm et al., 2010).

The package can fit data sets of single experiments or complete series containing multiple data sets. Included are functions for extracting estimates and for plotting. The package supports growth models given as numerically solved differential equations. Multi-core computation is used to speed up fitting of parametric models.

Install package from within R or RStudio like any other package, or with:

install.packages("growthrates")

Install with package devtools:

install.packages("devtools")
library(devtools)
install_github("tpetzoldt/growthrates")

html: https://tpetzoldt.github.io/growthrates/doc/Introduction.html

html: https://tpetzoldt.github.io/growthrates/doc/User_models.html

Hall, B. G., H. Acar, A. Nandipati, and M. Barlow. 2014. Growth Rates Made Easy. Mol. Biol. Evol. 31: 232-38. https://dx.doi.org/10.1093/molbev/mst187

Kahm, Matthias, Guido Hasenbrink, Hella Lichtenberg-Frate, Jost Ludwig, and Maik Kschischo. 2010. grofit: Fitting Biological Growth Curves with R. Journal of Statistical Software 33 (7): 1-21. https://dx.doi.org/10.18637/jss.v033.i07

R Core Team. 2015. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/

Soetaert, Karline, and Thomas Petzoldt. 2010. Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME. Journal of Statistical Software 33 (3): 1-28. https://dx.doi.org/10.18637/jss.v033.i03

Soetaert, Karline, Thomas Petzoldt, and R. Woodrow Setzer. 2010. Solving Differential Equations in R: Package deSolve. Journal of Statistical Software 33 (9): 1-25. https://dx.doi.org/10.18637/jss.v033.i09

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