Package: miLAG 1.0.4

miLAG: Calculates Microbial Lag Duration (on the Population Level) from Provided Growth Curve Data

Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions. It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022. Citation: "How to determine microbial lag phase duration?", M. Opalek, B. Smug, D. Wloch-Salamon (2022) <doi:10.1101/2022.11.16.516631>.

Authors:Bogna Smug [aut, cre]

miLAG_1.0.4.tar.gz
miLAG_1.0.4.zip(r-4.5)miLAG_1.0.4.zip(r-4.4)miLAG_1.0.4.zip(r-4.3)
miLAG_1.0.4.tgz(r-4.4-any)miLAG_1.0.4.tgz(r-4.3-any)
miLAG_1.0.4.tar.gz(r-4.5-noble)miLAG_1.0.4.tar.gz(r-4.4-noble)
miLAG_1.0.4.tgz(r-4.4-emscripten)miLAG_1.0.4.tgz(r-4.3-emscripten)
miLAG.pdf |miLAG.html
miLAG/json (API)

# Install 'miLAG' in R:
install.packages('miLAG', repos = c('https://bognasmug.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.60 score 5 scripts 217 downloads 13 exports 51 dependencies

Last updated 1 months agofrom:cac0556f4a. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:calc_lagcut_the_datafit_exp_lagfit_max_infl_lagget_all_methods_lagget_def_parsget_lagget_themelag_biomass_incrmake_grwoth_curve_dfplot_dataplot_lag_fitsmooth_data

Dependencies:briocallrclicolorspacecrayondescdiffobjdigestdplyrevaluatefansifarverfsgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvminpack.lmmunsellnlmenlsMicrobionlstoolspillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RColorBrewerrlangrprojrootscalestestthattibbletidyselectutf8vctrsviridisLitewaldowithr

Calculating Lags for Growth Models

Rendered fromcalculating_lag.rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2023-11-18
Started: 2023-11-18

Choosing Lag Fit Algorithms in Growth Modeling

Rendered fromchoosing_lag.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2023-11-18
Started: 2023-11-18

Getting Initial Parameters for Growth Models

Rendered fromgetting_lag.rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2023-11-18
Started: 2023-11-18

Plotting Lag Fit

Rendered fromplotting_lag.rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2023-11-18
Started: 2023-11-18

Readme and manuals

Help Manual

Help pageTopics
calc_baranyi_fit_lagcalc_baranyi_fit_lag
calc_lagcalc_lag
calc_lag_fit_to_baranyi_with_lagcalc_lag_fit_to_baranyi_with_lag
calc_lag_fit_to_logistic_with_lagcalc_lag_fit_to_logistic_with_lag
calc_lagistic_fit_lagcalc_lagistic_fit_lag
choose_lag_fit_algorithm_baranyichoose_lag_fit_algorithm_baranyi
choose_lag_fit_algorithm_logisticchoose_lag_fit_algorithm_logistic
compare_algorithmscompare_algorithms
cut_the_data Subsets the data frame containing only the observations up to the specified maximum timecut_the_data
fit_exp_lagfit_exp_lag
fit_exp_lag_to_curvefit_exp_lag_to_curve
fit_max_infl_lagfit_max_infl_lag
get_all_methods_lagget_all_methods_lag
get_def_pars Set defaults parameters used by calc_lag functionget_def_pars
get_init_pars_baranyiget_init_pars_baranyi
get_init_pars_logisticget_init_pars_logistic
get_lagget_lag
get_n0get_n0
get_themeget_theme
lag_biomass_incrlag_biomass_incr
make_grwoth_curve_dfmake_grwoth_curve_df
plot_dataplot_data
plot_lag_fitplot_lag_fit
smooth_data Smoothens growth curves datasmooth_data