basic_theme             A generic basic theme for time courses. It
                        extends ggplot2 theme_classic().
check_exp_dataset       Check that the experimental data set exists.
combine_param_best_fits_stats
                        Combine the parameter best fits statistics.
combine_param_ple_stats
                        Combine the parameter PLE statistics.
compute_aic             Compute the Akaike Information Criterion.
                        Assuming additive Gaussian measurement noise of
                        width 1, the term -2ln(L(theta|y)) ~ SSR ~
                        Chi^2
compute_aicc            Compute the corrected Akaike Information
                        Criterion. Assuming additive Gaussian
                        measurement noise of width 1, the term
                        -2ln(L(theta|y)) ~ SSR ~ Chi^2
compute_bic             Compute the Bayesian Information Criterion.
                        Assuming additive Gaussian measurement noise of
                        width 1, the term -2ln(L(theta|y)) ~ SSR ~
                        Chi^2
compute_cl_objval       Compute the confidence level based on the
                        minimum objective value.
compute_fratio_threshold
                        Compute the fratio threshold for the confidence
                        level.
compute_sampled_ple_stats
                        Compute the table for the sampled PLE
                        statistics.
gen_stats_table         Generate a table of statistics for each model
                        readout.
get_param_names         Get parameter names
get_sorted_level_indexes
                        Return the indexes of the files as sorted by
                        levels.
histogramplot           Plot a generic histogram
insulin_receptor_1      A stochastic model simulation
insulin_receptor_2      A stochastic model simulation
insulin_receptor_3      A stochastic model simulation
insulin_receptor_IR_beta_pY1146
                        A stochastic simulation data set for the
                        insulin receptor beta phosphorylated at pY1146.
insulin_receptor_all_fits
                        A parameter estimation data set including all
                        the evaluated fits.
insulin_receptor_best_fits
                        A parameter estimation data set including only
                        the best evaluated fits.
insulin_receptor_exp_dataset
                        Experimental data set for the insulin receptor
                        beta phosphorylated at pY1146 as published in
                        Dalle Pezze et al. Science Signaling 2012.
insulin_receptor_ps1_l0
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 0.
insulin_receptor_ps1_l1
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 1.
insulin_receptor_ps1_l11
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 11.
insulin_receptor_ps1_l13
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 13.
insulin_receptor_ps1_l14
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 14.
insulin_receptor_ps1_l16
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 16.
insulin_receptor_ps1_l3
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 3.
insulin_receptor_ps1_l4
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 4.
insulin_receptor_ps1_l6
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 6.
insulin_receptor_ps1_l8
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 8.
insulin_receptor_ps1_l9
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 1 model
                        parameter. The initial amount of IR-beta is
                        approx 9.
insulin_receptor_ps2_tp2
                        A deterministic simulation of the insulin
                        receptor model upon scanning of 2 model
                        parameters.
kurtosis                Calculate the kurtosis of a numeric vector
leftCI                  Return the left value of the parameter
                        confidence interval. The provided dataset has
                        two columns: ObjVal | ParamValue
load_exp_dataset        Load the experimental data set.
normalise_vec           Normalise a vector within 0 and 1
objval.col              The name of the Objective Value column
objval_vs_iters_analysis
                        Analysis of the Objective values vs Iterations.
parameter_density_analysis
                        Parameter density analysis.
parameter_pca_analysis
                        PCA for the parameters. These plots rely on
                        factoextra fviz functions.
pca_theme               A generic basic theme for pca. It extends
                        ggplot2 theme_classic().
pe_ds_preproc           Parameter estimation pre-processing. It renames
                        the data set columns, and applies a log10
                        transformation if logspace is TRUE.  If
                        all.fits is true, it also computes the
                        confidence levels.
plot_comb_sims          Plot the simulation time courses using a
                        heatmap representation.
plot_combined_tc        Plot repeated time courses in the same plot
                        with mean, 1 standard deviation, and 95%
                        confidence intervals.
plot_double_param_scan_data
                        Plot model double parameter scan time courses.
plot_fits               Plot the number of iterations vs objective
                        values in log10 scale.
plot_heatmap_tc         Plot time courses organised as data frame
                        columns with a heatmap.
plot_objval_vs_iters    Plot the Objective values vs Iterations
plot_parameter_density
                        Plot parameter density.
plot_raw_dataset        Add experimental data points to a plot. The
                        length of the experimental time course to plot
                        is limited by the length of the simulated time
                        course (=max_sim_tp).
plot_repeated_tc        Plot repeated time courses in the same plot
                        separately. First column is Time.
plot_sampled_2d_ple     Plot 2D profile likelihood estimations.
plot_sampled_ple        Plot the sampled profile likelihood estimations
                        (PLE). The table is made of two columns: ObjVal
                        | Parameter
plot_sep_sims           Plot the simulations time course separately.
plot_single_param_scan_data
                        Plot model single parameter scan time courses
plot_single_param_scan_data_homogen
                        Plot model single parameter scan time courses
                        using homogeneous lines.
replace_colnames        Rename data frame columns. 'ObjectiveValue' is
                        renamed as 'ObjVal'. Substrings 'Values.' and
                        '..InitialValue' are removed.
rightCI                 Return the right value of the parameter
                        confidence interval. The provided dataset has
                        two columns: ObjVal | ParamValue
sampled_2d_ple_analysis
                        2D profile likelihood estimation analysis.
sampled_ple_analysis    Run the profile likelihood estimation analysis.
sbpiper_pe              Main R function for SBpipe pipeline:
                        parameter_estimation().
sbpiper_ps1             Main R function for SBpipe pipeline:
                        parameter_scan1().
sbpiper_ps2             Main R function for SBpipe pipeline:
                        parameter_scan2().
sbpiper_sim             Main R function for SBpipe pipeline:
                        simulate().
scatterplot             Plot a generic scatter plot
scatterplot_log10       Plot a generic scatter plot in log10 scale
scatterplot_ple         Plot a profile likelihood estimation (PLE)
                        scatter plot
scatterplot_w_colour    Plot a scatter plot using a coloured palette
skewness                Calculate the skewness of a numeric vector
summarise_data          Summarise the model simulation repeats in a
                        single file.
tc_theme                A theme for time courses. It extends ggplot2
                        theme_classic().
