aggregate_study_info    create a table with aggregated data: each row
                        contains information about control and
                        treatments of a single study
animal_info_classification
                        Generate table representing number of animals
                        in classification groups
assess_efficacy         Credible interval (or say Bayesian confidence
                        interval) of the mean difference between two
                        groups (treatment and reference) is used to
                        assess the efficacy. If 0 falls outside the
                        interval, the drug was considered significantly
                        effective
below_min_points        makes df with data to be excluded
calc_gr                 Function to return rate of growth (e.g. the
                        slope after a log transformation of the tumour
                        data against time)
calc_probability        Calculate probability of categories
calc_survived           Calculate percentage of survived animals
change_time_multi       Get an array with change_time for studies from
                        the population-level effects, multiple studies
change_time_single      Get a change time from the population-level
                        effects, single study
classify_data_point     Classify individual data points as Responders
                        or Non-responders
classify_subcategories
                        Make predictions for subcategories
classify_type_responder
                        Classify tumour based on the growth rate and
                        the p_value for a two-sided T test Tumour will
                        be considered as "Non-responder", "Modest
                        responder", "Stable responder" or "Regressing
                        responder"
clean_string            function to remove hyphens, underscores, spaces
                        and transform to lowercase
control_growth_plot     Function to plot a control growth profile
example_data            Tumour volume data over time for in-vivo
                        studies
exclude_data            Filter rows to exclude from the analysis
expand_palette          Function to expand a vector of colors if needed
f_start                 Calculate coefficients for a nonlinear model
get_responder           Classify tumour based on response status of
                        individuals
guess_match             function to search for the possible critical
                        columns in a data.frame
hide_outliers           Function to hide outliers in boxplots with
                        jitterdodge as suggested
load_data               function to read data from users (.csv or .xlsx
                        files)
make_terms              Create a character vector with the names of
                        terms from model, for which predictions should
                        be displayed Specific values are specified in
                        square brackets
model_control           Build model and make predictions
notify_error_and_reset_input
                        Display a popup message and reset fileInput
ordered_regression      Fit model (Bayesian ordered logistic
                        regression)
plot_animal_info        Plot representing number of animals in
                        classification groups
plot_class_gr           Function to plot classification over growth
                        rate
plot_class_tv           Function to plot classification over tumour
                        volume
plot_proportions        Plot estimated proportions
plot_waterfall          Function to plot waterfall
plotly_volume           Create volume plot for one-batch data
predict_lm              Make predictions, linear model
predict_nlm_multi       Make predictions based on non-linear model,
                        multiple studies
predict_nlm_single      Make predictions based on non-linear model,
                        single study
predict_regr_model      Make predictions
run_app                 Run the Shiny Application
run_nl_model            Fit nonlinear model - continuous hinge function
set_waiter              Set up a waiting screen
