Package index
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df_confluency_htds - A high-throughput drug screening dataset example
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generate_initial_splitting() - Generates a splitting of the experimental units into groups
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get_best_splitting() - Generates a random splitting of the groups
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get_df_predict() - Get dataframe to predict the outcome measurement given a bayesian model
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get_difference_summarized() - Calculate the summary of the differences between control and all treatments
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get_long_df() - Get data in the long format
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get_pooled_sds() - Calculate pooled standard deviation from the first day of treatment
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get_slope_difference() - Calculate the difference in slopes given the days for a specific combination
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get_slope_differences() - Calculate the difference in slopes given a pair of days and global estimates
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get_slopes() - Get the slopes for each experimental unit
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get_stats_from_data() - Get dataframe with statistics from specified column value
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obj_function() - Objective function to obtain scores used in the randomization
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pdx_treat_ivis - Total flux of PDX T110
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pdx_treat_ivis_long - Total flux of a PDX in the long format
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plot_eff_size_dist() - Plot bayesian effect size
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plot_outcome_by_id() - Plot outcome values by ID
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plot_posterior_estimates() - Get plot with posterior linear predictors and credible intervals
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plot_summary_growth_curves() - Plot summary growth curves
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plot_summary_with_splines() - Get plot with summarised data and fit
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rule_splitting() - Function that defines what is considered to be a better splitting
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swap_splitting() - Change two cluster rows randomly from a dataframe
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t110_ivis - Total flux of PDX T110
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t110_ivis_long - Total flux of PDX T110 in long format