Unified interface for the estimation of causal networks, including
    the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate
    additive noise model), 'bivariateCAM' (bivariate causal additive model),
    'CAM' (causal additive model) (from package 'CAM'; the package is 
    temporarily unavailable on the CRAN repository; formerly available versions 
    can be obtained from the archive), 'hiddenICP' (invariant
    causal prediction with hidden variables), 'ICP' (invariant causal prediction)
    (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence
    search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC
    Algorithm), 'FCI' (fast causal inference), 
    'RFCI' (really fast causal inference) (all from package 'pcalg') and
    regression.

Compare C-statistics (concordance statistics)
    between two survival models, using either bootstrap resampling (Harrell's C)
    or Uno's C with perturbation-resampling (from the survC1 package). Returns
    confidence intervals and a p-value for the difference in C-statistics.
    Useful for evaluating and comparing predictive performance of survival models.
    Methods implemented for Uno's C are described in Uno et al. (2011) <doi:10.1002/sim.4154>.

It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. It also calculates the expected effect and the probability of observing the composite endpoint, among others. The methodology can be found in Bofill & Gómez (2019) <doi:10.1002/sim.8092> and Gómez & Lagakos (2013) <doi:10.1002/sim.5547>.

Compares two dataframes which have the same column
    structure to show the rows that have changed. Also gives a git style diff format
    to quickly see what has changed in addition to summary statistics.

Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.