alpha_1                 Vector that defines the success probability
                        null curve.
beta_1                  Vector that defines the MEE under the
                        alternative hypothesis.
compute_m_sigma         Computes "M" and "Sigma" matrices for the
                        sandwich estimator of variance-covariance
                        matrix.
compute_ncp             Computes the non-centrality parameter for an F
                        distributed random variable in the context of a
                        MRT with binary outcome.
f_t_1                   A matrix defining the MEE under the alternative
                        hypothesis.
g_t_1                   A matrix defining the success probability null
                        curve.
is_full_column_rank     Check if a matrix is full column rank.
m_matrix_1              An example matrix for "bread" of sandwich
                        estimator of variance.
max_samp                Returns default maximum sample size to end
                        power_vs_n_plot().
min_samp                Compute minimum sample size.
mrt_binary_power        Calculate power for binary outcome MRT
mrt_binary_ss           Calculate sample size for binary outcome MRT
p_t_1                   A vector of randomization probabilities for
                        each time point.
power_summary           Calculate sample size at a range of power
                        levels.
power_vs_n_plot         Returns a plot of power vs sample size in the
                        context of a binary outcome MRT. See the
                        vignette for more details.
sigma_matrix_1          An example matrix for "meat" of sandwich
                        estimator of variance.
tau_t_1                 Vector that holds the average availability at
                        each time point.
