| autocorrelation_coeff_h | 'autocorrelation_coeff_h' Computes the approximate functional autocorrelation coefficient at a given lag. |
| autocorrelation_coeff_plot | Plot Confidence Bounds of Estimated Functional Autocorrelation Coefficients |
| autocov_approx_h | Compute the approximate autocovariance at specified lag |
| bartlett_kernel | Bartlett Kernel Function |
| block_bootsrap | 'block_bootstrap' Performs a block bootstrap on the functional data f_data with block size b. |
| brown_motion | 'brown_motion' Creates at J x N matrix, containing N independent Brownian motion sample paths in each of the columns. |
| B_h_bound | Compute weak white noise confidence bound for autocorrelation coefficient. |
| B_iid_bound | Compute strong white noise confidence bound for autocorrelation coefficient. |
| center | Center functional data |
| covariance_diag_store | List storage of diagonal covariances. |
| covariance_i_j | Compute the approximate covariance tensor for lag windows defined by i,j |
| covariance_i_j_vec | Compute the approximate covariance tensor for lag windows defined by i,j |
| daniell_kernel | Daniell Kernel Function |
| diagonal_autocov_approx_0 | Compute the diagonal covariance |
| diagonal_covariance_i | Compute the approximate diagonal covariance matrix for lag windows defined by i |
| far_1_S | 'far_1_S' Simulates an FAR(1,S)-fGARCH(1,1) process with N independent observations, each observed discretely at J points on the interval [0,1]. |
| fgarch_1_1 | 'fgarch_1_1' Simulates an fGARCH(1,1) process with N independent observations, each observed |
| fport_test | Compute Functional Hypothesis Tests |
| iid_covariance | Compute part of the covariance under a strong white noise assumption |
| iid_covariance_vec | Compute part of the covariance under a strong white noise assumption |
| independence_test | Independence Test |
| multi_lag_test | Multi-Lag Hypothesis Test |
| parzen_kernel | Parzen Kernel Function |
| Q_WS_hyp_test | Compute size alpha single-lag hypothesis test under weak or strong white noise assumption |
| scalar_covariance_i_j | Compute the approximate covariance at a point for lag windows defined by i,j |
| scalar_covariance_i_j_vec | Compute the approximate covariance at a point for lag windows defined by i,j |
| single_lag_test | Single-Lag Hypothesis Test |
| spectral_test | Spectral Density Test |