Man pages for deankoch/pkern
Fast Kriging with Product Kernels
| anyNA.bk | Check for presence of grid points with missing data (NAs) |
| as.double.bk | Coerce grid values to numeric (double type) |
| as.integer.bk | Coerce grid values to integer |
| as.logical.bk | Coerce grid values to logical |
| as.matrix.bk | convert to matrix |
| as.vector.bk | Convert grid data to vector of specified mode |
| bk_make | bk.R Dean Koch, 2022 S3 class and methods for bk objects... |
| bk_validate | VALIDATOR |
| dim.bk | Grid dimensions |
| is.na.bk | Indices of grid points with missing data (NAs) |
| length.bk | The number of grid-points |
| Math.bk | GROUP GENERICS |
| mean.bk | Calculate the mean value in a grid |
| Ops.bk | Operations group generics |
| pkern_add_bins | Add bin labels to a variogram data frame |
| pkern_bds | Set default parameter covariance parameter bounds |
| pkern_cmean | Compute ordinary kriging predictor (or variance) for data on... |
| pkern_coords | Return coordinates of a grid of points in column-vectorized... |
| pkern_corr | Stationary 1D correlation kernels |
| pkern_corr_mat | Construct 1D stationary correlation matrices for regularly... |
| pkern_export | Convert column-vectorized grid to SpatRaster |
| pkern_fit | Fit a covariance model to the data by maximum likelihood |
| pkern_GLS | Generalized least squares (GLS) estimator |
| pkern_grid | Make a pkern grid list object |
| pkern_kp | Return named vector of kernel parameters initialized to NA |
| pkern_LL | Likelihood of covariance model 'pars' given data 'g_obs' |
| pkern_mat2vec | Column-vectorization indices |
| pkern_nLL | Negative log-likelihood for parameter vector 'p' |
| pkern_optim | Fit covariance parameters to data by maximum (profile)... |
| pkern_pars | Initialize covariance kernel parameters for grid 'g' |
| pkern_pars_make | Build a parameter list defining the 2d spatial covariance... |
| pkern_pars_update | Convert covariance parameter list to/from vectorized form |
| pkern_plot | Plot grid data |
| pkern_plot_pars | Plot the covariance structure of a pkern model |
| pkern_plot_semi | Plot a semi-variogram |
| pkern_rescale | Up or down-scale a grid |
| pkern_sample_pt | Sub-grid point sampler for grid data |
| pkern_sample_vg | Sample point pair absolute differences for use in... |
| pkern_sim | Random draw from multivariate normal distribution for grids |
| pkern_snap | Snap a set of points to a grid |
| pkern_sub | Return a sub-grid of a pkern grid object |
| pkern_sub_find | Check vectorized grid data for non-NA points that form a... |
| pkern_sub_idx | Find column-vectorized index of a sub-grid |
| pkern_toep_mult | Efficiently compute yzx for symmetric Toeplitz matrices y and... |
| pkern_toString | Extract kernel parameters as plot-friendly strings |
| pkern_var | Generate a covariance matrix or its factorization |
| pkern_vario_fun | Theoretical variogram function |
| pkern_var_mult | Multiply a vector by a power of the covariance matrix |
| pkern_vec2mat | Invert column-vectorization indices |
| plot.bk | Heatmap plots |
| print.bk | OTHER METHODS |
| sub-.bk | Extract a bk list element (single-bracket access) |
| subset-.bk | Single-bracket assign |
| sub-sub-.bk-set | INDEXING METHODS |
| summary.bk | Grid summary |
