compute_tau             Compute the FSSD feature matrix
cross_kernel            Score-derivative coupling of a base kernel
custom_stein_kernel     Create a Stein kernel from callbacks
densitygmm              Evaluate a Gaussian mixture density
eval_kernel             Evaluate a base kernel
find_median_distance    Median-heuristic squared scale
fmin_grid               Create the grid search used by Stein Points
fmin_mc                 Create the Monte Carlo search used by Stein
                        Points
fmin_nm                 Create the multi-start Nelder-Mead search used
                        by Stein Points
fssd_null_pvalue        Simulate the FSSD null distribution
fssd_opt_test           FSSD test with optimized test locations
fssd_rand_test          FSSD test with random test locations
fssd_statistic          Compute the scaled FSSD test statistic
fssd_test               Finite Set Stein Discrepancy goodness-of-fit
                        test
get_score_evaluator     Create a score function for a fixed Gaussian
                        mixture model
gmm                     Create a Gaussian mixture model
grad_theta_v_kernel     Gradient of the local FSSD-opt objective
grad_x_kernel           Differentiate a base kernel in its first
                        argument
kernel_scale2           Read or replace a kernel's squared scale
ksd_u_bootstrap         Centered multinomial bootstrap for KSD-U
ksd_u_statistic         Compute the KSD-U statistic
ksd_u_test              KSD-U goodness-of-fit test for independent
                        observations
ksd_uq_matrix           Build the Stein-kernel matrix for the KSD tests
ksd_v_bootstrap         Wild bootstrap for KSD-V
ksd_v_statistic         Compute the KSD-V statistic
ksd_v_test              KSD-V goodness-of-fit test with wild-bootstrap
                        calibration
mala                    Run a Metropolis-adjusted Langevin chain
print.SteinKernel       Print a Stein kernel
print.stein_points      Print a Stein point set
print.svgd              Print an SVGD fit
rgmm                    Sample from a Gaussian mixture model
rwm                     Run a Gaussian random-walk Metropolis chain
sp_mcmc                 Select Stein points from short Markov chains
sp_mcmc_eval_candidates
                        Score candidate points with the greedy Stein
                        objective
stein_codescent         Refine Stein Points by coordinate descent
stein_kernel            Create a built-in Stein kernel
stein_kernel_imq_score
                        Create a score-distance IMQ Stein kernel
stein_kernel_inverse_log
                        Create an inverse-log Stein kernel
stein_kernel_matrix     Assemble the pairwise Stein-kernel matrix
stein_points            Construct points by Stein discrepancy
                        minimization
stein_thinning          Select existing samples by Stein thinning
steinsampling-package   steinsampling: Stein tests and Stein sampling
                        tools
summary.gmm             Summarize a Gaussian mixture model
summary.sp_mcmc         Summarize an SP-MCMC point set
summary.stein_points    Summarize a Stein point set
summary.svgd            Summarize an SVGD fit
svgd                    Transport particles with Stein variational
                        gradient descent
trace_mixed_kernel      Mixed-derivative trace of a base kernel
