Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling

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Documentation for package ‘steinsampling’ version 0.1.1

• DESCRIPTION file.

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steinsampling-package	steinsampling: Stein tests and Stein sampling tools
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_uq_matrix	Build the Stein-kernel matrix for KSD-U
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_vq_matrix	Build the Stein-kernel matrix for KSD-V
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.gmm	Create a Gaussian mixture model
print.SteinKernel	Print a Stein kernel
print.stein_codescent	Print a Stein point set
print.stein_points	Print a Stein point set
print.svgd	Print a Stein point set
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_criterion	Create an SP-MCMC start-point rule
sp_mcmc_eval_candidates	Score SP-MCMC candidate points
sp_mcmc_select_start	Choose the next SP-MCMC chain start
sp_mcmc_state	Store the current SP-MCMC state for a start rule
steinsampling	steinsampling: Stein tests and Stein sampling tools
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	Select points by Stein discrepancy minimization
stein_thinning	Select existing samples by Stein thinning
svgd	Transport particles with Stein variational gradient descent
trace_mixed_kernel	Mixed-derivative trace of a base kernel