Package index • rbbnp

Main Estimation Functions

Core functions for density and regression estimation

biasBound_density(): Bias bound approach for density estimation
biasBound_condExpectation(): Bias bound approach for conditional expectation estimation

Bandwidth Selection

Functions for selecting optimal bandwidths

select_bandwidth(): Select Optimal Bandwidth
cv_bandwidth(): Cross-Validation for Bandwidth Selection
silverman_bandwidth(): Silverman's Rule of Thumb for Bandwidth Selection

S3 Methods for Density Objects

Methods for working with bbnp_density objects

print(<bbnp_density>): Print Method for bbnp_density Objects
summary(<bbnp_density>): Summary Method for bbnp_density Objects
plot(<bbnp_density>): Plot Method for bbnp_density Objects
coef(<bbnp_density>): Extract Coefficients from bbnp_density Object
confint(<bbnp_density>): Extract Confidence Intervals from bbnp_density Object

S3 Methods for Regression Objects

Methods for working with bbnp_regression objects

print(<bbnp_regression>): Print Method for bbnp_regression Objects
summary(<bbnp_regression>): Summary Method for bbnp_regression Objects
plot(<bbnp_regression>): Plot Method for bbnp_regression Objects
coef(<bbnp_regression>): Extract Coefficients from bbnp_regression Object
confint(<bbnp_regression>): Extract Confidence Intervals from bbnp_regression Object
fitted(<bbnp_regression>): Extract Fitted Values from bbnp_regression Object

Utilities

Helper functions and configuration

gen_sample_data(): Generate Sample Data
create_kernel_functions(): Create kernel functions based on configuration
create_biasBound_config(): Create a configuration object for bias bound estimations
plot_ft(): Plot the Fourier Transform (Deprecated)

Data

Sample datasets

sample_data: Sample Data

Internal Functions

Internal helper functions (not for general use)

get_avg_f1x(): Kernel point estimation
get_avg_fyx(): Kernel point estimation
get_avg_phi(): Compute Sample Average of Fourier Transform Magnitude
get_avg_phi_log(): Compute log sample average of fourier transform and get mod
get_conditional_var(): get the conditional variance of Y on X for given x
get_est_B(): get the estimation of B
get_est_b1x(): Estimation of bias b1x
get_est_byx(): Estimation of bias byx
get_est_vy(): get the estimation of Vy
get_sigma(): Estimation of sigma
get_sigma_yx(): Estimation of sigma_yx
get_xi_interval(): get xi interval
epanechnikov_kernel(): Epanechnikov Kernel
epanechnikov_kernel_ft(): Fourier Transform Epanechnikov Kernel
normal_kernel(): Normal Kernel Function
normal_kernel_ft(): Fourier Transform of Normal Kernel
W_kernel(): Define the inverse Fourier transform function of W
W_kernel_ft(): Define the Fourier transform of a infinite kernel proposed in Schennach 2004
print(<summary.bbnp_density>): Print Method for summary.bbnp_density Objects
print(<summary.bbnp_regression>): Print Method for summary.bbnp_regression Objects
fun_approx(): Approximation Function for Intensive Calculations
kernel_reg(): Kernel Regression function
true_density_2fold(): True density of 2-fold uniform distribution
rpoly01(): Generate n samples from the distribution
sinc(): Infinite Kernel Function
sinc_ft(): Define the closed form FT of the infinite order kernel sin(x)/(pi*x)