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rbbnp
View on CRAN: Click
here
Download and install rbbnp package within the R console
Install from CRAN:
install.packages("rbbnp")
Install from Github:
library("remotes")
install_github("cran/rbbnp") Install by package version:
library("remotes")
install_version("rbbnp", "0.3.0") Attach the package and use:
library("rbbnp")
Maintained by
Xinyu DAI
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-02-01
Latest Update: 2025-04-30
Description:
A novel bias-bound approach for non-parametric inference is introduced,
focusing on both density and conditional expectation estimation.
It constructs valid confidence intervals that account for the presence of
a non-negligible bias and thus make it possible to perform inference with
optimal mean squared error minimizing bandwidths.
This package is based on Schennach (2020) .
How to cite:
Xinyu DAI (2024). rbbnp: A Bias Bound Approach to Non-Parametric Inference. R package version 0.3.0, https://cran.r-project.org/web/packages/rbbnp. Accessed 15 Jul. 2026.
Previous versions and publish date:
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Complete documentation for rbbnp
Functions, R codes and Examples using
the rbbnp R package
Some associated functions: DATA_PATH . EXT_DATA_PATH . W_kernel . W_kernel_ft . biasBound_condExpectation . biasBound_density . epanechnikov_kernel . epanechnikov_kernel_ft . fun_approx . gen_sample_data . get_avg_f1x . get_avg_fyx . get_avg_phi . get_avg_phi_log . get_conditional_var . get_est_Ar . get_est_B . get_est_b1x . get_est_byx . get_est_vy . get_sigma . get_sigma_yx . get_xi_interval . kernel_reg . normal_kernel . normal_kernel_ft . plot_ft . rpoly01 . sample_data . sinc . sinc_ft . true_density_2fold .
Some associated R codes: gen_sample.R . globals.R . kernels.R . main.R . rbbnp-pkg.R . utils.R . Full rbbnp package functions and examples
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