Package: rbbnp 0.2.0

rbbnp: A Bias Bound Approach to Non-Parametric Inference

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) <doi:10.1093/restud/rdz065>.

Authors:Xinyu DAI [aut, cre], Susanne M Schennach [aut]

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# Install 'rbbnp' in R:
install.packages('rbbnp', repos = c('https://dxy99999.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 129 downloads 7 exports 38 dependencies

Last updated 1 days agofrom:90346db31c. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 26 2025
R-4.5-winOKMar 26 2025
R-4.5-macOKMar 26 2025
R-4.5-linuxOKMar 26 2025
R-4.4-winOKMar 26 2025
R-4.4-macOKMar 26 2025
R-4.4-linuxOKMar 26 2025
R-4.3-winOKMar 26 2025
R-4.3-macOKMar 26 2025

Exports:biasBound_condExpectationbiasBound_densitycreate_biasBound_configcreate_kernel_functionsDATA_PATHEXT_DATA_PATHplot_ft

Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpracmapurrrR6RColorBrewerrlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Bias bound approach for conditional expectation estimationbiasBound_condExpectation
Bias bound approach for density estimationbiasBound_density
Create a configuration object for bias bound estimationscreate_biasBound_config
Create kernel functions based on configurationcreate_kernel_functions
The Path to the Data FolderDATA_PATH
Epanechnikov Kernelepanechnikov_kernel
Fourier Transform Epanechnikov Kernelepanechnikov_kernel_ft
The Path to the External Data Folder for Non-R Data FilesEXT_DATA_PATH
Approximation Function for Intensive Calculationsfun_approx
Generate Sample Datagen_sample_data
Kernel point estimationget_avg_f1x
Kernel point estimationget_avg_fyx
Compute Sample Average of Fourier Transform Magnitudeget_avg_phi
Compute log sample average of fourier transform and get modget_avg_phi_log
get the conditional variance of Y on X for given xget_conditional_var
get the estimation of A and rget_est_Ar
get the estimation of Bget_est_B
Estimation of bias b1xget_est_b1x
Estimation of bias byxget_est_byx
get the estimation of Vyget_est_vy
Estimation of sigmaget_sigma
Estimation of sigma_yxget_sigma_yx
get xi intervalget_xi_interval
Kernel Regression functionkernel_reg
Normal Kernel Functionnormal_kernel
Fourier Transform of Normal Kernelnormal_kernel_ft
Plot the Fourier Transformplot_ft
Generate n samples from the distributionrpoly01
Sample Datasample_data
Infinite Kernel Functionsinc
Define the closed form FT of the infinite order kernel sin(x)/(pi*x)sinc_ft
True density of 2-fold uniform distributiontrue_density_2fold
Define the inverse Fourier transform function of WW_kernel
Define the Fourier transform of a infinite kernel proposed in Schennach 2004W_kernel_ft