qdfauction: copyright notes
============================

The package is Copyright (C) 2026 Hassan Doosti and is distributed under the
GNU General Public License, version 3 or later. The parts listed below are
derived from code by other authors, who hold copyright in the original code
and are listed as contributors and copyright holders in DESCRIPTION; they
have been informed of their inclusion in this package.

The quantile density estimators in R/estimators.R (qdf_kernel,
qdf_kernel_corrected, qdf_poisson, qdf_bernstein, qdf_jones, qdf_soni,
qdf_indirect_poisson, quantile_indirect_poisson) and the cross-validation
criteria bcv_criterion() and rlcv_criterion() in R/bandwidth.R are revised,
vectorised versions of R code by Yogendra P. Chaubey, Isha Dewan and Jun Li
accompanying

  Chaubey, Y. P., Dewan, I. and Li, J. (2021). On some smooth estimators of
  the quantile function for a stationary associated process. Sankhya B,
  83 (Suppl 1), 114-139. <doi:10.1007/s13571-020-00242-x>

  Chaubey, Y. P., Dewan, I. and Li, J. (2024). On some non parametric
  estimators of the quantile density function for a stationary associated
  process. Communications in Statistics - Theory and Methods, 53, 5553-5573.

as further developed in Doosti, Dewan and Talebian (2025). The original code
is (C) Yogendra P. Chaubey, Isha Dewan and Jun Li.

kde_hh() and its internal helpers (hh_kernel, hh_hardle_c, hh_b0) in
R/competitors.R are an R translation of the MATLAB functions kspdf_bc.m,
evalkspdf.m, evalkspdf_num.m and evalkspdf_denom.m distributed by
Brent R. Hickman and Timothy P. Hubbard with

  Hickman, B. R. and Hubbard, T. P. (2015). Replacing sample trimming with
  boundary correction in nonparametric estimation of first-price auctions.
  Journal of Applied Econometrics, 30, 739-762. <doi:10.1002/jae.2385>

The original MATLAB code is (C) Brent R. Hickman and Timothy P. Hubbard; its
authors ask that users cite the paper above, which the documentation of
kde_hh() does.

scaling_function(), phi_jk(), dwt_periodic(), idwt_periodic() and
loclin_smooth() in R/wavelet_core.R are R translations of the MATLAB routines
Phijk.m (Daubechies-Lagarias evaluation of the scaling function), dwtr.m and
Idwtr.m (periodic discrete wavelet transform and its inverse) and loc_lin.m
(local linear smoothing) written by Brani Vidakovic and used in

  Ramirez, P. and Vidakovic, B. (2010). Wavelet density estimation for
  stratified size-biased sample. Journal of Statistical Planning and
  Inference, 140, 419-432.

and in Chesneau, Dewan and Doosti (2016). The original MATLAB code is
(C) Brani Vidakovic.

density_marmer_shneyerov() in R/competitors.R is an R translation of the
MATLAB implementation (qbest4.m) of the quantile-based estimator of

  Marmer, V. and Shneyerov, A. (2012). Quantile-based nonparametric inference
  for first-price auctions. Journal of Econometrics, 167(2), 345-357.
  <doi:10.1016/j.jeconom.2011.09.020>

The original code is (C) Vadim Marmer and Artyom Shneyerov and is publicly
available from the first author's website,
<https://vadimmarmer.com/files/auc-pdf_codes_and_paper.zip>.

fpa_density_ci() in R/fpa_density_ci.R implements the inference procedures
of

  Ma, J., Marmer, V. and Shneyerov, A. (2019). Inference for first-price
  auctions with Guerre, Perrigne, and Vuong's estimator. Journal of
  Econometrics, 211, 507-538. <doi:10.1016/j.jeconom.2019.02.006>

It was written from the paper; the authors' MATLAB simulation code, kindly
provided by Vadim Marmer, was used only to validate it and is not included.

The internal function values_zincenko() in R/fpa_revenue.R, used by
fpa_values(method = "zincenko"), is adapted from code provided by
Federico Zincenko implementing the estimator of

  Zincenko, F. (2024). Estimation and inference of seller's expected revenue
  in first-price auctions. Journal of Econometrics, 241(1), 105734.
  <doi:10.1016/j.jeconom.2024.105734>

The original code is (C) Federico Zincenko.

All other code is original to this package: it was written by the package
author, either from the published descriptions of the methods cited in the
documentation or from his own earlier code. Wavelet filter coefficients are
standard published constants.

The data set 'timber' is derived from the replication data of Lu and
Perrigne (2008) in the Journal of Applied Econometrics Data Archive; see
?timber.
