working on JOSS review

README.md

@@ -33,6 +33,9 @@ To install/update in R type:
 install.packages('lpcde')
 ```
 
+## Contributing
+If you have any contributions to help improve or increase the functionality of this package, then please feel free to contribute, by opening a PR or an issue if you have any suggestions.
+
 ## Major Updates
 Details regarding package updates can be found in [NEWS.md](https://github.com/nppackages/lpcde/blob/main/R/lpcde/NEWS.md).
 

paper.bib

@@ -4,8 +4,9 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 @article{CCJM_2024_Bernoulli,
-    title={Local Polynomial Conditional Density Estimators},
+    title={Boundary adaptive local polynomial conditional density estimators},
     author={Cattaneo, Matias D. and Chandak, Rajita and Jansson, Michael and Ma, Xinwei},
+    doi={10.3150/23-BEJ1711},
     journal={Bernoulli},
     volume={30}, number={4}, pages={3193-3223}, year={2024}
 }
@@ -26,6 +27,7 @@ @article{Calonico-Cattaneo-Farrell_2018_JASA
   pages =         {767--779},
   title =         {On the Effect of Bias Estimation on Coverage Accuracy
                    in Nonparametric Inference},
+                   doi = {10.1080/01621459.2017.1285776},
   volume =        {113},
   year =          {2018},
 }
@@ -38,57 +40,66 @@ @article{Calonico-Cattaneo-Farrell_2022_Bernoulli
   pages =         {2998--3022},
   title =         {Coverage Error Optimal Confidence Intervals for Local
                    Polynomial Regression},
+                   doi = {10.3150/21-bej1445},
   volume =        {28},
   year =          {2022},
 }
 
 @article{DeGooijer-Zerom_2003_SN,
   title={On Conditional Density Estimation},
   author={De Gooijer, Jan G and Zerom, Dawit},
+  doi={10.1111/1467-9574.00226},
   journal={Statistica Neerlandica}, volume={57}, number={2}, pages={159--176}, year={2003}
 }
 
 @book{Fan-Gijbels_1996_Book,
     title	={Local Polynomial Modelling and Its Applications},
     author	={Fan, Jianqing and Gijbels, Irene},
+    doi={10.1201/9780203748725},
     publisher={Chapman \& Hall/CRC}, year	={1996}
 }
 
 @article{Fan-Yao-Tong_1996_Biometrika,
     title={Estimation of Conditional Densities and Sensitivity Measures in Nonlinear Dynamical Systems},
     author={Fan, Jianqing and Yao, Qiwei and Tong, Howell},
+    doi={10.1093/biomet/83.1.189},
     journal={Biometrika}, volume={83}, number={1}, pages={189--206}, year={1996}
 }
 
 @article{Hall-Racine-Li_2004_JASA,
     title={Cross-Validation and the Estimation of Conditional Probability Densities},
     author={Hall, Peter and Racine, Jeff and Li, Qi},
+    doi={10.1198/016214504000000548},
     journal={Journal of the American Statistical Association}, volume={99}, number={468}, pages={1015--1026}, year={2004}
 }
 
 @article{Hall-Wolff-Yao_1999_JASA,
     title={Methods for Estimating a Conditional Distribution Function},
     author={Hall, Peter and Wolff, Rodney CL and Yao, Qiwei},
+    doi={10.2307/2669691},
     journal={Journal of the American Statistical Association}, volume={94}, number={445}, pages={154--163}, year={1999}
 }
 
 @book{scott2015multivariate,
   title={Multivariate Density Estimation: Theory, Practice, and Visualization},
   author={Scott, David W},
+  doi={10.2307/1270280},
   year={2015},
   publisher={John Wiley \& Sons}
 }
 
 @book{simonoff2012smoothing,
   title={Smoothing Methods in Statistics},
   author={Simonoff, Jeffrey S},
+  doi={10.1007/978-1-4612-4026-6},
   year={2012},
   publisher={Springer--Verlag}
 }
 
 @book{Wand-Jones_1995_Book,
 title	={Kernel Smoothing},
 author  ={Wand, M.P. and M.C. Jones},
+doi={10.1002/9781118890370},
 publisher={Chapman \& Hall/CRC},
 year={1995}
 }
@@ -97,6 +108,7 @@ @Article{np
     title = {Nonparametric Econometrics: The np Package},
     author = {Tristen Hayfield and Jeffrey S. Racine},
     journal = {Journal of Statistical Software},
+    doi={10.18637/jss.v027.i05},
     year = {2008},
     volume = {27},
     number = {5}
@@ -106,13 +118,15 @@ @Book{ggplot2
   author = {Hadley Wickham},
   title = {ggplot2: Elegant Graphics for Data Analysis},
   publisher = {Springer-Verlag New York},
+  doi={10.1007/978-0-387-98141-3},
   year = {2016},
   isbn = {978-3-319-24277-4}
 }
 
 @Manual{hdrcde,
   title = {hdrcde: Highest Density Regions and Conditional Density Estimation},
   author = {Rob J Hyndman and Jochen Einbeck and Matthew P Wand},
+  doi={10.32614/cran.package.hdrcde},
   year = {2021},
   note = {\proglang{R} package version 3.4}
 }

paper.md

@@ -8,24 +8,25 @@ tags:
 - local polynomials
 date: "21 August 2024"
 output:
+  pdf_document: default
   html_document:
     df_print: paged
 authors:
 - name: Matias D. Cattaneo
-  orcid: "0000-0003-0493-7506"
+  orcid: 0000-0003-0493-7506
   affiliation: 1
 - name: Rajita Chandak
-  orcid: "0009-0006-4289-2520"
-  corresponding: true
+  orcid: 0009-0006-4289-2520
+  corresponding: yes
   affiliation: 2
 - name: Michael Jansson
-  orcid: "0000-0003-4678-7518"
+  orcid: 0000-0003-4678-7518
   affiliation: 3
 - name: Xinwei Ma
-  orcid: "0000-0001-8827-9146"
+  orcid: 0000-0001-8827-9146
   affiliation: 4
 bibliography: paper.bib
-link-citations: true
+link-citations: yes
 affiliations:
 - name: Department of Operations Research and Financial Engineering, Princeton University,
     USA
@@ -48,7 +49,7 @@ This package is currently the only open source implementation of an estimator of
 
 # Statement of need
 
-@Wand-Jones_1995_Book, @Fan-Gijbels_1996_Book, @simonoff2012smoothing, and @scott2015multivariate give textbook introductions to kernel-based density and local polynomial estimation and inference methods. The core idea underlying the estimator implemented in `lpcde` is to use kernel-based local polynomial smoothing methods to construct an automatic boundary adaptive estimator for conditional CDFs, conditional PDFs, and derivatives thereof. The estimator implemented in this package consists of two steps. The first step estimates the conditional distribution function using standard local polynomial regression methods, and the second step applies local polynomial smoothing to the (non-smooth) local polynomial conditional CDF estimate from the first step to obtain a smooth estimate of the conditional CDF, conditioal PDF, and derivatives thereof.
+@Wand-Jones_1995_Book, @Fan-Gijbels_1996_Book, @simonoff2012smoothing, and @scott2015multivariate give textbook introductions to kernel-based density and local polynomial estimation and inference methods. The core idea underlying the estimator implemented in `lpcde` is to use kernel-based local polynomial smoothing methods to construct an automatic boundary adaptive estimator for conditional CDFs, conditional PDFs, and derivatives thereof. The estimator implemented in this package consists of two steps. The first step estimates the conditional distribution function using standard local polynomial regression methods, and the second step applies local polynomial smoothing to the (non-smooth) local polynomial conditional CDF estimate from the first step to obtain a smooth estimate of the conditional CDF, conditional PDF, and derivatives thereof.
 
 A distinct advantage of this estimation method over existing ones is its boundary adaptivity for a possibly unknown compact support of the data. Furthermore, the estimator has a simple closed form representation, which leads to easy and fast implementation. Unlike other boundary adaptive procedures, the estimation procedures implemented in the package `lpcde` do not require pre-processing of data, and thus avoid the challenges of hyper-parameter tuning: only one bandwidth parameter needs to be selected for implementation. See @CCJM_2024_Bernoulli and @CCJM_2024_lpcde for more details.
 

todo.org

@@ -1,12 +1,17 @@
 #+title: lpcde
 #+author: Rajita Chandak
 
-* DONE Add code coverage github action
-
-* DONE Multivariate extension
-
-* TODO Nonnegative constraint
-
-* TODO Integrates to 1 constraint
-
-* TODO Add tutorial for lpcde
+* JOSS Revision
+** TODO bandwidth selection error example
+** TODO check grid redundancy
+** TODO adding predict method
+** TODO add more unit tests
+*** TODO checking integrates to 1
+*** TODO checking non-negativity
+** TODO ggplot deprecated warnings
+** DONE add missing doi
+** DONE fix main paper reference
+** DONE spelling mistake on pg 2
+** TODO add haldensify to table
+** TODO check mit liscense
+** DONE add community guideline for contributions