[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-227982-en":3,"doc-seo-227982-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},227982,687207412472,"Angel","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","haarfisz - Software to Perform Haar Fisz Transforms - Algorithm for Poisson Intensity Estimation","R package haarfisz provides a Haar-Fisz algorithm for denoising one-dimensional Poisson distributed sequences when the underlying intensity is not constant. The method uses a multiscale variance-stabilization approach based on the Haar-Fisz transform, including forward and inverse transforms and denoising on near-Gaussian sequences. It also supports cycle-spinning, enhancing robustness through repeated cyclic shifts before applying denoising and transforming back.","Package ‘haarfisz’  \nMay 8, 2026  \nType Package  \nTitle Software to Perform Haar Fisz Transforms Version 4.5.4  \nDate 2023-09-01  \nDepends R (>= 3.5.0), wavethresh  \nDescription A Haar-Fisz algorithm for Poisson intensity estimation.  \nWill denoise Poisson distributed sequences where  \nunderlying intensity is not constant. Uses the multiscale  \nvariance-stabilization method called the Haar-Fisz transform.  \nContains functions to carry out the forward and inverse  \nHaar-Fisz transform and denoising on near-Gaussian sequences.  \nCan also carry out cycle-spinning.  \nMain reference: Fryzlewicz, P. and Nason, G.P. (2004)  \n``A Haar-Fisz algorithm for Poisson intensity estimation.''  \nJournal of Computational and Graphical Statistics,  \n13, 621-638 . \u003Cdoi:10 . 1198/106186004X2697> .  \nLicense GPL (>= 2) NeedsCompilation no Author Piotr Fryzlewicz [aut],  \nGuy Nason [cre]  \nMaintainer Guy Nason \u003C[g.nason@imperial.ac.uk](g.nason@imperial.ac.uk)> Repository CRAN  \nDate/Publication 2023-09-01 11:20:02 UTC  \nContents  \nhaarfisz-package ...................................... 2  \ndenoise.poisson ....................................... 3  \n[hf.bt .............................................](hf.bt ............................................. 4)[ 4](hf.bt ............................................. 4)  \n[hf.cv .............................................](hf.cv ............................................. 5)[ 5](hf.cv ............................................. 5)  \n[hf.tiu .............................................](hf.tiu ............................................. 7)[ 7](hf.tiu ............................................. 7)  \n[hf.u .............................................](hf.u ............................................. 8)[ 8](hf.u ............................................. 8)  \n2 haarfisz-package  \nhft .............................................. 9  \nhft.inv ............................................ 11  \nshift.sequence ........................................ 12  \nxquake ............................................ 13  \nIndex 14  \n\n| haarfisz-package | A Haar-Fisz Algorithm for Poisson Intensity Estimation. |\n| --- | --- |\n\nDescription  \nPackage to denoise Poisson distributed sequence where underlying intensity is not constant. Uses the multiscale variance-stabilization method called the Haar-Fisz transform. Contains functions to carry out the foward and inverse Haar-Fisz transform and denoising on near-Gaussian sequences. Can also carry out cycle-spinning.  \nDetails  \nPackage to denoise Poisson distributed sequence where underlying intensity is not constant. Uses the multiscale variance-stabilization method called the Haar-Fisz transform. Contains functions to carry out the foward and inverse Haar-Fisz transform and denoising on near-Gaussian sequences. Can also carry out cycle-spinning. See main routine denoise.poisson  \nAuthor(s)  \nPiotr Fryzlewicz>  \nReferences  \nFryzlewicz, P. (2003) Wavelet Techniques for Time Series and Poisson Data. PhD Thesis, University of Bristol, Bristol, UK [https://www.ma.imperial.ac.uk/~gnason/Research/MAPZFthesis.](https://www.ma.imperial.ac.uk/~gnason/Research/MAPZFthesis.)[ ](https://www.ma.imperial.ac.uk/~gnason/Research/MAPZFthesis.)ps.gz  \nFryzlewicz, P. and Nason, G.P. (2004) A Haar-Fisz algorithm for Poisson intensity estimation. Journal of Computational and Graphical Statistics, 13, 621-638 . doi:10.1198/106186004X2697  \nNason, G.P. (2008) Wavelet Methods in Statistics with R. Springer: New York (Section 6.4) doi:10.1007/ 9780387759616  \nSee Also  \ndenoise.poisson, hft, hft. inv  \ndenoise.poisson 3  \nExamples  \n\\#\\#  \n\\# Main Poisson denoising function is denoise.poisson  \n\\#  \n\\# Forward Haar-Fisz transform is hft  \n\\#  \n\\# Inverse Haar-Fisz transform is hft. inv  \n\n| denoise.poisson | denoise.poisson |\n| --- | --- |\n\nDescription  \nMain routine of the package. Estimates the deterministic discretised intensity of a one-dimensional Poisson process using the Haar-Fisz ","cbCaivTudDANbZEM","https://ap.wps.com/l/cbCaivTudDANbZEM","pdf",144411,1,14,"English","en",105,"# haarfisz-package\n## denoise.poisson\n## hft\n## hft.inv\n## shift.sequence\n## xquake\n# denoise.poisson\n## Examples","[{\"question\":\"What problem does the haarfisz package address?\",\"answer\":\"It denoises one-dimensional Poisson distributed sequences where the underlying intensity varies over time rather than staying constant.\"},{\"question\":\"How does denoise.poisson work conceptually?\",\"answer\":\"It applies cyclic shifts to the data, performs the Haar-Fisz transform, applies a denoising method in the transformed domain, then uses the inverse transform and shifts back, optionally repeating across multiple cycle spins.\"},{\"question\":\"What outputs does denoise.poisson return?\",\"answer\":\"It returns a denoised estimate vector of the same length as the input Poisson count vector.\"}]","haarfisz - 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