{"id":13189,"date":"2024-04-22T18:12:16","date_gmt":"2024-04-22T16:12:16","guid":{"rendered":"https:\/\/www.istat.it\/?page_id=13189"},"modified":"2026-04-20T12:32:59","modified_gmt":"2026-04-20T10:32:59","slug":"process-tools","status":"publish","type":"page","link":"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/process-tools\/","title":{"rendered":"Process tools"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><strong>Data integration<\/strong><\/h2>\n\n\n\n<p><strong><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/data-integration\/relais\/\">RELAIS<\/a><\/strong>&nbsp;(REcord Linkage At IStat)<br>Toolkit for dealing with record linkage projects.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/data-integration\/statmatch\/\">StatMatch<\/a><br>R package for data integration through statistical matching and, as a by-product, the ability to impute missing values in a data set.<\/p>\n\n\n\r\n\t<section class=\"gblock spacer white-bg  py-0\"  aria-labelledby=\"section-1\"><div class=\"container p-lg-0 block_count_1\" data-blockcount=\"1\"><div class=\"row pb-2\">\t<div class=\"col-12\">\r\n\t\t<div><\/div>\r\n\t<\/div>\r\n\t<\/div><\/div><\/section>\n\n\n<p><\/p>\n\n\n\r\n\t<section class=\"gblock spacer white-bg  py-0\"  aria-labelledby=\"section-2\"><div class=\"container p-lg-0 block_count_2\" data-blockcount=\"2\"><div class=\"row pb-2\">\t<div class=\"col-12\">\r\n\t\t<div><\/div>\r\n\t<\/div>\r\n\t<\/div><\/div><\/section>\n\n\n<h2 class=\"wp-block-heading\"><strong>Detection and treatment of measurement errors and imputation of<\/strong>&nbsp;<strong>partial non-responses<\/strong><\/h2>\n\n\n\n<p><strong><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/detection-and-treatment-of-measurement-errors-and-imputation-of-partial-non-responses\/banff\/\">Banff<\/a><\/strong><br>Edit and imputation system for numeric and continuous variables.<\/p>\n\n\n\n<p><strong><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/detection-and-treatment-of-measurement-errors-and-imputation-of-partial-non-responses\/canceis\/\">CANCEIS<\/a><\/strong>&nbsp;(CANadian Census Edit and Imputation System)<br>Edit and imputation system based on the Nearest-neighbour Imputation Methodology (NIM). The NIM allows the simultaneous hot-deck imputation of numeric and categorical variables based on a single donor. The basic entity that is processed (the unit) can be composed of one or more sub-units of lower hierarchical level.<\/p>\n\n\n\n<p><strong><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/detection-and-treatment-of-measurement-errors-and-imputation-of-partial-non-responses\/concordjava\/\">CONCORDJava<\/a><\/strong>&nbsp;(CONtrollo e CORrezione dei Dati version with Java interface)<br>Integrated system to check and correct the data (imputation). One of the modules (SCIA) implements the Fellegi-Holt Methodology to treat the inconsistencies between the values of qualitative variables.<\/p>\n\n\n\n<p><strong><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/detection-and-treatment-of-measurement-errors-and-imputation-of-partial-non-responses\/selemix\/\">SeleMix<\/a><\/strong>&nbsp;(Selective editing via Mixture models)<br>R package to treat quantitative data, which aims to identify a set of units affected by errors which potentially influence the interest estimates (selective editing).<\/p>\n\n\n\n<p><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/detection-and-treatment-of-measurement-errors-and-imputation-of-partial-non-responses\/unitmix\/\">UnitMix<\/a><br>The UnitMix package for the R environment provides tools to detect and correct errors in multivariate numeric data using model-based clustering.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/detection-and-treatment-of-measurement-errors-and-imputation-of-partial-non-responses\/univoutl\/\">UnivOutl<\/a><br>The univOutl package is package for the R environment that implements the main techniques for identifying outliers in data related to a single quantitative variable.<\/p>\n\n\n\n<p><\/p>\n\n\n\r\n\t<section class=\"gblock spacer white-bg  py-0\"  aria-labelledby=\"section-3\"><div class=\"container p-lg-0 block_count_3\" data-blockcount=\"3\"><div class=\"row pb-2\">\t<div class=\"col-12\">\r\n\t\t<div><\/div>\r\n\t<\/div>\r\n\t<\/div><\/div><\/section>\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Weighting, estimation and sampling error evaluation<\/strong><\/strong><\/h2>\n\n\n\n<p><strong><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/weighting-estimation-and-sampling-error-evaluation\/ever\/\">EVER<\/a><\/strong>&nbsp;(Estimation of Variance by Efficient Replication)<br>R package for calibration, estimation and sampling error assessment in complex sample surveys, based on replication methods .<\/p>\n\n\n\n<p><strong><a href=\"https:\/\/www.istat.it\/en\/classifications-and-tools\/methods-and-software-of-the-statistical-process\/process-phase\/weighting-estimation-and-sampling-error-evaluation\/regenesees\/\">ReGenesees<\/a><\/strong>&nbsp;(R evolved Generalised software for sampling estimates and errors in surveys)<br>R-based software system for design-based and model-assisted analysis of complex sample surveys.<\/p>\n\n\n\r\n\t<section class=\"gblock spacer white-bg  py-0\"  aria-labelledby=\"section-4\"><div class=\"container p-lg-0 block_count_4\" data-blockcount=\"4\"><div class=\"row pb-2\">\t<div class=\"col-12\">\r\n\t\t<div><\/div>\r\n\t<\/div>\r\n\t<\/div><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>Data integration RELAIS&nbsp;(REcord Linkage At IStat)Toolkit for dealing with record linkage projects. StatMatchR package for data integration through statistical matching and, as a by-product, the [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":2525,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-13189","page","type-page","status-publish","hentry"],"acf":[],"wpml_current_locale":"en_US","wpml_translations":[],"_links":{"self":[{"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/pages\/13189","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/comments?post=13189"}],"version-history":[{"count":6,"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/pages\/13189\/revisions"}],"predecessor-version":[{"id":124004,"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/pages\/13189\/revisions\/124004"}],"up":[{"embeddable":true,"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/pages\/2525"}],"wp:attachment":[{"href":"https:\/\/www.istat.it\/en\/wp-json\/wp\/v2\/media?parent=13189"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}