<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>danielhermosilla.r-universe.dev</title><link>https://danielhermosilla.r-universe.dev</link><description>Recent package updates in danielhermosilla</description><generator>R-universe</generator><image><url>https://github.com/danielhermosilla.png</url><title>R packages by danielhermosilla</title><link>https://danielhermosilla.r-universe.dev</link></image><lastBuildDate>Thu, 09 Jul 2026 02:09:18 GMT</lastBuildDate><item><title>[danielhermosilla] fastei 1.1.0</title><author>daniel.hermosilla.r@ug.uchile.cl (Daniel Hermosilla)</author><description>Estimates the probability matrix for the R×C Ecological
Inference problem using the Expectation-Maximization Algorithm
with four approximation methods for the E-Step, and an exact
method as well. It also provides a bootstrap function to
estimate the standard deviation of the estimated probabilities.
In addition, it has functions that aggregate rows optimally to
have more reliable estimates in cases of having few data
points. For comparing the probability estimates of two groups,
a Wald test routine is implemented. The library has data from
the first round of the Chilean Presidential Election 2021 and
can also generate synthetic election data. Methods described in
Thraves, Charles; Ubilla, Pablo; Hermosilla, Daniel (2024) ''A
Fast Ecological Inference Algorithm for the R×C case''
&lt;doi:10.2139/ssrn.4832834&gt;.</description><link>https://github.com/r-universe/danielhermosilla/actions/runs/28991507031</link><pubDate>Thu, 09 Jul 2026 02:09:18 GMT</pubDate><r:package>fastei</r:package><r:version>1.1.0</r:version><r:status>success</r:status><r:repository>https://danielhermosilla.r-universe.dev</r:repository><r:upstream>https://github.com/danielhermosilla/ecological-inference-elections</r:upstream><r:article><r:source>demonstration.Rmd</r:source><r:filename>demonstration.html</r:filename><r:title>Demonstration of the package usage</r:title><r:created>2025-03-30 15:06:58</r:created><r:modified>2025-05-20 00:34:19</r:modified></r:article><r:article><r:source>covariates.Rmd</r:source><r:filename>covariates.html</r:filename><r:title>Using the covariate approach</r:title><r:created>2026-01-17 01:20:27</r:created><r:modified>2026-01-17 01:20:27</r:modified></r:article></item></channel></rss>