<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0121-7488</journal-id>
<journal-title><![CDATA[Ciencia en Desarrollo]]></journal-title>
<abbrev-journal-title><![CDATA[Ciencia en Desarrollo]]></abbrev-journal-title>
<issn>0121-7488</issn>
<publisher>
<publisher-name><![CDATA[Universidad Pedagógica y Tecnológica de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-74882024000200135</article-id>
<article-id pub-id-type="doi">10.19053/uptc.01217488.v15.n2.2024.16145</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Stests: an R package to perform multivariate statistical tests]]></article-title>
<article-title xml:lang="es"><![CDATA[Stests: una librería de R para realizar pruebas de hipótesis multivariadas]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Piedrahita García]]></surname>
<given-names><![CDATA[Jean Paul]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández Barajas]]></surname>
<given-names><![CDATA[Freddy]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>15</volume>
<numero>2</numero>
<fpage>135</fpage>
<lpage>142</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-74882024000200135&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-74882024000200135&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-74882024000200135&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The Behrens-Fisher problem refers to a statistical challenge in comparing the mean vectors of two normally distributed p-variate populations when the covariance matrices of these populations are assumed to be unequal. This problem can be addressed using Hotelling's T2 test that requires equality of covariance matrices. However, when the assumption of equality between the two covariance matrices is violated, the performance of this test can be affected, leading to incorrect conclusions. This article presents the implementation of 11 alternative tests proposed in the statistical literature for the Behrens-Fisher problem. These tests are hosted in the stests library in R, and any of these tests can be used through a single function. Additionally, this article conducted a Monte Carlo simulation study in which factors such as sample size, the distance between the mean vectors, and a scaling factor between the covariance matrices were examined. The results found that the rejection rate of the null hypothesis (H0: &#956; 1 = &#956; 2) increases when there is a greater discrepancy between the two mean vectors and when the sample size increases. The results demonstrate that all the tests developed in the stests package, which address the multivariate Behrens-Fisher problem, are plausible for comparing two mean vectors.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El problema de Behrens-Fisher se refiere a un desafío estadístico en la comparación de vectores de medias de dos poblaciones distribuidas normal p-variadas cuando se asume que las matrices de covarianzas de estas poblaciones no son iguales. Este problema se puede abordar mediante la prueba T 2 de Hotelling y que exige igualdad de matrices de covarianzas. Sin embargo, cuando el supuesto de igualdad entre las dos matrices de covarianzas se viola, el desempeño de esta prueba puede verse afectada, dando lugar a conclusiones incorrectas. En este artículo se muestra la implementación de 11 pruebas alternativas propuestas en la literatura estadística para el problema de Behrens-Fisher, estas pruebas están alojadas en la librería stests en R y por medio de una sola función se puede usar cualquiera de estas pruebas. Adicionalmente, en este artículo se hizo un estudio de simulación Monte Carlo en el cual se estudiaron factores como el tamaño de muestra, la distancia entre los vectores de medias y un factor escalar entre las matrices de covarianza. Como resultado se encontró que la tasa de rechazos de la hipótesis nula (H0: &#956; 1 = &#956; 2) aumenta cuando hay una mayor discrepancia entre los dos vectores de media y cuando el tamaño de muestra aumenta. Los resultados demuestran que todas las pruebas desarrolladas en el paquete stests, que abordan el problema multivariado de Behrens-Fisher, son plausibles para comparar dos vectores de medias.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Behrens-Fisher problem]]></kwd>
<kwd lng="en"><![CDATA[multivariate statistic]]></kwd>
<kwd lng="en"><![CDATA[statistical test]]></kwd>
<kwd lng="es"><![CDATA[Problema de Behrens-Fisher]]></kwd>
<kwd lng="es"><![CDATA[estadística multivariada]]></kwd>
<kwd lng="es"><![CDATA[test estadístico]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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