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	<title>Curving Normality &#187; R-Project</title>
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	<description>&#34;The extra-ordinary lies within the curve of normality&#34;</description>
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		<title>Applied R: Manual for the quantitative social scientist</title>
		<link>http://www.rensenieuwenhuis.nl/applied-r-manualfor-the-quantitative-social-scientist/</link>
		<comments>http://www.rensenieuwenhuis.nl/applied-r-manualfor-the-quantitative-social-scientist/#comments</comments>
		<pubDate>Wed, 23 Mar 2011 10:50:31 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[Science]]></category>
		<category><![CDATA[manual]]></category>
		<category><![CDATA[R]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1425</guid>
		<description><![CDATA[Applied R for the quantitative social scientist is a manual on R written specifically as an introduction for the quantitative social scientist. To my opinion, R-Project is a magnificent statistical program, ready to be accepted and implemented in the social sciences. The flexibility of this program and the way data are handled gives the user a sense of closeness to and control over the data. I think this inspires users to analyze their data more creatively and sometimes in a more advanced way.]]></description>
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		<slash:comments>2</slash:comments>
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		<title>Index of the R-Sessions</title>
		<link>http://www.rensenieuwenhuis.nl/index-of-the-r-sessions/</link>
		<comments>http://www.rensenieuwenhuis.nl/index-of-the-r-sessions/#comments</comments>
		<pubDate>Mon, 17 May 2010 10:00:19 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[R]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1209</guid>
		<description><![CDATA[The R-Sessions are a series of blog entries on using R. A large part consists of an R-manual I once wrote. Other posts include some tricks I found out, as well as entries detailing functions and packages I wrote for ...]]></description>
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		<slash:comments>4</slash:comments>
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		<item>
		<title>R Sessions 33: Select (nested) observations with equal number of occurences</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-33-select-nested-observations-with-equal-number-of-occurences/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-33-select-nested-observations-with-equal-number-of-occurences/#comments</comments>
		<pubDate>Wed, 23 Sep 2009 10:00:05 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[balanced data]]></category>
		<category><![CDATA[merge]]></category>
		<category><![CDATA[R]]></category>
		<category><![CDATA[subset]]></category>
		<category><![CDATA[unbalanced data]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1107</guid>
		<description><![CDATA[Recently, I was contacted with an question about R code. A befriended researcher was working with nested data, which was unbalanced. He was working with data in a &#8216;long&#8217; format: all observations nested within the same group had the same ...]]></description>
		<wfw:commentRss>http://www.rensenieuwenhuis.nl/r-sessions-33-select-nested-observations-with-equal-number-of-occurences/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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		<title>Influence.ME: Simple Analysis</title>
		<link>http://www.rensenieuwenhuis.nl/influence-me-simple-analysis/</link>
		<comments>http://www.rensenieuwenhuis.nl/influence-me-simple-analysis/#comments</comments>
		<pubDate>Thu, 16 Jul 2009 11:00:19 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[example]]></category>
		<category><![CDATA[influential data]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[mixed effects]]></category>
		<category><![CDATA[multilevel]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1015</guid>
		<description><![CDATA[With the introduction of our new package for influential data influence.ME, I&#8217;m currently writing a manual for the package. This manual will address topics for both the experienced, and the inexperienced users. I will also present much of the content ...]]></description>
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		<slash:comments>1</slash:comments>
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		<item>
		<title>Presenting influence.ME at useR!</title>
		<link>http://www.rensenieuwenhuis.nl/presenting-influence-me-at-user/</link>
		<comments>http://www.rensenieuwenhuis.nl/presenting-influence-me-at-user/#comments</comments>
		<pubDate>Fri, 10 Jul 2009 09:49:33 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[mixed effects]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[social sciences]]></category>
		<category><![CDATA[useR!]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1028</guid>
		<description><![CDATA[Today I presented influence.ME at the useR! conference in Rennes. Influence.ME is an R package for detecting influential data in mixed models. I developed this package together with Ben Pelzer and Manfred te Grotenhuis. More information about influence.ME can be ...]]></description>
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		<slash:comments>2</slash:comments>
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		<item>
		<title>Influence.ME: don&#8217;t specify the intercept</title>
		<link>http://www.rensenieuwenhuis.nl/influence-me-dont-specify-the-intercept/</link>
		<comments>http://www.rensenieuwenhuis.nl/influence-me-dont-specify-the-intercept/#comments</comments>
		<pubDate>Thu, 18 Jun 2009 11:00:00 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[intercept]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[lmer]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1003</guid>
		<description><![CDATA[Just recently, I was contacted by a researcher who wanted to use influence.ME to obtain model estimates from which iteratively some data was deleted. In his case, observations were nested within an area, but there were very unequal numbers of ...]]></description>
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		<slash:comments>0</slash:comments>
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		<item>
		<title>One outlier and you&#8217;re out: Influential data and racial prejudice</title>
		<link>http://www.rensenieuwenhuis.nl/one-outlier-and-youre-out-influential-data-and-racial-prejudice/</link>
		<comments>http://www.rensenieuwenhuis.nl/one-outlier-and-youre-out-influential-data-and-racial-prejudice/#comments</comments>
		<pubDate>Tue, 16 Jun 2009 11:00:54 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[IAT]]></category>
		<category><![CDATA[influential data]]></category>
		<category><![CDATA[outlier]]></category>
		<category><![CDATA[prejudice]]></category>
		<category><![CDATA[psychology]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1008</guid>
		<description><![CDATA[Currently preparing a presentation on analyzing influential data in mixed effects models myself, my eye fell on an article in which important claims on racial prejudice were refuted. An important aspect of the criticism on existing work, is that in ...]]></description>
		<wfw:commentRss>http://www.rensenieuwenhuis.nl/one-outlier-and-youre-out-influential-data-and-racial-prejudice/feed/</wfw:commentRss>
		<slash:comments>4</slash:comments>
		</item>
		<item>
		<title>Introducing Influence.ME: Tools for detecting influential data in mixed models</title>
		<link>http://www.rensenieuwenhuis.nl/introducing-influenceme/</link>
		<comments>http://www.rensenieuwenhuis.nl/introducing-influenceme/#comments</comments>
		<pubDate>Wed, 29 Apr 2009 09:03:25 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[lmer]]></category>
		<category><![CDATA[mixed models]]></category>
		<category><![CDATA[R-Project]]></category>
		<category><![CDATA[regression]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=914</guid>
		<description><![CDATA[I&#8217;m highly excited to announce that influence.ME is now available. Influence.ME is a new software package for R, providing statistical tools for detecting influential data in mixed models. It has been developed by Rense Nieuwenhuis, Ben Pelzer, and Manfred te ...]]></description>
		<wfw:commentRss>http://www.rensenieuwenhuis.nl/introducing-influenceme/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>useR! 2009 acceptance: presenting influence.ME</title>
		<link>http://www.rensenieuwenhuis.nl/user-2009-acceptance-presenting-influenceme/</link>
		<comments>http://www.rensenieuwenhuis.nl/user-2009-acceptance-presenting-influenceme/#comments</comments>
		<pubDate>Thu, 23 Apr 2009 10:23:56 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[glmer]]></category>
		<category><![CDATA[influential data]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[lmer]]></category>
		<category><![CDATA[R]]></category>
		<category><![CDATA[R-Project]]></category>
		<category><![CDATA[useR! 2009]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=935</guid>
		<description><![CDATA[The organizing committee of the useR! 2009 conference just informed me, that my submission for presenting my extension package influence.ME, has been accepted! Influence.ME is a new R package that I&#8217;m currently developing, with the indispensable help of Ben Pelzer ...]]></description>
		<wfw:commentRss>http://www.rensenieuwenhuis.nl/user-2009-acceptance-presenting-influenceme/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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		<title>R-Sessions 32: Forward.lmer: Basic stepwise function for mixed effects in R</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-32/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-32/#comments</comments>
		<pubDate>Fri, 13 Feb 2009 10:59:03 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[forward]]></category>
		<category><![CDATA[hierarchical]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[mixed effects models]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[stepwise]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=897</guid>
		<description><![CDATA[Intended to be a customized solution, it may have grown to be a little more. forward.lmer is an early installment of a full stepwise function for mixed effects regression models in R-Project. I may put in some work to extend ...]]></description>
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		<slash:comments>7</slash:comments>
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