tag:crantastic.org,2005:/authors/3254Latest activity for Daniel W. Heck2019-12-05T09:44:23Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/956032019-12-05T09:44:23Z2019-12-05T09:44:23ZTreeBUGS was upgraded to version 1.4.4<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/90862">1.4.4</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/955982019-12-05T09:22:33Z2019-12-05T09:22:33ZMCMCprecision was upgraded to version 0.4.0<a href="/packages/MCMCprecision">MCMCprecision</a> was <span class="action">upgraded</span> to version <a href="/packages/MCMCprecision/versions/90857">0.4.0</a><br /><h3>Package description:</h3><p>Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/955762019-12-04T22:43:32Z2019-12-04T22:43:32ZRRreg was upgraded to version 0.7.1<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/90835">0.7.1</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369, <doi:10.2307/2283137>). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 129, <doi:10.18637/jss.v085.i02>). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724732, <doi:10.3758/s13428-016-0729-x>).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/868502019-04-04T09:23:22Z2019-04-04T09:23:22ZTreeBUGS was upgraded to version 1.4.3<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/82558">1.4.3</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/827742018-12-18T17:23:11Z2018-12-18T17:23:11ZTreeBUGS was upgraded to version 1.4.1<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/78754">1.4.1</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/790262018-08-27T15:22:47Z2018-08-27T15:22:47ZRRreg was upgraded to version 0.7.0<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/75369">0.7.0</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369, <doi:10.2307/2283137>). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 129, <doi:10.18637/jss.v085.i02>). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724732, <doi:10.3758/s13428-016-0729-x>).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/787922018-08-20T11:15:22Z2018-08-20T11:15:22Zcrantastic_production tagged RRreg with OfficialStatistics<a href="/users/146">crantastic_production</a> <span class="action">tagged</span> <a href="/packages/RRreg">RRreg</a> with <a href="/task_views/OfficialStatistics">OfficialStatistics</a>crantastic_productiontag:crantastic.org,2005:TimelineEvent/782632018-08-10T16:55:45Z2018-08-10T16:55:45ZRRreg was upgraded to version 0.6.9<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/74637">0.6.9</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369, <doi:10.2307/2283137>). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 129, <doi:10.18637/jss.v085.i02>). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724732, <doi:10.3758/s13428-016-0729-x>).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/780642018-08-10T16:49:49Z2018-08-10T16:49:49ZMCMCprecision was upgraded to version 0.3.9<a href="/packages/MCMCprecision">MCMCprecision</a> was <span class="action">upgraded</span> to version <a href="/packages/MCMCprecision/versions/74438">0.3.9</a><br /><h3>Package description:</h3><p>Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2018, Statistics & Computing) <doi:10.1007/s11222-018-9828-0> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/759502018-06-04T18:42:30Z2018-06-04T18:42:30ZRRreg was upgraded to version 0.6.8<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/72475">0.6.8</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369, <doi:10.2307/2283137>). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 129, <doi:10.18637/jss.v085.i02>). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724732, <doi:10.3758/s13428-016-0729-x>).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/750172018-05-15T12:42:48Z2018-05-15T12:42:48ZTreeBUGS was upgraded to version 1.4.0<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/71619">1.4.0</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/734892018-04-08T17:21:56Z2018-04-08T17:21:56ZMCMCprecision was upgraded to version 0.3.8<a href="/packages/MCMCprecision">MCMCprecision</a> was <span class="action">upgraded</span> to version <a href="/packages/MCMCprecision/versions/70155">0.3.8</a><br /><h3>Package description:</h3><p>Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2017) <arXiv:1703.10364> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/733192018-04-04T11:42:45Z2018-04-04T11:42:45ZRRreg was upgraded to version 0.6.7<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/69998">0.6.7</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369). <doi:10.2307/2283137> Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), as predictors in a linear regression, or as dependent variable in a beta-binomial ANOVA. For simulation and bootstrap purposes, RR data can be generated according to several models.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/731382018-03-29T11:42:34Z2018-03-29T11:42:34ZRRreg was upgraded to version 0.6.6<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/69820">0.6.6</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369). <doi:10.2307/2283137> Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), as predictors in a linear regression, or as dependent variable in a beta-binomial ANOVA. For simulation and bootstrap purposes, RR data can be generated according to several models.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/718742018-02-23T10:02:56Z2018-02-23T10:02:56ZTreeBUGS was upgraded to version 1.3.1<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/68649">1.3.1</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/714562018-02-11T15:22:18Z2018-02-11T15:22:18ZRRreg was upgraded to version 0.6.5<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/68249">0.6.5</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369). <doi:10.2307/2283137> Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), as predictors in a linear regression, or as dependent variable in a beta-binomial ANOVA. For simulation and bootstrap purposes, RR data can be generated according to several models.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/712492018-02-05T16:02:43Z2018-02-05T16:02:43ZTreeBUGS was upgraded to version 1.3.0<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/68045">1.3.0</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/706072018-01-19T18:42:55Z2018-01-19T18:42:55ZTreeBUGS was upgraded to version 1.2.0<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/67435">1.2.0</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/700542018-01-05T17:21:57Z2018-01-05T17:21:57ZRRreg was upgraded to version 0.6.4<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/66933">0.6.4</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369). <doi:10.2307/2283137> Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), as predictors in a linear regression, or as dependent variable in a beta-binomial ANOVA. For simulation and bootstrap purposes, RR data can be generated according to several models.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/657542017-08-23T17:22:22Z2017-08-23T17:22:22ZTreeBUGS was upgraded to version 1.1.1<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/62881">1.1.1</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/651692017-08-04T14:01:35Z2017-08-04T14:01:35ZMCMCprecision was upgraded to version 0.3.7<a href="/packages/MCMCprecision">MCMCprecision</a> was <span class="action">upgraded</span> to version <a href="/packages/MCMCprecision/versions/62331">0.3.7</a><br /><h3>Package description:</h3><p>Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2017) <arXiv:1703.10364> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/610312017-04-03T06:21:12Z2017-04-03T06:21:12ZMCMCprecision was released<a href="/packages/MCMCprecision">MCMCprecision</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/609872017-04-01T16:01:54Z2017-04-01T16:01:54ZTreeBUGS was upgraded to version 1.1.0<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/58373">1.1.0</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/601422017-03-13T18:12:54Z2017-03-13T18:12:54ZRRreg was upgraded to version 0.6.2<a href="/packages/RRreg">RRreg</a> was <span class="action">upgraded</span> to version <a href="/packages/RRreg/versions/57577">0.6.2</a><br /><h3>Package description:</h3><p>Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 6369). <doi:10.2307/2283137> Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), as predictors in a linear regression, or as dependent variable in a beta-binomial ANOVA. For simulation and bootstrap purposes, RR data can be generated according to several models.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/583792017-01-09T16:51:47Z2017-01-09T16:51:47ZTreeBUGS was upgraded to version 1.0.1<a href="/packages/TreeBUGS">TreeBUGS</a> was <span class="action">upgraded</span> to version <a href="/packages/TreeBUGS/versions/55901">1.0.1</a><br /><h3>Package description:</h3><p>User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions.</p>crantastic.org