cjvanlissa r-universe repositoryhttps://cjvanlissa.r-universe.devPackage updated in cjvanlissacranlike-server 0.11.53https://github.com/cjvanlissa.png?size=400cjvanlissa r-universe repositoryhttps://cjvanlissa.r-universe.devThu, 08 Sep 2022 11:11:10 GMT[cjvanlissa] tidySEM 0.2.4.2c.j.vanlissa@uu.nl (Caspar J. van Lissa)A tidy workflow for generating, estimating, reporting,
and plotting structural equation models using 'lavaan', 'OpenMx', or
'Mplus'. Throughout this workflow, elements of syntax, results, and graphs
are represented as 'tidy' data, making them easy to customize.https://github.com/r-universe/cjvanlissa/actions/runs/3014961550Thu, 08 Sep 2022 11:11:10 GMTtidySEM0.2.4.2successhttps://cjvanlissa.r-universe.devhttps://github.com/cjvanlissa/tidySEMGenerating_syntax.RmdGenerating_syntax.htmlGenerating syntax for structural equation models2019-12-26 07:49:482022-02-06 07:27:18Plotting_graphs.RmdPlotting_graphs.htmlPlotting graphs for structural equation models2019-12-15 09:12:082021-04-19 12:12:41sem_graph.Rmdsem_graph.htmlSEM graphing conventions2020-06-01 14:48:522021-11-16 14:41:10Tabulating_results.RmdTabulating_results.htmlTabulating results from structural equation models2020-01-22 15:47:122022-07-29 08:12:39[cjvanlissa] worcs 0.1.10c.j.vanlissa@tilburguniversity.edu (Caspar J. Van Lissa)Create reproducible and transparent research projects in 'R'.
This package is based on the Workflow for Open
Reproducible Code in Science (WORCS), a step-by-step procedure based on best
practices for
Open Science. It includes an 'RStudio' project template, several
convenience functions, and all dependencies required to make your project
reproducible and transparent. WORCS is explained in the tutorial paper
by Van Lissa, Brandmaier, Brinkman, Lamprecht, Struiksma, & Vreede (2021).
<doi:10.3233/DS-210031>.https://github.com/r-universe/cjvanlissa/actions/runs/2983518905Sat, 03 Sep 2022 06:38:08 GMTworcs0.1.10successhttps://cjvanlissa.r-universe.devhttps://github.com/cjvanlissa/worcscitation.Rmdcitation.htmlCiting references in worcs2020-05-25 13:14:332021-02-02 11:26:08git_cloud.Rmdgit_cloud.htmlConnecting to 'Git' remote repositories2020-05-18 18:43:402022-07-16 12:35:26reproduce.Rmdreproduce.htmlReproducing a WORCS project2020-11-20 07:34:212022-07-16 18:05:08setup.Rmdsetup.htmlSetting up your computer for WORCS2020-02-04 14:46:552022-08-05 13:09:06setup-docker.Rmdsetup-docker.htmlSetting up your computer for WORCS - Docker-edition2020-11-17 09:15:202021-02-02 11:26:08workflow.Rmdworkflow.htmlThe WORCS workflow, version 0.1.62020-05-20 14:12:042022-07-19 10:56:27[cjvanlissa] pema 0.1.2c.j.vanlissa@tilburguniversity.edu (Caspar J van Lissa)Conduct penalized meta-analysis, see Van Lissa & Van Erp (2021).
<doi:10.31234/osf.io/6phs5>. In meta-analysis, there are
often between-study differences. These can be coded as moderator variables,
and controlled for using meta-regression. However, if the number of
moderators is large relative to the number of studies, such an analysis may
be overfit. Penalized meta-regression is useful in these cases, because
it shrinks the regression slopes of irrelevant moderators towards zero.https://github.com/r-universe/cjvanlissa/actions/runs/3059835708Sun, 17 Jul 2022 13:51:06 GMTpema0.1.2successhttps://cjvanlissa.r-universe.devhttps://github.com/cjvanlissa/pemausing-brma.Rmdusing-brma.htmlConducting a Bayesian Regularized Meta-analysis2022-04-07 12:25:582022-07-16 17:15:00[cjvanlissa] bain 0.2.8c.j.vanlissa@uu.nl (Caspar J van Lissa)Computes approximated adjusted fractional Bayes factors for
equality, inequality, and about equality constrained hypotheses.
For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu,
(2019) <doi:10.31234/osf.io/v3shc>. For applications in structural equation
modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, &
Hoijtink, (2021) <doi:10.1080/10705511.2020.1745644>. For the statistical
underpinnings, see Gu, Mulder, and Hoijtink (2018) <doi:10.1111/bmsp.12110>;
Hoijtink, Gu, & Mulder, J. (2019) <doi:10.1111/bmsp.12145>; Hoijtink, Gu,
Mulder, & Rosseel, (2019) <doi:10.31234/osf.io/q6h5w>.https://github.com/r-universe/cjvanlissa/actions/runs/3125462268Mon, 27 Jun 2022 14:23:37 GMTbain0.2.8successhttps://cjvanlissa.r-universe.devhttps://github.com/cjvanlissa/bainIntroduction_to_bain.RmdIntroduction_to_bain.htmlIntroduction to bain2019-01-14 18:56:532021-11-18 06:33:57[cjvanlissa] tidyLPA 2.0.0jmrosenberg@utk.edu (Joshua M Rosenberg)Easily carry out latent profile analysis ("LPA"), determine the
correct number of classes based on best practices, and tabulate and plot the
results. Provides functionality to estimate commonly-specified models with
free means, variances, and covariances for each profile. Follows a tidy
approach, in that output is in the form of a data frame that can
subsequently be computed on. Models can be estimated using the free open
source 'R' packages 'Mclust' and 'OpenMx', or using the commercial program
'MPlus', via the 'MplusAutomation' package.https://github.com/r-universe/cjvanlissa/actions/runs/3110813606Tue, 22 Feb 2022 15:53:38 GMTtidyLPA2.0.0successhttps://cjvanlissa.r-universe.devhttps://github.com/cjvanlissa/tidyLPAbenchmarking-mclust-and-mplus.Rmdbenchmarking-mclust-and-mplus.htmlBenchmarking mclust and MPlus2019-07-19 13:22:362021-06-03 14:21:07Introduction_to_tidyLPA.RmdIntroduction_to_tidyLPA.htmlIntroduction to tidyLPA2017-10-28 05:39:232021-06-03 14:21:07[cjvanlissa] gorica 0.1.2c.j.vanlissa@uu.nl (Caspar J. van Lissa)Implements the generalized order-restricted information criterion
approximation (GORICA), an AIC-like information criterion that can be
utilized to evaluate informative hypotheses specifying directional
relationships between model parameters in terms of (in)equality
constraints (see Altinisik, Van Lissa, Hoijtink, Oldehinkel, & Kuiper,
2021), <doi:10.31234/osf.io/t3c8g>. The GORICA is applicable not only to
normal linear models, but also to generalized linear models (GLMs),
generalized linear mixed models (GLMMs), structural equation models
(SEMs), and contingency tables. For contingency tables, restrictions on cell
probabilities can be non-linear.https://github.com/r-universe/cjvanlissa/actions/runs/3088187004Fri, 17 Dec 2021 19:12:49 GMTgorica0.1.2successhttps://cjvanlissa.r-universe.devhttps://github.com/cjvanlissa/gorica[cjvanlissa] BFpack 0.3.2j.mulder3@tilburguniversity.edu (Joris Mulder)Implementation of various default Bayes factors
for testing statistical hypotheses. The package is
intended for applied quantitative researchers in the
social and behavioral sciences, medical research,
and related fields. The Bayes factor tests can be
executed for statistical models such as
univariate and multivariate normal linear models,
generalized linear models, special cases of
linear mixed models, survival models, relational
event models. Parameters that can be tested are
location parameters (e.g., group means, regression coefficients),
variances (e.g., group variances), and measures of
association (e.g,. bivariate correlations), among others.
The statistical underpinnings are
described in
Mulder, Hoijtink, and Xin (2019) <arXiv:1904.00679>,
Mulder and Gelissen (2019) <arXiv:1807.05819>,
Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>,
Mulder and Fox (2019) <DOI:10.1214/18-BA1115>,
Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>,
Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder <DOI:10.1037/met0000116>,
Hoijtink, Mulder, van Lissa, and Gu, (2018) <DOI:10.31234/osf.io/v3shc>,
Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bmsp.12110>,
Hoijtink, Gu, and Mulder, (2018) <DOI:10.1111/bmsp.12145>, and
Hoijtink, Gu, Mulder, and Rosseel, (2018) <DOI:10.1037/met0000187>.https://github.com/r-universe/cjvanlissa/actions/runs/3110749277Wed, 24 Feb 2021 07:39:58 GMTBFpack0.3.2successhttps://cjvanlissa.r-universe.devhttps://github.com/cjvanlissa/BFpackvignette_BFpack.Rmdvignette_BFpack.htmlBFpack_introduction2020-11-29 08:24:472021-02-04 06:51:34[cjvanlissa] metaforest 0.1.4.8c.j.vanlissa@gmail.com (Caspar J. van Lissa)Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates, even with as little as 20 studies (Van Lissa, in preparation). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.https://github.com/r-universe/cjvanlissa/actions/runs/2941810219Thu, 04 Feb 2021 14:25:25 GMTmetaforest0.1.4.8successhttps://cjvanlissa.r-universe.devhttps://github.com/cjvanlissa/metaforestIntroduction_to_metaforest.RmdIntroduction_to_metaforest.htmlIntroduction to metaforest2019-01-22 10:12:402020-01-04 09:11:21