lifebrain r-universe repositoryhttps://lifebrain.r-universe.devPackage updated in lifebraincranlike-server 0.16.70https://github.com/lifebrain.png?size=400lifebrain r-universe repositoryhttps://lifebrain.r-universe.devSat, 25 Nov 2023 07:59:44 GMT[lifebrain] semtree 0.9.19andy@brandmaier.de (Andreas M. Brandmaier)SEM Trees and SEM Forests -- an extension of model-based
decision trees and forests to Structural Equation Models (SEM).
SEM trees hierarchically split empirical data into homogeneous
groups each sharing similar data patterns with respect to a SEM
by recursively selecting optimal predictors of these
differences. SEM forests are an extension of SEM trees. They
are ensembles of SEM trees each built on a random sample of the
original data. By aggregating over a forest, we obtain measures
of variable importance that are more robust than measures from
single trees. A description of the method was published by
Brandmaier, von Oertzen, McArdle, & Lindenberger (2013)
<doi:10.1037/a0030001> and Arnold, Voelkle, & Brandmaier (2020)
<doi:10.3389/fpsyg.2020.564403>.https://github.com/r-universe/lifebrain/actions/runs/6987951016Sat, 25 Nov 2023 07:59:44 GMTsemtree0.9.19successhttps://lifebrain.r-universe.devhttps://github.com/brandmaier/semtreeconstraints.Rmdconstraints.htmlConstraints in semtree2019-09-17 06:09:512023-11-24 08:36:30semforest-focus.Rmdsemforest-focus.htmlFocus parameters in SEM forests2020-04-23 11:10:252023-11-24 08:36:30getting-started.Rmdgetting-started.htmlGetting Started with the semtree package2019-09-12 08:45:262023-11-23 20:34:33score-based-tests.Rmdscore-based-tests.htmlScore-based Tests2020-04-17 05:31:522023-11-24 08:36:30forests.Rmdforests.htmlSEM Forests2020-11-05 08:12:222023-11-24 10:57:59[lifebrain] ggseg3d 1.6.3a.m.mowinckel@psykologi.uio.no (Athanasia Mo Mowinckel)Mainly contains a plotting function ggseg3d(), and data of
two standard brain atlases (Desikan-Killiany and aseg). By far,
the largest bit of the package is the data for each of the
atlases. The functions and data enable users to plot
tri-surface mesh plots of brain atlases, and customise these by
projecting colours onto the brain segments based on values in
their own data sets. Functions are wrappers for 'plotly'.
Mowinckel & Vidal-Piñeiro (2020)
<doi:10.1177/2515245920928009>.https://github.com/r-universe/lifebrain/actions/runs/6953551833Mon, 23 Oct 2023 09:01:59 GMTggseg3d1.6.3successhttps://lifebrain.r-universe.devhttps://github.com/ggseg/ggseg3dFAILURE: [lifebrain] ICED 0.0.1sam.parsons@radboudumc.nl (Sam Parsons)https://github.com/r-universe/lifebrain/actions/runs/6911856278Fri, 30 Jun 2023 12:59:19 GMTICED0.0.1https://github.com/sdparsons/ICED[lifebrain] regsem 1.9.5rcjacobuc@gmail.com (Ross Jacobucci)Uses both ridge and lasso penalties (and extensions) to
penalize specific parameters in structural equation models. The
package offers additional cost functions, cross validation, and
other extensions beyond traditional structural equation models.
Also contains a function to perform exploratory mediation
(XMed).https://github.com/r-universe/lifebrain/actions/runs/7014775459Thu, 01 Jun 2023 17:12:17 GMTregsem1.9.5successhttps://lifebrain.r-universe.devhttps://github.com/Rjacobucci/regsemshort_intro.Rmdshort_intro.htmlOverview2018-02-17 01:36:492021-03-22 20:09:55[lifebrain] metagam 0.4.0oystein.sorensen@psykologi.uio.no (Oystein Sorensen)Meta-analysis of generalized additive
models and generalized additive mixed models. A typical use case is
when data cannot be shared across locations, and an overall meta-analytic
fit is sought. 'metagam' provides functionality for removing individual
participant data from models computed using the 'mgcv' and 'gamm4' packages such
that the model objects can be shared without exposing individual data.
Furthermore, methods for meta-analysing these fits are provided. The implemented
methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>,
extending previous works by Schwartz and Zanobetti (2000)
and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.https://github.com/r-universe/lifebrain/actions/runs/6716154470Fri, 05 May 2023 18:06:03 GMTmetagam0.4.0successhttps://lifebrain.r-universe.devhttps://github.com/Lifebrain/metagamdominance.Rmddominance.htmlDominance Plots2020-01-30 21:29:452022-01-14 10:13:39heterogeneity.Rmdheterogeneity.htmlHeterogeneity Plots2020-01-30 09:57:572022-01-14 10:13:39introduction.Rmdintroduction.htmlIntroduction2020-01-30 09:57:572022-01-18 11:48:39multivariate.Rmdmultivariate.htmlMultivariate Smooth Terms2020-02-05 11:00:332022-01-14 10:13:39pvals.Rmdpvals.htmlSimultaneous confidence intervals and p-values2021-11-12 09:02:482022-01-16 10:04:39[lifebrain] ggseg 1.6.6a.m.mowinckel@psykologi.uio.no (Athanasia Mo Mowinckel)Contains 'ggplot2' geom for plotting brain atlases using
simple features. The largest component of the package is the
data for the two built-in atlases. Mowinckel & Vidal-Piñeiro
(2020) <doi:10.1177/2515245920928009>.https://github.com/r-universe/lifebrain/actions/runs/6993825677Fri, 31 Mar 2023 09:00:28 GMTggseg1.6.6successhttps://lifebrain.r-universe.devhttps://github.com/ggseg/ggsegexternalData.RmdexternalData.htmlAdding external data to ggseg plotting2018-09-03 20:51:252021-08-13 10:31:08freesurfer_files.Rmdfreesurfer_files.htmlRead in files from FreeSurfer2020-03-04 13:16:272021-08-10 08:34:58ggseg.Rmdggseg.htmlUsing geom_brain2018-08-30 08:38:292022-06-13 06:28:31[lifebrain] efast 0.6.3e.vankesteren1@uu.nl (Erik-Jan van Kesteren)Create and estimate EFA and EFA with structured residuals
(EFAST) models using structural equation modeling.https://github.com/r-universe/lifebrain/actions/runs/7042714619Thu, 30 Mar 2023 20:25:29 GMTefast0.6.3successhttps://lifebrain.r-universe.devhttps://github.com/vankesteren/efast[lifebrain] gbhs 0.0.1.9000a.m.mowinckel@psykologi.uio.no (Athanasia Mo Mowinckel)Between June 2019 and August 2020, Lifebrain conducted the
Global Brain Health Survey to collect data on people’s
perceptions of brain health and willingness to take care of
their brain by adopting new lifestyles. The survey was
conducted online and translated into 14 languages to reach as
many people as possible. In total, it collected 27,590
responses from people in 81 countries. This package contains
code and data from this survey.https://github.com/r-universe/lifebrain/actions/runs/6993825736Wed, 28 Sep 2022 08:50:52 GMTgbhs0.0.1.9000successhttps://lifebrain.r-universe.devhttps://github.com/lifebrain/gbhsgbhs.Rmdgbhs.htmlgbhs2022-09-16 15:06:012022-09-27 16:24:53FAILURE: [lifebrain] ggsegExtra 1.5.33.004a.m.mowinckel@psykologi.uio.no (Athanasia Mo Mowinckel)https://github.com/r-universe/lifebrain/actions/runs/6766365777Mon, 02 May 2022 12:52:33 GMTggsegExtra1.5.33.004https://github.com/ggseg/ggsegExtra