ggseg - Plotting Tool for Brain Atlases

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>.

Last updated 2 years ago

11.52 score 215 stars 14 packages 538 scripts 687 downloads

semtree - Recursive Partitioning for Structural Equation Models

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>.

Last updated 2 months ago

bigdatadecision-treeforestmultivariaterandomforestrecursive-partitioningsemstatistical-modelingstructural-equation-modelingstructural-equation-models

8.62 score 13 stars 67 scripts 521 downloads

ggseg3d - Tri-Surface Mesh Plots for Brain Atlases

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>.

Last updated 1 years ago

7.60 score 34 stars 13 packages 149 scripts 318 downloads

regsem - Regularized Structural Equation Modeling

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).

Last updated 1 years ago

6.63 score 14 stars 77 scripts 647 downloads

metagam - Meta-Analysis of Generalized Additive Models

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>.

Last updated 5 months ago

5.85 score 10 stars 14 scripts 285 downloads

efast - Exploratory Factor Analysis with Structured Residuals

Create and estimate EFA and EFA with structured residuals (EFAST) models using structural equation modeling.

Last updated 9 months ago

2.30 score 2 stars 5 scripts

gbhs - Lifebrain Global Brain Health Survey Data

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.

Last updated 2 years ago

2.78 score 1 stars 12 scripts