Checking the Installation | Prepare a dataset using prepare_data.R | Add the Dataset to the Repository | Add some demo analyses | Reproduce the Project

Optional step

Steps Involved | Check 'Git' and 'GitHub' | Optionally: Connect Local to Remote ('GitHub') Repository | Snapshot renv Dependencies | Pushing These Changes to the Remote Repository | Check Your 'GitHub' Repository | Login to 'Zenodo' | Authorize 'GitHub' to connect with 'Zenodo' | Select the Repository to Archive | Optional: Check repository settings | Create a New Release | Verify on 'Zenodo' | Optionally: Updating Meta-Data | Verifying That 'Zenodo' Mints a DOI for Your Project | CONGRATULATIONS! | Checklist for citing your project

Install 'RStudio' and 'R' | Install R-package dependencies | Verifying WORCS Installation | Obtaining the project repository | Open the project in 'RStudio' | Restore the package dependencies | Open the project entry point | Reproduce the analyses | No access to original data

WORCS: Steps to follow for a project | Phase 1: Study design | Phase 2: Writing and analysis | Phase 3: Submission and publication | Notes for cautious researchers | Sample WORCS projects

Adding endpoints | Integration Tests as Endpoints | Adding Integration Tests | Reproducing a Project | Checking reproducibility | Updating endpoints | Automating Reproducibility | Automating Endpoint Checks

GitLab | Setup steps (do this only once) | Connect new worcs project to 'GitLab' | Bitbucket | Connect new worcs project to 'Bitbucket'

Generating Data from a Structural Equation Model | Illustrating the Problem | Adding a Custom Dataset

Defining a Pipeline | Using targets Markdown

Tutorial Requirements | Meta-Analysis in R | Using Your Own Data | Using Different Demo Data | Fixed Effect Model | Fixed-effects Model with rma | Random-Effects-Model | Random-Effects Model with rma | Meta-Regression | Testing Moderators' Significance | Assessing Meta-Regression Model Fit | Meta-regression in R | Continuous Predictors | Multiple Meta-Regression | Optional: Pitfalls of Meta-Regression | Multicollinearity | Overfitting | Penalized Meta-Regression | Two-level model | Two-level model with the lasso prior | Assessing convergence and interpreting the results | Optional: Three-level model with Horseshoe Prior | More Information | Random Forest Meta-Regression | Check Convergence | Model Tuning | Interpreting the Results | Bayesian Evidence Synthesis | Repeating the RMA | Product Bayes Factor | Credit | References

Tutorial example | Tuning parameters | Inspecting the results | More information

Packages | Data | Impute missings | Moderators | Two-level model | Two-level model with the lasso prior | Assessing convergence and interpreting the results | Two-level model with the horseshoe prior | Three-level model | Standardization

Acknowledgment | Introduction | Usage | Arguments | Using bain with a lm or t_test object | Using bain with a lavaan object | Using bain with a named vector | The specification of hypotheses | Output from an analysis with bain | Examples

Purpose | Using mclust | Just one fit | Using MPlus | Many fits

Background on Latent Profile Analysis (LPA) | Description of the goals of tidyLPA | Software approach to carrying out LPA: Interface to mclust (and to MPlus) | Example | Installation | Mclust | Mplus | Comparing a wide range of solutions | Passing additional arguments | More information on model specifications | Model specification | 1. Equal variances, and covariances fixed to 0 (model 1) | 2. Varying variances and covariances fixed to 0 (model 2) | 3. Equal variances and equal covariances (model 3) | 4. Varying means, varying variances, and equal covariances (model 4) | 5. Varying means, equal variances, and varying covariances (model 5) | 6. Varying variances and varying covariances (model 6) | Other functionality | Getting estimates | Getting data | Getting fit statistics | Acknowledgments

Introduction | Reference | Usage | Arguments | Output | Example analyses | Bayesian t testing | Analysis of variance | Testing independent group variances | Logistic regression | Correlation analysis | Univariate/Multivariate multiple regression | Running BF on a named vector