No packages match

VARcheck - Visual Diagnostic Checks for Vector Autoregressive Models

Provides model-agnostic visual diagnostics for vector autoregressive (VAR) models. Given empirical data, model predictions, residuals, and optionally simulated data, the package assembles a multi-panel diagnostic grid: empirical vs. predicted time series, residual inspection, residuals vs. predictions scatter, and posterior predictive style checks via simulated trajectories. Output is a 'patchwork' object composed of 'ggplot2' plots, allowing further customisation via standard 'ggplot2' theme calls. Follows the approach described in Haslbeck et al. (2026) <doi:10.31234/osf.io/k6uz4_v3>.

Last updated

4.60 score 1 stars 4 scripts 199 downloads

openesm - Access the Open Experience Sampling Method Database

Provides programmatic access to the Open Experience Sampling Method ('openESM') database (<https://openesmdata.org>), a collection of harmonized experience sampling datasets. The package enables researchers to discover, download, and work with the datasets while ensuring proper citation and license compliance.

Last updated

3.85 score 5 scripts 306 downloads

tsnet - Fitting, Comparing, and Visualizing Networks Based on Time Series Data

Fit, compare, and visualize Bayesian graphical vector autoregressive (GVAR) network models using 'Stan'. These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.

Last updated

cpp

3.20 score 2 stars 16 scripts 227 downloads

VARcheck - Visual Diagnostic Checks for Vector Autoregressive Models

Provides model-agnostic visual diagnostics for vector autoregressive (VAR) models. Given empirical data, model predictions, residuals, and optionally simulated data, the package assembles a multi-panel diagnostic grid: empirical vs. predicted time series, residual inspection, residuals vs. predictions scatter, and posterior predictive style checks via simulated trajectories. Output is a 'patchwork' object composed of 'ggplot2' plots, allowing further customisation via standard 'ggplot2' theme calls. Follows the approach described in Haslbeck et al. (2026) <doi:10.31234/osf.io/k6uz4_v3>.

Last updated

3.00 score 4 scripts