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Please check the latest news (change log) and keep this package updated.

DPI 2025.11

• Improved DPI_dag() and plot.dpi.dag().
• (Fixed in patch version 2025.10-1) Fixed DPI_curve() for wrong (reverse) direction of DPI caused by the change of parameter order of x and y in version 2025.10.
• (Fixed in patch version 2025.10-1) Fixed a bug caused by dpi parameter-object name conflict (internally) when saving DPI() results into a file.

DPI 2025.10

This version contains breaking changes to function names and visualization methods.

• Added DPI_dag(): Directed acyclic graphs (DAGs) via DPI exploratory analysis (causal discovery) for all significant partial correlations.
• Added bonf and pseudoBF parameters to DPI(), DPI_curve(), and DPI_dag().
  • bonf: Bonferroni correction to control for false positive rates among multiple pairwise DPI tests.
  • pseudoBF: Use normalized pseudo Bayes Factors sigmoid(log(PseudoBF10)) as the Significance score (0~1). Pseudo Bayes Factors are computed using the transformation rules proposed by Wagenmakers (2022) https://doi.org/10.31234/osf.io/egydq.
• Added S3 methods plot.cor.net(), plot.bns.dag(), and plot.dpi.dag() that can transform qgraph base-plot objects into ggplot objects for more stable and flexible visualization.
• Added p_to_bf(): Convert p values to pseudo Bayes Factors (\(\text{PseudoBF}_{10}\)).
• Renamed cor_network() to cor_net(), dag_network() to BNs_dag(), and matrix_cor() to cor_matrix().
• Fixed cor_net() to return the exactly correct p values of (partial) correlation coefficients.
• Improved output information in console and plot.

DPI 2025.9

This version contains breaking changes to both algorithm and functionality.

• Refined DPI() algorithm to limit \(\text{DPI} \in (-1, 1)\) and also simplified its output information. \[ \begin{aligned} \text{DPI}_{X \rightarrow Y} & = \text{Direction}_{X \rightarrow Y} \cdot \text{Significance}_{X \rightarrow Y} \\ & = \text{Delta}(R^2) \cdot \text{Sigmoid}(\frac{p}{\alpha}) \\ & = \left( R_{Y \sim X + Covs}^2 - R_{X \sim Y + Covs}^2 \right) \cdot \left( 1 - \tanh \frac{p_{XY|Covs}}{2\alpha} \right) \\ & \in (-1, 1) \end{aligned} \]
  • In an earlier version of algorithm, the strength score was computed as \(t_{\beta_{XY|Covs}}^2 = t_{r.partial_{XY|Covs}}^2 \in [0, +\infty)\). While this algorithm performs as well as the new \(\text{Sigmoid}(\frac{p}{\alpha})\) approach (e.g., both have low false positive and false negative rates), \(t^2\) has a major flaw that its values cannot converge to a limited range so that the final DPI values would be heavily determined by \(t^2\), which is not a desired attribute. In contrast, the new algorithm can make the significance score more likely to be an “on-off switch”, with values more likely approximating 0 or 1, thereby minimizing its impact on the interpretation of final DPI values.
• Renamed data_random() to sim_data() with enhanced functionality that supports data simulation from a multivariate normal distribution, using MASS::mvrnorm().
• Added sim_data_exp(): Simulate experiment-like data with independent binary Xs.
• Used gc() in DPI(), DPI_curve(), and dag_network() for memory garbage collection.
• Provided a better example in dag_network() for arranging multiple base-R-style plots using aplot::plot_list().

DPI 2025.8

• Added dag_network(): Directed acyclic graphs (DAGs) via causal Bayesian networks (BNs).
• Improved cor_network(): Correlation and partial correlation networks.
• Moved help pages of all S3 method functions to S3method.dpi and S3method.network and made them as internal topics.

DPI 2025.6

• CRAN package publication.
• Initial public release on GitHub.
• Developed core functions and package logo.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.