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Please check the latest news (change log) and keep this package updated.
DPI_dag()
and
plot.dpi.dag()
.DPI_curve()
for wrong (reverse) direction of DPI caused by the change of parameter
order of x
and y
in version 2025.10.dpi
parameter-object name conflict (internally) when saving
DPI()
results into a file
.This version contains breaking changes to function names and visualization methods.
DPI_dag()
: Directed acyclic graphs (DAGs) via DPI
exploratory analysis (causal discovery) for all significant partial
correlations.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.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.p_to_bf()
: Convert p values to pseudo
Bayes Factors (\(\text{PseudoBF}_{10}\)).cor_network()
to cor_net()
,
dag_network()
to BNs_dag()
, and
matrix_cor()
to cor_matrix()
.cor_net()
to return the exactly correct
p values of (partial) correlation coefficients.This version contains breaking changes to both algorithm and functionality.
DPI()
algorithm to limit \(\text{DPI} \in (-1, 1)\) and also
simplified its output information. \[
\begin{aligned}
\text{DPI}_{X \rightarrow Y}
& = \text{Direction}_{X \rightarrow Y} \cdot \text{Significance}_{X
\rightarrow Y} \\
& = \text{Delta}(R^2) \cdot \text{Sigmoid}(\frac{p}{\alpha}) \\
& = \left( R_{Y \sim X + Covs}^2 - R_{X \sim Y + Covs}^2 \right)
\cdot \left( 1 - \tanh \frac{p_{XY|Covs}}{2\alpha} \right) \\
& \in (-1, 1)
\end{aligned}
\]
data_random()
to sim_data()
with
enhanced functionality that supports data simulation from a multivariate
normal distribution, using MASS::mvrnorm()
.sim_data_exp()
: Simulate experiment-like data
with independent binary Xs.gc()
in DPI()
,
DPI_curve()
, and dag_network()
for memory
garbage collection.dag_network()
for
arranging multiple base-R-style plots using
aplot::plot_list()
.dag_network()
: Directed acyclic graphs (DAGs) via
causal Bayesian networks (BNs).cor_network()
: Correlation and partial
correlation networks.S3method.dpi
and S3method.network
and made
them as internal topics.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.