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Changed default behavior: entities whose values are entirely
NA for the selected variable are now removed from both axes
by default (drop.empty = TRUE). This avoids plotting empty
rows or columns caused by degenerate distributions or missing data. Set
drop.empty = FALSE to retain all entities and reproduce the
legacy full-grid layout. If fewer than two entities remain after
dropping empty rows/columns, plotgg() now stops with an
informative error rather than producing an empty or misleading
plot.
New argument sig.only: When enabled, values of the
selected variable are masked to NA wherever
p_value exceeds a specified cutoff, so only statistically
significant tiles are displayed.
sig.only = TRUE applies a default cutoff of
0.05sig.only = <numeric> uses a user-specified
p-value cutoff This option requires a p_value column in
data$all. When variable = "p_value", p-values
above the cutoff are masked to NA, allowing direct
visualization of significant p-values.Stable legend scaling under significance filtering: When
sig.only is used, legend limits and color scaling
("datarange" or "balanced") are computed from
the original, unfiltered data rather than the masked tiles. This
prevents legend collapse and ensures visual comparability between full
and significance-filtered plots.
Stricter input validation and supported-variable enforcement:
plotgg() now requires data$occur_mat to be
present and uses it as the authoritative source for entity ordering on
both axes. Only explicitly supported variables can be plotted; interval
and confidence-related columns are rejected, and unsupported or legacy
variable names are no longer accepted.
Revised color-scale logic: Color scales are now variable-aware rather than uniform:
alpha_mle uses a diverging color scale centered at zero
(null expectation)[0,1] (p_value,
jaccard, sorensen, simpson) use
fixed [0,1] limits under "balanced"
scalingp_value, color mapping is reversed so smaller
p-values appear more intenseobs_cooccur_X and exp_cooccur share a
common color scale to enable direct visual comparison When
col is not supplied, appropriate defaults are generated
automatically.Improved legend titles and diagnostics: Legend titles are now mapped to human-readable labels (including multi-line titles and mathematical notation where appropriate) rather than raw column names. Standardized legend notes are printed describing palette type, effective scaling mode, applied limits, and whether masking affects the plot.
Improved value labeling: A new argument text.col
controls the color of numeric labels printed on tiles. Values continue
to be printed automatically when the number of plotted entities is ≤ 20,
or when show.value = TRUE.
finches dataset now ships internally and can be loaded via
data(finches) without requiring
cooccur.affinity() for computing co-occurrence affinity metrics
and their intervalsdataprep() to turn abundance data into
presence/absenceCovrgPlot() to visualize coverage probability of
confidence intervalsplotgg() ggplot2 wrapper for affinity outputsML.Alpha(),
AlphInts())Package published on CRAN; available at https://cran.r-project.org/web/packages/CooccurrenceAffinity/index.html.
We are pleased to inform you that the interaction issue between our package and BiasedUrn v2.0.8 has been resolved in the latest version, BiasedUrn v2.0.9. We kindly request that you remove any previous versions of BiasedUrn and install v2.0.9 to ensure proper operation of the CooccurrenceAffinity package. We extend our heartfelt thanks to Agner Fox for promptly updating BiasedUrn and addressing these important issues.
After inspecting this issue https://github.com/kpmainali/CooccurrenceAffinity/issues/6, we have discovered that the recent revision of our dependency package BiasedUrn is causing R to crash occasionally while running CooccurrenceAffinity. We are actively working to resolve this issue from within our package. In the meantime, we strongly advise against updating BiasedUrn to version 2.0.8. If you have already upgraded, we recommend removing this version and installing the prior version 1.07 as a temporary solution. We will provide updates as soon as the issue is resolved.
Completed writing the package manuscript serving as the vignette. Preprint available: https://www.biorxiv.org/content/10.1101/2022.11.01.514801v1
CovrgPlot() revised to generate multipanel plots.
Added function minmaxAlpha.pFNCH() to address range
inconsistency in BiasedUrn::pFNCHypergeo() for extreme examples. Without
this function, BiasedUrn::pFNCHHypergeo() returns inconsistency message
for extreme examples like: AlphInts(20,c(204,269,2016), lev=0.9,
scal=10). This problem is solved within our package by restricting the
range of allowed alpha to the computed (alphmin, alphmax) range.
Fixed a bug in ML.Alpha() affecting computation of the
upper bound of the alpha MLE. Users before this date should
reinstall.
Substantial revision of existing functions’ descriptions, examples, and arguments. Added new functions for binary matrix analysis and plotting.
This package was released with a basic set of functions in Dec 2021.
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.
Health stats visible at Monitor.