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cor_matrix() now draws column i
from distribution i (previously random with
replacement, so columns could repeat or omit inputs); columns are now
labelled with distribution names. Results will differ.[-1, 1] range,
unit diagonal) are now shared across mcs(),
smm(), and sensitivity().sensitivity() warns when an index is negative, which
can happen under negative correlation.cost_pdf() no longer requires
sum(risk_probs) <= 1.cost_post_pdf() gained risk_probs, so
unobserved (NA) risks are drawn from their prior instead of
treated as absent; warns if omitted with NAs present.predict_sigmoidal() now errors on a
model_type/fit mismatch instead of returning
NA confidence bounds.mcs() fixes the Cholesky decomposition for
correlated tasks — target correlations and marginal means were
previously distorted. Correlated results will differ; independent tasks
are unaffected.pra_chat(), pra_app(), slash commands, and the
RAG functions; use pra_mcp_server() and the bundled Agent
Skills (inst/skills/) instead.ragnar, shiny,
shinychat, bslib, vitals,
mockr; added corrplot, igraph,
networkD3, devtools, remotes,
mcptools.prob_net(), prob_net_sim(),
prob_net_learn(), prob_net_update().links are now load-bearing — edges must match the
distributions’ dependencies, nodes must be topologically ordered, and
adjacency_matrix is now directed. Networks that were
already self-consistent are unaffected.risk_prob()/risk_post_prob() combine multiple
causes with a noisy-OR, keeping results in [0, 1].
Single-cause results are unchanged.sensitivity() indices now reflect each task’s actual
variance contribution instead of always returning 1.0 for independent
tasks.building_project example dataset.inst/paper/pra-jss/).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.