lifecycle::badge() in Roxygen docs
because of CRAN Note..data$ to simply "" in all
functions to fix test warnings from dplyr.rhub GitHub Action workflow to check CRAN
compatibility more fully.nc_estimate_* function output the full model
list as an attribute, that is really only necessary for those interested
in the underlying models used for classifying the effectsnc_estimate_*_links() functions to
set thresholds for classifying links (#157)as_edge_tbl()
(#142)nc_classify_effects() and
nc_filter_estimates(), merged them into the two main
estimation functions insteadlm and glm
models were removed for improving computing speed (they slowed things
down quite a bit)nc_estimate_* functions output@seealsolm and glm models, model summary
statistics are added (#88).nc_plot_network()
(#89, #110).nc_adjacency_graph(),
nc_adjacency_matrix(), and
nc_partial_corr_matrix() to help create the weights for the
network plot. (Issue #80, PR #89).pcor() (#125, #131).nc_filter_estimates() (#109).nc_standardize() that prevented the
ability to use the .regressed_on. argument to extract
residuals (#108).nc_standardize() function to standardize the
metabolic variables (#73).matches() or
starts_with() (#73).net_coupler_out(),
getExp.coef.permetabolite(), and
getExp.coef.out() (#59)nc_exposure_estimates()nc_outcome_estimates() function. Because of this
streamlining, the code is much faster and with the move to use MuMIn we
can remove our dependency on rJava via glmulti.nc_create_network() function so that only the
graph skeleton is output (#55).nc_create_network().nc_create_network() and the
outcome estimation functions. Travis and code coverage were added as
well.nc_make_network() to
nc_create_network() and moved into own file.nc_make_network() code and moved into
another file.NEWS.md file to track changes to the
package.
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