Nothing
group_regulation_long
.matrix
as the internal data format used by TNA models for performance improvements across all functions.prepare_data()
that resulted in excessive console output.import_data()
to read wide format sequence data into long format.plot_frequencies()
that can be used to plot the state frequency distribution for both tna
and group_tna
objects.permutation_test()
is now a method for both ungrouped (build_model()
) and grouped (group_model()
) models. For grouped models, the function performs the test between every unique pair of groups. adjust
has been added for permutation_test()
to optionally adjust p-values using p.adjust
. By default, the p-values are not adjusted (adjust = "none"
).groupwise
has been added for group_model()
. When FALSE
(the default), scaling methods listed in scaling
are performed globally over the groups. When TRUE
, the scaling is performed within each group instead (this was the default behavior in previous versions of the package).simulate()
method for tna
objects. For models with type = "relative"
, this function simulates sequence data based on the initial probabilities and transition probability matrix.plot.tna_centralities()
and plot.group_tna_centralities()
functions now plot the centralities in the same order as provided in the measures
argument.plot.tna()
and plot_model()
functions now use the median edge weight as the default value for the cut
argument.from
and to
columns in bootstrap()
output, which were inverted from the true edge direction.bootstrap()
output, which plots the corresponding network where non-significant edges have been pruned.permutation_test()
function now properly checks that its arguments x
and y
can be compared.permutation_test()
and bootstrap()
have been adjusted by adding 1 to both the number of permutations/bootstrap samples and the number of extreme events so that these estimates are never zero. The documentation has also been clarified regarding p-values emphasizing that these are estimates only.plot_compare()
function now supports negCol
and posCol
for specifying the color of the positive and negative differences in transition and initial probabilities.plot_mosaic()
function now plots the x-axis on the top and rotates the labels 90 degrees only when there are more than three groups.detailed
argument of estimate_centrality_stability()
has been removed. Previously this argument had no effect on the output of the function.prepare_data()
function now produces an object of class tna_data
, which can be directly used as an argument to build_model()
and other methods.prepare_data()
function now supports order
when used together with time
and actor
.prepare_data()
function gains the unused_fn
argument of tidyr::pivot_wider()
to process any extra columns. The default is to keep all columns and use the first value.compare()
to compare tna
models and weight matrices. This function produces an object of class tna_comparison
which has print()
and plot()
methods.plot_mosaic()
which can be used to produce mosaic plots of transition counts for frequency-based transition network models and to contrast the state counts between groups.plot.tna_communities()
which now checks for the availability of a particular community detection method before plotting.event2sequence()
has been renamed to prepare_data()
. The function is now also more general and can process more date formats.method
argument to bootstrap()
. The new default option "stability"
implements a bootstrapping scheme where the edge weights are compared against a range of "consistent" weights (see the documentation for details). The old functionality can be accessed with method = "threshold"
.permutatation_test()
when x
and y
had a differing number of columns.methods
argument in communities()
.build_model()
function has gained the argument cols
which can be used to subset the columns of the data for stslist
and data.frame
inputs.verbose
arguments in favor of options(rlib_message_verbosity = "quiet").
and options(rlib_warning_verbosity = "quiet")
.character
type arguments.bootstrap()
function to determine edge significance based on
deviation from the observed value, rather than a fixed threshold.event2sequence()
to parse event data into sequence data.Any scripts or data that you put into this service are public.
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