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scatter_legend_dots_alpha
and
scatter_legend_bg_dots_alpha
parameters for the
topicsPlot()
function.logistic_regression
.occurance_rate
to topicsGrams()
removal_mode
, removal_rate_most
and
removal_rate_least
to topicsGrams()
ngram_window = c(1)
now supported by
topicsDtm()
topicsPlot()
with ngramssize
in the dot legend will be based on
prevalence
if scatter_legend_dot_size = “prevalence”. And
the popouts are not transparent.generate_scatter_plot
.highlight_topic_words
is set to
NULL
in the topicsPlot()
function.topicsGrams()
, including
removing top_n
and treating n-grams type differently.stopwords
function to
topicsGrams()
.pmi
calculation.ngrams_max
parameter in
`topicsPlot()```.allowed_word_overlap
in
topicsPlot()
for plotting the most prevalence.highlight_topic_words
parameter to add different
colours for a word list.stopwords
removal for
topicsGram()
.ngrams_max
functionality to
topicsPlot()
.save_dir
and load_dir
from all
function; only topicsPlot()
now has the
save_dir
as an option.prevalence
.p_adjust_method
to
topicsPlots()
.scatter_show_axis_values
to the
topcisPlot()
.n_most_prevalent_topics
.default
to linear_regression if not the
variable only contains 0s and 1s; i.e., now different tests can be
applied to different axes.dtm
for downstream use in other
functions.topicsPred()
function
including num_iteration
, sampling_interval
,
burn_in
.create_new_dtm
for creating a new
dtm
for new datatopics
dimension for training
using textTrainRegression()
.topicsTest()
incl. x_variable, y_variable and controlspmi_threshold
(experimental) to
topicsDtm()
split
procedure
in the topicsDtm()
topicsDtm()
p_threshold
to p_alpha
p_alpha
from the topicsTest()
function to the topicsPlots()
functiontopicsTest()
text
-packagetopicsPlot()
.topicsTest().
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.