refine_keywords() function (#232)refine_keywords() function to refine
keywords by dropping topics that do not have any occurrence in the
documents (#222)save() function (thank you @AMindToThink for repoting this
error in #214)eta.shuffled_indexes() that
internally used std::random_shuffle(). This change does not
guarantee backward compatibility across all platforms.cli instead of the base R
message() and warning() functions.label model in
keyATM() (it was an experimental feature).plot_timetrend() output (thank you @WenHanGao for the suggestion in #188).summary.keyATM_docs().plot_topicprop() function.semantic_coherence() function (thanks to
Seo-young Silvia Kim for your suggestion).width) in the
plot_timetrend() function.Rcpp::message() if
verbose = TRUE.parallel::mclapply.future.apply instead of parallel (no
backward compatibility if you use the init_parallel
option).keyATM_read() function returns a list of objects
(e.g., text and document index).keep_docnames option in the keyATM_read()
function (thank you Morgan
‘Les’ DeBusk-Lane for the suggestion!).method = "hdi") in plot.strata_doctopic(),
plot_timetrend(), and plot_pi(). The previous
version uses the Equal-tailed Interval
(method = "eti").read_keywords for reading dictionary files
(e.g. YAML, LIWC).predict() function for the covariate keyATM
(thank you Sanja
Hajdinjak for the suggestion!).standardize option in model_settings
argument of the keyATM() function now takes one of
"all", "none", or "non-factor"
(default)."all" standardizes all covariates (except the
intercept), "none" does not standardize any covariates, and
"non-factor" standardizes non-factor covariates.TRUE
(default, standardizing all covariates) or FALSE.by_strata_DocTopic() function.keyATM() includes the index of
documents used for fitting (this will be useful if the input includes
documents with zero length).progress_bar option in the
keyATM_read() function (thank you Jae Yeon Kim for the
suggestion!).test-Initialization.R to deal with
some errors.by_strata_DocTopic() function.save_fig() function.plot.strata_doctopic(): showing by topic by
default (thank you Soichiro
Yamauchi for the suggestion!).weightedLDA() without specifying the number of
iterations (Chung-hong
Chan independently reported this bug, thank you!).summary.strata_doctopic(): the last topic is removed
when the number of no-keyword topic is 0 (thank you Emma Ebowe for pointing out this
issue!).hashmap with fastmap.
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