Version numbers follow Semantic Versioning.
2023-09-16
2020-03-18
2020-03-02
straingsAsFactors update2019-12-22
sim_TS2019-07-28
2019-07-24
print/cat has been
replaced with message messages.duntrun{} with donttest{})messageq replaces qprint2019-07-10
2019-07-09
2019-07-09
LDA_TS function level, the separate inputs for
data tables (document_term_table and
document_covariate_table) have been merged into a single
input data, which can be just the
document_term_table or a list including the
document_term_table and optionally also a
document_covariate_table. If covariates aren’t provided,
the function now constructs a covariate table assuming equi-spaced
observations. If using a list, the function assumes that one and only
one element of the list will have a name containing the letters “term”,
and at most one element containing the letters “covariate” (regular
expressions are used for matching). (addresses issue
119)timename has been moved from within the
TS_controls_list to a main argument in all associated
functions.LDA_controls_list,
TS_controls_list, or LDA_TS_controls_list
inputs now take general list inputs (so LDA_TS
does not need to have a nested set of control functions). Each control
list is passed through a function (LDA_set_control,
TS_control, or LDA_TS_control) to set any
non-input values to their defaults. This also allows the removal of
those controls list class definitions. (addresses issue
130)control input in the plot call in
the example in the README (addresses issue
116)?logLik.LDA_VEM for references.devAskNewPage to help flip
through multiple outputs, but were only resetting it with
devAskNewPage(FALSE) at the end of a clean execution. The
code has been updated with on.exit(devAskNewPage(FALSE)),
which accounts for failed executions. (addresses issue
118)summarize_TS has been renamed package_TS
to align with the other package_ functions that package
model output.sim_LDA_data simulates an LDA model’s
document-term-matrixsim_TS_data simulates an TS model’s document-topic
distribution matrixsim_LDA_TS_data simulates an LDA_TS model’s
document-term-matrixsoftmax and logsumexp are added as utility
functionsTSTS was named “deviance”. The output has been updated to
return the AIC.AIC method with logLik method for
TS_fit2019-02-11
AIC.LDA_VEM() now uses the number of parameters as
reported from logLik to calculate AIC.document_weights() function is provided to allow for
appropriate weighting of documents by their sizes (number of words) so
that an average-length document is 1.weights = NULL.multinom_TS() and
multinom_TS_chunk() now is optional via
memoise_fun() and is controlled through the TS controls
list.LDA_set(), LDA_TS(), and TS()
now all have default plotting options on their outputs.plot.TS() provides MCMC diagnostic plots and summary
plots.plot.LDA_TS() plots produce the combination of
plots.data(rodents).The comparison vignette provides a step-by-step comparison of the LDATS pipeline to the analysis in Christensen et al. 2018.
The key differences are as follows:
* The `document_term_table` in Christensen *et al.* 2018 was adjusted to account for variable trapping effort. The data included in LDATS are not adjusted, so that sampling periods can be weighted appropriately.
* The LDA model selection criterion has changed (see LDA model AIC calculation, above), so that LDATS now identifies 6 topics compared to the 4 topics found in the paper.
* LDATS will by default weight sampling periods according to the number of terms (see Document weighting, above).
* Despite these changes, the updated LDATS pipeline gives qualitatively similar results to the analysis in Christensen *et al.* 2018. 2017-11-16