Version numbers follow Semantic Versioning.

*2023-09-16*

- The paper comparison vignette is now pre-computed to avoid needing to access internet resources during cran build (see https://ropensci.org/blog/2019/12/08/precompute-vignettes/).

- Travis CI no longer works, so shifting to github actions for builds

*2020-03-18*

- Dependencies are being managed different now
- For the paper comparison vignette, all of the code is pre-run and saved in the LDATS-replications repository
- Allows removal of otherwise unused packages from this package’s dependency list

*2020-03-02*

`straingsAsFactors`

update- only involved patching one test

*2019-12-22*

- Don’t need to make a sparse matrix to pass in now
- Tweaking the simulation functions to simplify X

`sim_TS`

- Using only as.matrix() fails if there is only 1 year in a segment and there are multiple covariates. In that case, as.matrix(X[in1, ]) returns a matrix of n_covariates rows x 1 column, instead of a matrix of 1 row and n_covariates columns. This edit should fix that by forcing it into a matrix of the correct number of rows.

*2019-07-28*

- Added space between braces for auto-linking in the description text.

*2019-07-24*

- Output given by
`print`

/`cat`

has been replaced with`message`

messages. - Added examples in documentation (and replacement of
`duntrun{}`

with`donttest{}`

) - Editing of description file for specs
- Reduction of test runtimes

`messageq`

replaces`qprint`

*2019-07-10*

- Including appropriate files in the Rbuildignore

- Handling the downloads so they work robustly locally

*2019-07-09*

- Incorporates Hao’s feedback and edits on the paper comparison vignette
- Updates the vignette to work with the contemporary version of the package
- Allowed removal of the large model cache files

- Inclusion of the json file for the Zenodo page

- The .pdf describing the model (the manuscript work in progress) is now at the top level and named “LDATS_model.pdf”, to allow the full model description to remain stable while the ms development happens elsewhere.

*2019-07-09*

- At the
`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.- The control lists have been made easier to interact with. Primarily,
the arguments that previously required
`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)

- Reduced the complexity of the example in the README (addresses issue 115)
- Added
`control`

input in the`plot`

call in the example in the README (addresses issue 116) - Reduced the number of seeds in the rodent vignette example (addresses issue 117)

- The number of observations for a VEM-fit LDA is now calculated as
the number of entries in the document-term matrix (following Hoffman et
al. and Buntine, see
`?logLik.LDA_VEM`

for references. - Associated, we now include an AICc function that is general and works in this specific case as defined (addresses issue 129)

- A few plotting functions use
`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.

- Basic simulation functionality has been added for help with generating data sets to analyze. (addresses issue 114)
`sim_LDA_data`

simulates an LDA model’s document-term-matrix`sim_TS_data`

simulates an TS model’s document-topic distribution matrix`sim_LDA_TS_data`

simulates an LDA_TS model’s document-term-matrix`softmax`

and`logsumexp`

are added as utility functions

- Function organization (addresses issue 122) and navbar formatting.

`TS`

- Due to a misread of earlier code, the AIC value in the output from
`TS`

was named “deviance”. The output has been updated to return the AIC.

`AIC`

method with `logLik`

method for
`TS_fit`

*2019-02-11*

- Creation of a standard API and code pipeline for all components of the LDATS analysis.
- Substantial refactor of the underlying code from hardcoded to generalized functions.
- Development of checking functions used to run the basic structural checks on the function inputs.
- Inclusion of control options lists for the LDA stage, TS stage, and overall to reduce the length of input lists.

- All functions used in the code base are now exported, documented, and tested.

`AIC.LDA_VEM()`

now uses the number of parameters as reported from`logLik`

to calculate AIC.- Previous by-hand calculations of AIC included variational parameters that are integrated out of the model in the total parameter count.

- Time series models allow for flexible covariate set for regression via formula inputs to the top-level functions.
- The time series model code now also includes estimation of the
parameters defining the between-change point regressions (
*i.e.*, the regressors). - Regressor estimates come as marginal posterior distributions, and are calculated by unconditioning the estimates generated under known change points.

`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.- Document weighting is done automatically by default, which is easily
undone by using
`weights = NULL`

.

- The ptMCMC code has been refactored into functions, many of which are generalized to use in other contexts.
- The temperature schema is fully controllable via arguments to the TS controls list
- Burn-in removal and thinning of final chains is controllable via the TS controls list

- Memoisation of
`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.

- Portal rodent data from Christensen
*et al.*(2018) are now provided in a pre-formatted and ready-to-roll data object. - Access the data using
`data(rodents)`

. - Note, however, that the data in Christensen
*et al.*2018 are scaled according to trapping effort. The data included in LDATS are not, to allow for appropriate weighting. See comparison vignette for further details.

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*

- Beginning initial development of package from original
code used in Christensen
*et al.*(2018).