The ** LDATS** package provides
functionality for analyzing time series of high-dimensional data using a
two-stage approach comprised of Latent Dirichlet Allocation (LDA) and
Bayesian time series (TS) analyses.

For a full description of the math underlying the
** LDATS** package, see the technical
document.

A stable version of LDATS is available on CRAN, but the package is actively being developed by the Weecology Team. The API is well defined at this point and should not change substantially.

You can install the stable version of
** LDATS** from CRAN with:

To obtain the current development version of
** LDATS** from GitHub, install the

`devtools`

`LDATS`

```
install.packages("devtools")
::install_github("weecology/LDATS") devtools
```

Here is an example of a full LDA-TS analysis using the Portal rodent data:

```
library(LDATS)
data(rodents)
<- LDA_TS(rodents, topics = 2:5, nseeds = 2, formulas = ~1,
r_LDATS nchangepoints = 0:1, timename = "newmoon")
```

Which conducts two replicates (`nseeds`

) for each of two
to five topics in an LDA model using the document term table, selects
the best (AIC) of those, then conducts two time series models on it (an
intercept-only model under 0 and 1 changepoints), then selects the best
(AIC) of the time series, and packages all the models together. This
uses the document term table to weight the samples by their sizes
(number of words) and instructs the function to use the column named
`"newmoon"`

in the document covariates table as the time
variable.

The resulting object is of class `LDA_TS`

, which has a few
basic routines available:

`print(r_LDATS)`

prints the selected LDA and TS models and

`plot(r_LDATS)`

produces a 4-panel figure of them a la Figure 1 from Christensen et al. 2018.

Based on initial work using LDA to
analyze time-series data at Portal by Erica M. Christensen, David J.
Harris, and S. K. Morgan Ernest, which has been published in
*Ecology*

The motivating study—the Portal Project—has been funded nearly continuously since 1977 by the National Science Foundation, most recently by DEB-1622425 to S. K. M. Ernest, which also supported (in part) E. Christensen’s time. Much of the computational work (including time of J. Simonis, D. Harris, and H. Ye) was supported by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grant GBMF4563 to E. P. White. R. Diaz was supported in part by a National Science Foundation Graduate Research Fellowship (No. DGE-1315138 and DGE-1842473).

**J. L. Simonis** provided insight on LDA applications
and feedback on technical writing during development of the first
version of the LDATS model and application, led the coding and
mathematical development of the model into an R package, and led writing
on the technical model document. **E. M. Christensen** led
the project during development of the first version of the LDATS model
and its application to the Portal data, specifically conceiving the
project, coding the pipeline wrappers of the analysis, and writing and
editing the first description of the model and its application (Christensen *et al.*
2018). **D. J. Harris** was involved in developing and
applying the first version of the LDATS model, specifically suggesting
the LDA and change point approaches, coding the first version of the
change point model, and writing and editing the first description of the
model (Christensen *et
al.* 2018). **R. Diaz** contributed code to the
LDATS package, wrote vignettes, provided insight into model development,
and conducted extensive end-user code application testing. **H.
Ye** contributed code to the LDATS package, insight into data
structures and LDA algorithms, and significant feedback on vignettes.
**E. P. White** helped design, troubleshoot, and supervise
initial methods development; provided big-picture feedback on
development of the R package; contributed end-user application testing;
and gave substantial editing feedback on the technical document.
**S. K. Morgan Ernest** provided managerial oversight and
feedback on the project in both the initial and second stages of LDATS
development, tested applications of the code to data sets, and assisted
with writing and editing of the first description of the model and its
application (Christensen
*et al.* 2018) as well as the technical model document.