Function reference • LDATS

Joint LDA and Time Series Functions for top-level LDATS modeling
`LDA_TS()` `conform_LDA_TS_data()` `check_LDA_TS_inputs()`	Run a full set of Latent Dirichlet Allocations and Time Series models
`package_LDA_TS()`	Package the output of LDA_TS
`print(<LDA_TS>)`	Print the selected LDA and TS models of LDA_TS object
`plot(<LDA_TS>)`	Plot the key results from a full LDATS analysis
`set_LDA_TS_plot_cols()`	Create the list of colors for the LDATS summary plot
Latent Dirichlet Allocation Functions to extend exisiting LDA functionality
`LDA_set()` `check_LDA_set_inputs()`	Run a set of Latent Dirichlet Allocation models
`package_LDA_set()`	Package the output from LDA_set
`logLik(<LDA_VEM>)`	Calculate the log likelihood of a VEM LDA model fit
`select_LDA()`	Select the best LDA model(s) for use in time series
`LDA_msg()`	Create the model-running-message for an LDA
`plot(<LDA_set>)`	Plot a set of LDATS LDA models
`plot(<LDA_VEM>)` `LDA_plot_top_panel()` `LDA_plot_bottom_panel()`	Plot the results of an LDATS LDA model
`set_LDA_plot_colors()`	Prepare the colors to be used in the LDA plots
Time Series Functions to execute and evaluate the overall TS model with changepoints and regression components
`TS()` `check_TS_inputs()`	Conduct a single multinomial Bayesian Time Series analysis
`package_TS()`	Summarize the Time Series model
`logLik(<TS_fit>)`	Determine the log likelihood of a Time Series model
`print(<TS_fit>)`	Print a Time Series model fit
`plot(<TS_fit>)`	Plot an LDATS TS model
`TS_diagnostics_plot()` `eta_diagnostics_plots()` `rho_diagnostics_plots()`	Plot the diagnostics of the parameters fit in a TS model
`trace_plot()`	Produce the trace plot panel for the TS diagnostic plot of a parameter
`ecdf_plot()`	Produce the posterior distribution ECDF panel for the TS diagnostic plot of a parameter
`posterior_plot()`	Produce the posterior distribution histogram panel for the TS diagnostic plot of a parameter
`autocorr_plot()`	Produce the autocorrelation panel for the TS diagnostic plot of a parameter
`set_TS_summary_plot_cols()`	Create the list of colors for the TS summary plot
`TS_summary_plot()` `pred_gamma_TS_plot()` `rho_hist()`	Create the summary plot for a TS fit to an LDA model
`rho_lines()`	Add change point location lines to the time series plot
`set_rho_hist_colors()`	Prepare the colors to be used in the change point histogram
`set_gamma_colors()`	Prepare the colors to be used in the gamma time series
`summarize_etas()` `measure_eta_vcov()`	Summarize the regressor (eta) distributions
`summarize_rhos()` `measure_rho_vcov()`	Summarize the rho distributions
`est_regressors()`	Estimate the distribution of regressors, unconditional on the change point locations
`est_changepoints()`	Use ptMCMC to estimate the distribution of change point locations
`prep_pbar()` `update_pbar()`	Initialize and tick through the progress bar
Multinomial Time Series modeling Functions to fit the multinomial time series models making up an overall TS model
`multinom_TS()` `check_multinom_TS_inputs()`	Fit a multinomial change point Time Series model
`logLik(<multinom_TS_fit>)`	Log likelihood of a multinomial TS model
`multinom_TS_chunk()`	Fit a multinomial Time Series model chunk
`prep_chunks()`	Prepare the time chunk table for a multinomial change point Time Series model
`package_chunk_fits()`	Package the output of the chunk-level multinomial models into a multinom_TS_fit list
`verify_changepoint_locations()`	Verify the change points of a multinomial time series model
Parallel tempering Markov Chain Monte Carlo Functions to execute ptMCMC optimization
`prep_ptMCMC_inputs()`	Prepare the inputs for the ptMCMC algorithm estimation of change points
`prep_ids()` `update_ids()`	Initialize and update the chain ids throughout the ptMCMC algorithm
`prep_proposal_dist()`	Pre-calculate the change point proposal distribution for the ptMCMC algorithm
`prep_saves()` `update_saves()` `process_saves()`	Prepare and update the data structures to save the ptMCMC output
`prep_cpts()` `update_cpts()`	Initialize and update the change point matrix used in the ptMCMC algorithm
`prep_temp_sequence()`	Prepare the ptMCMC temperature sequence
`diagnose_ptMCMC()`	Calculate ptMCMC summary diagnostics
`count_trips()`	Count trips of the ptMCMC particles
`swap_chains()`	Conduct a set of among-chain swaps for the ptMCMC algorithm
`step_chains()` `propose_step()` `eval_step()` `take_step()`	Conduct a within-chain step of the ptMCMC algorithm
`proposed_step_mods()`	Fit the chunk-level models to a time series, given a set of proposed change points within the ptMCMC algorithm
Time Series on LDA output Functions to facilitate connection running multiple TS models on LDA output
`TS_on_LDA()` `check_TS_on_LDA_inputs()`	Conduct a set of Time Series analyses on a set of LDA models
`package_TS_on_LDA()`	Package the output of TS_on_LDA
`print(<TS_on_LDA>)`	Print a set of Time Series models fit to LDAs
`expand_TS()`	Expand the TS models across the factorial combination of LDA models, formulas, and number of change points
`prep_TS_data()`	Prepare the model-specific data to be used in the TS analysis of LDA output
`select_TS()`	Select the best Time Series model
`print_model_run_message()`	Print the message to the console about which combination of the Time Series and LDA models is being run
Simulation Functions for simulating input data
`sim_LDA_data()`	Simulate LDA data from an LDA structure given parameters
`sim_TS_data()`	Simulate TS data from a TS model structure given parameters
`sim_LDA_TS_data()`	Simulate LDA_TS data from LDA and TS model structures and parameters
Control options Functions to set function options defined through control lists
`LDA_TS_control()`	Create the controls list for the LDATS model
`LDA_set_control()`	Create control list for set of LDA models
`TS_control()`	Create the controls list for the Time Series model
`prep_LDA_control()`	Set the control inputs to include the seed
Input checking Functions to tidy code by condensing input object verification
`check_changepoints()`	Check that a set of change point locations is proper
`check_control()`	Check that a control list is proper
`check_document_covariate_table()`	Check that the document covariate table is proper
`check_document_term_table()`	Check that document term table is proper
`check_formula()`	Check that a formula is proper
`check_formulas()`	Check that formulas vector is proper and append the response variable
`check_LDA_models()`	Check that LDA model input is proper
`check_nchangepoints()`	Check that nchangepoints vector is proper
`check_seeds()`	Check that nseeds value or seeds vector is proper
`check_timename()`	Check that the time vector is proper
`check_topics()`	Check that topics vector is proper
`check_weights()`	Check that weights vector is proper
Utilities A variety of helpful little functions
`AICc()`	Calculate AICc
`document_weights()`	Calculate document weights for a corpus
`iftrue()`	Replace if TRUE
`logsumexp()`	Calculate the log-sum-exponential (LSE) of a vector
`modalvalue()`	Determine the mode of a distribution
`memoise_fun()`	Logical control on whether or not to memoise
`messageq()`	Optionally generate a message based on a logical input
`mirror_vcov()`	Create a properly symmetric variance covariance matrix
`normalize()`	Normalize a vector
`softmax()`	Calculate the softmax of a vector or matrix of values
Data and Package Datasets provided and the package
`jornada`	Jornada rodent data
`rodents`	Portal rodent data
`LDATS`	Package to conduct two-stage analyses combining Latent Dirichlet Allocation with Bayesian Time Series models

Reference

Joint LDA and Time Series

Latent Dirichlet Allocation

Time Series

Multinomial Time Series modeling

Parallel tempering Markov Chain Monte Carlo

Time Series on LDA output

Simulation

Control options

Input checking

Utilities

Data and Package

Contents