AICc                    Calculate AICc
LDATS                   Package to conduct two-stage analyses combining
                        Latent Dirichlet Allocation with Bayesian Time
                        Series models
LDA_TS                  Run a full set of Latent Dirichlet Allocations
                        and Time Series models
LDA_TS_control          Create the controls list for the LDATS model
LDA_msg                 Create the model-running-message for an LDA
LDA_set                 Run a set of Latent Dirichlet Allocation models
LDA_set_control         Create control list for set of LDA models
TS                      Conduct a single multinomial Bayesian Time
                        Series analysis
TS_control              Create the controls list for the Time Series
                        model
TS_diagnostics_plot     Plot the diagnostics of the parameters fit in a
                        TS model
TS_on_LDA               Conduct a set of Time Series analyses on a set
                        of LDA models
TS_summary_plot         Create the summary plot for a TS fit to an LDA
                        model
autocorr_plot           Produce the autocorrelation panel for the TS
                        diagnostic plot of a parameter
check_LDA_models        Check that LDA model input is proper
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_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
count_trips             Count trips of the ptMCMC particles
diagnose_ptMCMC         Calculate ptMCMC summary diagnostics
document_weights        Calculate document weights for a corpus
ecdf_plot               Produce the posterior distribution ECDF panel
                        for the TS diagnostic plot of a parameter
est_changepoints        Use ptMCMC to estimate the distribution of
                        change point locations
est_regressors          Estimate the distribution of regressors,
                        unconditional on the change point locations
expand_TS               Expand the TS models across the factorial
                        combination of LDA models, formulas, and number
                        of change points
iftrue                  Replace if TRUE
jornada                 Jornada rodent data
logLik.LDA_VEM          Calculate the log likelihood of a VEM LDA model
                        fit
logLik.TS_fit           Determine the log likelihood of a Time Series
                        model
logLik.multinom_TS_fit
                        Log likelihood of a multinomial TS model
logsumexp               Calculate the log-sum-exponential (LSE) of a
                        vector
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
modalvalue              Determine the mode of a distribution
multinom_TS             Fit a multinomial change point Time Series
                        model
multinom_TS_chunk       Fit a multinomial Time Series model chunk
normalize               Normalize a vector
package_LDA_TS          Package the output of LDA_TS
package_LDA_set         Package the output from LDA_set
package_TS              Summarize the Time Series model
package_TS_on_LDA       Package the output of TS_on_LDA
package_chunk_fits      Package the output of the chunk-level
                        multinomial models into a multinom_TS_fit list
plot.LDA_TS             Plot the key results from a full LDATS analysis
plot.LDA_VEM            Plot the results of an LDATS LDA model
plot.LDA_set            Plot a set of LDATS LDA models
plot.TS_fit             Plot an LDATS TS model
posterior_plot          Produce the posterior distribution histogram
                        panel for the TS diagnostic plot of a parameter
prep_LDA_control        Set the control inputs to include the seed
prep_TS_data            Prepare the model-specific data to be used in
                        the TS analysis of LDA output
prep_chunks             Prepare the time chunk table for a multinomial
                        change point Time Series model
prep_cpts               Initialize and update the change point matrix
                        used in the ptMCMC algorithm
prep_ids                Initialize and update the chain ids throughout
                        the ptMCMC algorithm
prep_pbar               Initialize and tick through the progress bar
prep_proposal_dist      Pre-calculate the change point proposal
                        distribution for the ptMCMC algorithm
prep_ptMCMC_inputs      Prepare the inputs for the ptMCMC algorithm
                        estimation of change points
prep_saves              Prepare and update the data structures to save
                        the ptMCMC output
prep_temp_sequence      Prepare the ptMCMC temperature sequence
print.LDA_TS            Print the selected LDA and TS models of LDA_TS
                        object
print.TS_fit            Print a Time Series model fit
print.TS_on_LDA         Print a set of Time Series models fit to LDAs
print_model_run_message
                        Print the message to the console about which
                        combination of the Time Series and LDA models
                        is being run
proposed_step_mods      Fit the chunk-level models to a time series,
                        given a set of proposed change points within
                        the ptMCMC algorithm
rho_lines               Add change point location lines to the time
                        series plot
rodents                 Portal rodent data
select_LDA              Select the best LDA model(s) for use in time
                        series
select_TS               Select the best Time Series model
set_LDA_TS_plot_cols    Create the list of colors for the LDATS summary
                        plot
set_LDA_plot_colors     Prepare the colors to be used in the LDA plots
set_TS_summary_plot_cols
                        Create the list of colors for the TS summary
                        plot
set_gamma_colors        Prepare the colors to be used in the gamma time
                        series
set_rho_hist_colors     Prepare the colors to be used in the change
                        point histogram
sim_LDA_TS_data         Simulate LDA_TS data from LDA and TS model
                        structures and parameters
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
softmax                 Calculate the softmax of a vector or matrix of
                        values
step_chains             Conduct a within-chain step of the ptMCMC
                        algorithm
summarize_etas          Summarize the regressor (eta) distributions
summarize_rhos          Summarize the rho distributions
swap_chains             Conduct a set of among-chain swaps for the
                        ptMCMC algorithm
trace_plot              Produce the trace plot panel for the TS
                        diagnostic plot of a parameter
verify_changepoint_locations
                        Verify the change points of a multinomial time
                        series model
