An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model.

The step interval for approximation for a continuous time DSEM. The smaller the step interval, the better the approximation.

The variable in data that identifies the person or observational unit (as character).

The variable in data that contains the (continuous) time (as string).

The names of between-level variables in the data to be added in newly created rows with NAs.

data.frame. Output of mlts_model and related functions.

An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model. Alternatively, a list object with simulated data created by mlts_sim can be entered directly and allows for comparison of estimates and true population paramter values used in the data generation.

Character. The variable in data that identifies the observational cluster unit. Not necessary when data is a list object of simulated data generated with mlts_sim.

Character. The variable in data that identifies the grouping variable across cluster units.

Character. The variable(s) in data that contain the time-series construct(s) or their indicator variable(s). If multiple constructs are provided in the model, multiple entries are necessary. Note that the order of variable names provided in ts has to match the specification made in the model. E.g., if multiple constructs (e.g., mlts_model(q = 2)) are provided the order of variables names provided in ts determines which construct is referred to as mu_1, phi(1)_11, etc..

Named character vector. An optional named vector of characters to refer to predictors of random effects as specified in the model. Note that specifying covariates is only necessary if the respective variable name(s) in data differ from the variables names specified in model.

Named character vector. Similar to covariates, an optional named vector of characters to refer to outcome predicted by random effects as specified in the model. Note that specifying outcomes is only necessary if the respective variable name(s) in data differ from the outcome variable name(s) specified in model.

Named character vector. Similar to covariates, an optional named vector of characters to refer to additional between-level variables entered as outcome predictor(s) as specified in the model. Note that specifying outcome_pred_btw is only necessary if the respective variable name(s) in data differ from the variable name(s) specified in model.

Logical. Between-level covariates used as predictors of random effects will be grand-mean centered before model fitting by default. Set center_covs to FALSE when including categorical predictors into the set of covariates. Note that in this case, additional continuous covariates should be grand-mean centered prior to using mlts_fit. If group is specified, covariates will be centered on their respective group mean.

Character. The variable in data that contains the (continuous) time of observation.

The step interval for approximating equally spaced observations in time by insertion of missing values, to be specified with respect to the time stamp variable provided in time. Procedure for inserting missing values resembles the procedure for time shift transformation as described in Asparouhov, Hamaker, & Muthén (2018).

Character. The variable in data that contains the running beep number starting with 1 for each person.

Optional. If a running beep identifier is provided via the beep argument and observations are nested within days (or similar grouping unit), the variable in data that contains the day identifier can be added to correct for overnight lags (see Details).

Optional. The number of NA rows to add after the last observation of each day (if days is provided).

Integer. Specify a maximum number of consecutive missing values. Can decrease estimation times drastically in the presence of very long sequences of missing values (e.g., when setting tinterval to values of small time steps).

logical. Per default, missing values remain in the data and will be imputed during model estimation. Set to TRUE to remove all rows with missing values in variables given in ts.

A positive integer specifying the number of iterations for each chain (including 50% used as warmup). The default is 500.

A positive integer specifying the number of Markov chains. The default is 2.

The number of cores to use when executing the Markov chains in parallel. The default is 2 (see stan).

Logical. Should person parameters (i.e., values of the latent variables) be stored? Default is FALSE.

Logical. Should all parameters be stored? Default is FALSE.

Logical. Set to TRUE to obtain standardized estimates in multiple-indicator models.

Logical. Set to FALSE to avoid fitting the model which may be helpful to inspect prepared data used for model estimation (default = TRUE).

Logical. Print messages based on defined inputs (default = TRUE).

Logical. Print warnings based on defined inputs (default = TRUE).

Additional arguments passed to sampling.

the model object passed to mlts_fit

the preprocessed data used for fitting the model

a data.frame that provides the names of parameters used in the stan model. These parameter names are necessary when running standard post-processing functions using mlts_fit$stanfit

a data.frame that contains summary statistics for all parameter in model

if monitor_person_pars = TRUE, a data.frame containing summary statistics for cluster-specific parameters is provided

a list with the data as passed to sampling

an object of class stanfit with the raw output created by sampling

an array of the MCMC chain results for all parameters in model created by rstan::extract with dimnames adapted to match the parameter names provided in model

Character. Indicating the model type to be specified. For now restricted to VAR, the default. Future package releases might include additional model types.

Integer. The number of time-varying constructs.

Integer. For multiple-indicator models, specify a vector of length q with the number of manifest indicators per construct. If all constructs are measured with the same number of indicators, a single value is sufficient.

Integer. The maximum lag of the autoregressive effect to be included in the model. The maximum is 3. Defaults to 1.

Logical. If TRUE (the default), a common between-level factor is modeled across all indicator variables per construct q. If FALSE, instead of a between-level factor, indicator mean levels will be included as individual (random) effects drawn from a joint multivariate normal distribution.

A list to indicate for which manifest indicator variables a common between-level factor should be modeled (see Details for detailed instructions). At this point restricted to one factor per latent construct.

Logical. For multiple-indicator model with btw_factor = TRUE, if TRUE, factor loadings of the same indicators are assumed to be equal across levels. Note, that the first indicator loading parameters remain fixed to 1.

Logical. Fix all random effect variances of autoregressive and cross-lagged effects to zero (constraining parameters to be equal across clusters).

Logical. Fix all random effect variances of innovation variances to zero (constraining parameters to be equal across clusters).

Logical. Fix all random effect variances of innovation covariances to zero (constraining parameters to be equal across clusters).

Logical. Set to TRUE to treat all innovations as independent.

For bivariate VAR models with person-specific innovation covariances, a latent variable approach is applied (for a detailed description, see Hamaker et al., 2018). by specifying an additional factor that loads onto the contemporaneous innovations of both constructs, capturing the shared variance of innovations, that is not predicted by the previous time points. The loading parameters of this latent factor, however, have to be restricted in accordance with researchers assumptions about the sign of the association between innovations across construct. Hence, if innovations at time $t$ are assumed to be positively correlated across clusters, set the argument to pos, or neg respectively.

Character. A character vector to index which fixed effects (referring to the parameter labels in model$Param) should be constrained to zero (Note: this also results in removing the random effect variance of the respective parameter).

Character. A character vector to index which random effect variances (referring to the parameter labels in model$Param) should be constrained to zero.

A character vector or a named list. Include between-level covariate(s) as predictor(s) of all random effects in model by entering a vector of unique variable names. Alternatively, to include between-level covariates or differing sets of between-level covariates as predictors of specific random effects, a named list (using the labels in model$Param) can be entered (see examples). Note that if a named list is provided, all names that do not match random parameters in model will be ignored. Note that variables entered in ranef_pred will be grand-mean centered by default when fitting the model with mlts_fit.

A character vector or a named list. Include between-level outcome(s) to be regressed on all random effects in model by entering a vector of unique variable names. Alternatively, to include multiple between-level outcomes regressed differing sets of specific random effects, a named list (using the labels in model$Param) can be entered (see examples). Note that if a named list is provided, all character strings in the vector of each list (with independent variables) element that do not match random effect parameter names in model$Param will be treated as additional between-level predictors.

A character vector. If out_pred is a character (vector), all inputs will be treated as between-level covariates to be used as additional predictors of all outcomes specified in out_pred.

An integer specifying the number of groups (not yet supported). Add a binary coded (0 vs. 1) variable to include group differences in fixed effects (intercepts). When dynamic or variance parameters are allowed to vary by cluster, you can enter the grouping variable to re_pred.

Integer or a vector of integers. Indicate if any of the constructs should be treated as exogenous (i.e., no latent mean centering will be performed). Probable use case: Adding a dichotomous time-varying predictor variable.

A character vector. Experimental: Add contemporaneous effects to the model. For example, to include an effect of the first construct on the second construct at time $t$, following the general pattern for naming of dynamic parameters in the mlts framework, can be included by specifying ⁠phi(0)_21⁠ where the 0 indicates the lag, the first subscript letter (2) the dependent, and the latter subscript (1) the independent construct. The respective within-level correlation/covariance of innovations between involved constructs will be excluded from the model accordingly.

A character vector. Add interaction terms on the dynamic within-level. For example, to add an interaction term between the first construct at time $t$ (lag of 0) and the second construct at $t-1$ (lag of 1) to the prediction of the second construct at time $t$ specify ⁠incl_interaction_effects = phi(i)_2.2(1)1(0)⁠. where the i indicates an interaction effect, the first subscript letter (2) the dependent, and the latter subscripts after the dot (i.e., 2(1) and 1(0)) the independent constructs involved in the interaction each followed by the respective lag in brackets. Note, that in this case the respective lag 0 effects need to be included separately using incl_t0_effects.

Numeric. If an input is provided (i.e., a single numeric value) a left-censored version of the model will be estimated by treating all observations (of manifest indicators) at the censoring threshold (i.e., usually the lower bound of the scale) to be treated as missing during model estimation. These missing values (observations at the value of censor_left) are replaced with imputed values (declared as parameters in the stan model) with an upper limit of censor_left (see https://mc-stan.org/docs/stan-users-guide/truncation-censoring.html). Note that all manifest variables are affected by the censoring. To prevent individual variables from being treated as censored you could change the scale of the respective variable(s) so that all values exceed the censoring threshold.

Numeric. Developmental. Similar to censor_left but assumes variables to be censored on the upper bound of the scale. Can be combined with censor_left.

logical. Set to TRUE to suppress warnings and messages.

Indicates if the parameter in the respective row is part of the structural, or the measurement model (if multiple indicators per construct are provided)

Parameter on the between- or within-level.

Describes the parameter type.

Parameter names to be referred to in arguments of mlts_model.

Parameter labels (additional option to address specific parameters).

Indicates which within-level parameters are modeled as random (1) or a constant across clusters (0).

Optional. Included if multiple-indicators per construct (p > 1) are provided. Constraints on measurement model parameters can be changed by overwriting the respective value in model. Possible inputs are "free", "= 0" (for SDs of measurement error variances), and "= 1" (for loading parameters).

Contains the parameters' prior distribution used in mlts_fit (prior classes can not be changed at this point).

Location values of the parameters' prior distribution used in mlts_fit (can be changed to any real value by overwriting the respective value in model).

Scale values of the parameters' prior distribution used in mlts_fit (can be changed to any real value by overwriting the respective value in model).

A model built with mlts_model.

An optional string containing the name of the file and file path. Has to end with .pdf file format.

Logical. Should the TeX file be kept (additional to the Rmd file)? Defaults to FALSE.

To be included in future releases. An optional character vector containing the names of the time-series variables or indicators.

To be included in future releases. An optional character vector containing the names of the between-level covariates.

To be included in future releases. An optional character vector containing the names of the between-level outcomes.

A model built with mlts_model.

An optional string containing the name of the file and file path. Has to end with .pdf file format.

Logical. Set to TRUE to transform created PDF to .png file using pdftools::pdf_convert.

Logical. Should the TeX file be kept (additional to the Rmd file)? Defaults to FALSE.

To be included in future releases. An optional character vector containing the names of the time-series variables or indicators.

To be included in future releases. An optional character vector containing the names of the between-level covariates.

To be included in future releases. An optional character vector containing the names of the between-level outcomes.

data.frame. Output of mlts_model and related functions.

A numeric value specifying the aspect ratio for the decomposition plot region. Defaults to 0.25.

A numeric value specifying the aspect ratio between the within-level and between-level sections. Defaults to 0.5.

A numeric vector of length 2 defining the horizontal margins of the plot. Defaults to c(0, 8).

A numeric vector of length 2 defining the vertical margins of the plot. Defaults to c(0, 8).

Width of the plot in inches. Defaults to 7.

Height of the plot in inches. Defaults to 5.

A character string specifying the path to save the plot. If NULL, the plot will not be saved. Defaults to NULL.

The overall aspect ratio of the plot, computed as height / width. Defaults to height / width.

Font family used in the plot. Defaults to "serif".

Numeric value specifying the scaling of text in the between-level section. Defaults to 0.8.

Numeric value specifying the scaling of text in the within-level section. Defaults to 0.8.

Numeric value specifying the scaling of text in the decomposition section. Defaults to 0.8.

Numeric value specifying the scaling of text of loading parameters. Defaults to 0.8.

A character string specifying the style of the between-level plot ("h" for horizontal). Defaults to "h".

Numeric value specifying the vertical width of the within-level part. Defaults to 0.

Numeric value to control the vertical space between manifest indicators and latent factors in the decomposition part of the path model. Defaults to 4.

Numeric values controlling the arrowhead size for within-level paths. Defaults to 0.16.

Numeric values controlling the arrowhead size for between-level paths. Defaults to 0.16.

Numeric. Specify the scaling factor for manifest indicators in the decomposition section.

Numeric. Specify the scaling factor for latent factors in the decomposition section.

Numeric. Specify the scaling factor for latent factors in the within-level section.

Numeric. Specify the scaling factor for innovations in the within-level section.

Numeric. Specify the scaling factor for factors in the between-level section.

Numeric. Specify the scaling factor for interaction factors in the within-level section.

Line width for node borders in the plot. Defaults to 1.7.

Numeric value controlling the random dot position in the plot. Defaults to 0.5.

A character string specifying the units for saving the plot. Defaults to "in".

The nominal resolution in ppi. Defaults to 320.

Numeric value specifying the font point size for the plot. Defaults to 10.

A character string specifying the file type for the saved plot (e.g., "cairo"). Defaults to "cairo".

A vector of character strings with names of observed variables.

A vector of character strings with factor labels to replace numeric indices in parameter names.

A character string to separate multiple factor labels. Defaults to ",".

Logical. Remove lag index from phi-parameter labels. Defaults to FALSE.

Numeric value specifying the x-axis offset loading parameter labels. Defaults to 1.25.

Additional arguments passed to internal plotting functions.

An object of class mlts.fit

Type of plot. type = "fe" (Default) Forest-plot of model coefficients. type = "re" Plot of individual (random) effects type = "int" Experimental: Plot within-level interactions. type = "re.cor" Combined plot depicting the distribution of individual parameter estimates (posterior summary statistics as provided by bpe), as well as bivariate scatter plots.

The Bayesian point estimate is, by default, the median of the posterior distribution (bpe = "median"). Set bpe = "mean" to use the mean of the posterior distribution as point estimates.

Character. For type = "fe", indicate which parameters should be included in the plot by setting what to "all" (the default), or one (or multiple) of "Fixed effect", "Random effect SD", "RE correlation", "Outcome prediction", "RE prediction", "Item intercepts", "Loading", or "Measurement Error SD".

Add parameter label for sorting of random effects.

Title for the x axis.

Title for the y axis.

Number of facet columns (see ggplot2::facet_grid).

numeric, size of the dots that indicate the point estimates.

character. indicating the color of the point estimates.

numeric. shape of the dots that indicate the point estimates.

character. Color of error bars.

integer. Width of error bars.

logical. If model was fitted with simulated data using mlts_sim, true population parameter values can be plotted as reference by setting the argument ot TRUE.

character. Color of points depicting true population parameter used in the data generation.

integer. Shape of points depicting true population parameter used in the data generation.

integer. Size of points depicting true population parameter used in the data generation.

logical. Hide x-axis text if set to TRUE.

character vector. User-specified labels for random effect parameters can be specified.

logical. Should parameter names on plot labels be printed as mathematical expressions? Defaults to FALSE. Still experimental.

An object of class mlts.fit, as returned by a fitted model using mlts().

Logical. If TRUE, samples are generated using person-specific parameters (random effects). If FALSE, only population-level parameters are used. Defaults to FALSE.

Integer. Number of posterior draws to use for simulating replicated datasets. Ignored if draws is provided. Defaults to 10.

Optional integer vector indicating specific posterior draw indices to use. If NULL, n_draws draws are chosen with the maximum distance between posterior samples.

Logical. Return replications of each variable as a matrix with n_draw rows, ready to run graphical posterior predictive checks using the bayesplot package.

A fitted model object of class mlts.fit. Only used if fit_list is NULL.

An optional list of fitted mlts.fit objects for model comparison. If provided, fit is ignored.

Optional vector of variable names to include in the plot.

Optional. A list of posterior predictive samples (as returned by mlts_posterior_sample) for a single model. If NULL, samples are generated within the function.

Logical. If TRUE, density plots are faceted by individual and time-series variable. If FALSE, only time-series variables are used for faceting. Default is FALSE.

Logical. If TRUE, density plots are faceted by grouping and time-series variable. If FALSE, only time-series variables are used for faceting. Default is FALSE.

Optional vector of cluster IDs to include in the plot. If NULL, all IDs are shown.

Logical. If TRUE, samples are generated using person-specific parameters (random effects). If FALSE, only population-level parameters are used. Defaults to FALSE.

Integer. Number of posterior draws to use for generating replicated datasets. Defaults to 20. Ignored if draws is specified.

Optional vector of indices specifying which posterior draws to use. If NULL, n_draws samples are drawn randomly.

Logical. Whether to include the observed data distribution in the plot. Defaults to TRUE.

Optional character vector with labels for each model in fit_list. If NULL, defaults to "Model 1", "Model 2", etc.

Optional vector of colors for the posterior predictive densities of each model. If NULL, a default color palette is used.

Color for the observed data distribution. Default is "#009E73".

Line width of the observed data density curve. Default is 1.1.

Line width of the observed data density curve. Default is 0.5.

Alpha transparency for the predictive density curves. Default is 0.5.

data.frame. Output of mlts_model.

logical. If set to TRUE, default prior specifications are added.

integer Number of observational units.

vector of integers. Number of observational units per group.

integer. Number of measurements per observational unit.

integer. Length of ‘burn-in’ period.

integer. Seed used for data generation.

integer. Separate seed used for sampling of true population parameters values from plausible ranges for stationary time series.

named numeric vector. Provide standard deviation(s) for all exogenous between-level variable(s) specified in model, e.g. (btw.var.sds = c("covariate1" = 1), to set the SD of the variable "covariate1" to 1). Mean values of the respective variable(s) will be set to 0 per default.

Matrix of numeric values of exogenous variables with N*(TP+burn.in) rows and separate columns for each variable.

the model object passed to mlts_sim with true parameter values used in the data generation added in the column true.val

a long format data.frame of the generated time series data

a matrix of cluster-specific true values used in the data generation

mltsfit. Output of mlts_model and related functions.

character. Get between-level standardized estimates (what = "between", the default), within-level standardized estimates averaged over clusters (what = "within"), or both (what = "both").

Number of digits. Default is 3.

A value between 0 and 1 to indicate the width of the credible interval. Default is .95.

logical. If what = "within", within-level standardized effects for each cluster are included in the output (defaults to FALSE).

logical. For internal use.

An object of class mltsfit.

Add prior information (default = FALSE).

Logical. Should the Monte Carlo Standard Error be included in the summary? Defaults to FALSE.

A value between 0 and 1 to indicate the width of the credible interval. Default is .95.

Bayesian posterior estimate can be either "mean" (the default) or the "median" of the posterior distribution.

Number of digits.

Add significance flags based on prob (default = FALSE).

Additional arguments affecting the summary produced.