A psychonetrics model.

Vector of strings indicating the matrices to plot CIs for

The alpha level used for the CIs

A vector of alphas used for coloring the CIs

The labels for the variables associated with the rows of a matrix.

The labels for the variables associated with the columns of a matrix. Defaults to the value of labels for square matrices.

The value to determine if labels are printed to the right or to the left of the CI

Logical, should the plots also be printed? Only works when one matrix is used in 'matrices'

Numeric indicating the step size between major breaks

Numeric indicating the step size between minor breaks

Logical only used for results of aggregate_bootstraps. When set to TRUE, the displayed intervals are based on occasions when the parameter was not estimated to be zero, and an extra box is added indicating the number of times a parameter is estimated to be zero. Defaults to TRUE when model selection is used and FALSE otherwise.

Logical only used for results of aggregate_bootstraps, should boxes indicating the proportion of times parameters were estimated to be zero be added to the plot? Defaults to the value of split0.

Only used for results of aggregate_bootstraps. Size of the boxes indicating number of times a parameter was set to zero.

Only used for results of aggregate_bootstraps. Transparency of the boxes indicating number of times a parameter was set to zero.

Only used for results of aggregate_bootstraps. Minimal transparency of the *lines* of plotted intervals as the proportion of times an edge was not included goes to 0.

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

The network structure. Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions nNode x nNode with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Optional vector encoding the threshold/intercept structure. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Optional scalar encoding the inverse temperature. 1 indicate free beta parameters, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such scalers.

How should beta be modeled? Set beta_model = "log_beta" to model the log of beta rather than beta directly.

An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in data.

An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in data.

A sample variance–covariance matrix, or a list/array of such matrices for multiple groups. Make sure covtype argument is set correctly to the type of covariances used.

A vector of sample means, or a list/matrix containing such vectors for multiple groups.

The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.

If 'covs' is used, this is the type of covariance (maximum likelihood or unbiased) the input covariance matrix represents. Set to "ML" for maximum likelihood estimates (denominator n) and "UB" to unbiased estimates (denominator n-1). The default will try to find the type used, by investigating which is most likely to result from integer valued datasets.

A vector of dichotemous responses used (e.g., c(-1,1) or c(0,1). Only needed when 'covs' is used.)

How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "listwise" for listwise deletion, or "pairwise" for pairwise deletion. NOT RECOMMENDED TO BE USED YET IN ISING MODEL.

A character vector indicating which matrices should be constrained equal across groups.

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation, "FIML" for full-information maximum likelihood estimation, "ULS" for unweighted least squares estimation, "WLS" for weighted least squares estimation, and "DWLS" for diagonally weighted least squares estimation. Only ML estimation is currently supported for the Ising model.

The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), "nloptr_TNEWTON" (preconditioned truncated Newton via nloptr), and "LBFGS++" (pure C++ L-BFGS-B). Defaults to "nlminb".

Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).

Optional WLS weights matrix. CURRENTLY NOT USED.

An optional sample statistics object. Mostly used internally.

Logical, should the model be identified?

Logical, should messages be printed?

The maximum number of nodes allowed in the analysis. This function will stop with an error if more nodes are used (it is not recommended to set this higher).

The minimum sum score that is artifically possible in the dataset. Defaults to -Inf. Set this only if you know a lower sum score is not possible in the data, for example due to selection bias.

Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).

Proportion of cases to be subsampled (round(boot_sub * N)).

Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)

Numeric penalty strength for penalized ML estimation (PML/PFIML). NA (default) triggers automatic selection via EBIC-based grid search when a penalized estimator is used; set to a specific numeric value to use a fixed penalty strength (0 = no penalty). See find_penalized_lambda and penalize.

Elastic net mixing parameter: 1 = LASSO (default), 0 = ridge.

Character vector of matrix names to penalize. If missing, defaults are selected based on the model type.

A psychonetrics model.

Logical, should all MIs be printed or only the highest?

Optional vector of matrices to include in the output.

String indicating which kind of modification index should be printed. ("mi" is the typical MI, "mi_free" is the modification index free from equality constrains across groups, and "mi_equal" is the modification index if the parameter is added constrained equal across all groups).

Number of MIs to include in output if all = FALSE

Logical, should messages be printed?

Logical, should only MIs be printed of non-zero parameters? Useful to explore violations of group equality.

The original psychonetrics object (not bootstrapped)

A list of bootstrapped psychonetrics objects (i.e., using bootstrap = TRUE)

Remove bootstraps that did not converge (sum of absolute gradient > 1)

The data as used by lvm

The factor loadings matrix *without* the bifactor, as used by by lvm

A vector of names of the latent variables, as used by lvm

Name of the bifactor

Arguments sent to lvm

A psychonetrics model.

Logical, should new samples be drawn with replacement?

Proportion of sample to be drawn. Set to lower than $1$ for subsampling.

Logical, should messages be printed?

Logical, should the bootstrapped data also be stored?

Logical, should the baseline and saturated models be included?

A psychonetrics model.

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

A sample variance–covariance matrix, or a list/array of such matrices for multiple groups. IMPORTANT NOTE: psychonetrics expects the maximum likelihood (ML) covariance matrix, which is NOT obtained from cov directly. Manually rescale the result of cov with (nobs - 1)/nobs to obtain the ML covariance matrix.

The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.

A vector of sample means, or a list/matrix containing such vectors for multiple groups.

An optional string indicating the name of the group variable in data.

How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "listwise" for listwise deletion, or "pairwise" for pairwise deletion.

Any number of psychonetrics models. Can be named to change the rownames of the output.

Output of the compare function.

A dataset

The sample size

Arguments sent to the cov function.

A 'psychonetrics' object

A custom fit function or the psychonetrics default fit function (default).

A custom Jacobian function or the psychonetrics default Jacobian function (default).

A custom Fischer information function or the psychonetrics default Fischer information function (default).

Should the numeric Jacobian be transposed?

Should a diagnostic plot be produced?

Should start values be perturbed (only used in development)

Numeric derivative method (default: Richardson)

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

Required argument. For wide-format data, this must be a *matrix* with each row indicating a variable and each column indicating a measurement. The matrix must be filled with names of the variables in the dataset corresponding to variable i at wave j. NAs can be used to indicate missing waves. The rownames of this matrix will be used as variable names. For long-format data, this should be a character vector of variable names in the dataset.

Data format: "auto" (default) auto-detects based on whether vars is a matrix (wide) or character vector (long). "wide" forces wide-format interpretation. "long" forces long-format interpretation with automatic reshape to wide.

Optional string indicating the subject/cluster ID variable in data. Required when datatype = "long" or when vars is a character vector.

Optional string indicating the time point / measurement occasion variable. If missing when datatype = "long", a sequential measurement variable is created per subject.

Standardization method: "none" (default), "z" for global z-scores per variable across all waves (uses the overall mean and standard deviation for each variable computed across all waves of data), "quantile" for quantile normalization across all waves, "z_per_wave" for independent z-scores per wave column (note: this imposes stationarity as means become 0 and variances become 1 at each wave).

Required argument. A model matrix encoding the factor loading structure. Each row indicates an indicator and each column a latent. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

The type of within-person latent contemporaneous model to be used.

The type of within-person residual model to be used.

The type of between-person latent model to be used.

The type of between-person residual model to be used.

A model matrix encoding the temporal relationships (transpose of temporal network). A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Can also be "full" for a full temporal network or "zero" for an empty temporal network.

Only used when within_latent = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_latent = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_latent = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_latent = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_latent = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Optional vector encoding the intercepts of the observed variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Optional vector encoding the means of the latent variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Logical, should the model be automatically identified?

Type of identification used. "loadings" to fix the first factor loadings to 1, and "variance" to fix the diagonal of the latent variable model matrix (sigma_zeta, lowertri_zeta, delta_zeta or kappa_zeta) to 1.

An optional character vector with names of the latent variables.

Deprecated. Use groupvar instead. An optional string indicating the name of the group variable in data.

An optional string indicating the name of the group variable in data. Replaces the deprecated groups argument; if both are supplied, groupvar takes precedence with a warning.

A sample variance–covariance matrix, or a list/array of such matrices for multiple groups. IMPORTANT NOTE: psychonetrics expects the maximum likelihood (ML) covariance matrix, which is NOT obtained from cov directly. Manually rescale the result of cov with (nobs - 1)/nobs to obtain the ML covariance matrix.

A sample correlation matrix, or a list/array of such matrices for multiple groups. When supplied, the matrix is treated as a covariance matrix with a warning (appropriate for standardized data). Requires nobs. Note: corinput = TRUE is not supported in dlvm1.

A vector of sample means, or a list/matrix containing such vectors for multiple groups.

The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.

Logical. Not supported in dlvm1() and will produce an error if set to TRUE.

Start value specification. Can be either a string or a psychonetrics model. If it is a string, "version2" indicates the latest version of start value computation, "version1" indicates start values as they were computed up to version 0.11, and "simple" indicate simple starting values. If this is a psychonetrics model the starting values will be based on the ouptut. This can be useful, for example, if you first estimate a model with matrices set to a Cholesky decomposition, then use those values as start values for estimating Gaussian graphical models.

How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "auto" (default) for automatic detection, "listwise" for listwise deletion, or "pairwise" for pairwise deletion. When "auto", the function checks for missing data and switches ML to FIML, PML to PFIML, or defaults to listwise for LS estimators.

A character vector indicating which matrices should be constrained equal across groups.

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation, "FIML" for full-information maximum likelihood estimation, "PML" for penalized maximum likelihood estimation, "PFIML" for penalized full-information maximum likelihood estimation, "ULS" for unweighted least squares estimation, "WLS" for weighted least squares estimation, and "DWLS" for diagonally weighted least squares estimation. When missing = "auto" (default), "ML" is automatically switched to "FIML" and "PML" to "PFIML" if missing data is detected.

The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), "nloptr_TNEWTON" (preconditioned truncated Newton via nloptr), and "LBFGS++" (pure C++ L-BFGS-B). Defaults to "nlminb".

Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).

Logical, should progress be printed to the console?

An optional sample statistics object. Mostly used internally.

If 'covs' is used, this is the type of covariance (maximum likelihood or unbiased) the input covariance matrix represents. Set to "ML" for maximum likelihood estimates (denominator n) and "UB" to unbiased estimates (denominator n-1). The default will try to find the type used, by investigating which is most likely to result from integer valued datasets.

What baseline model should be used? "stationary_random_intercept" includes both within- and between person variances constrained equal across time (default), "stationary" only includes within-person variances constrained equal across time, "independence" (default up to version 0.11) includes a variance for every variable at every time point (not constrained equal across time), and "none" includes no baseline model.

Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).

Proportion of cases to be subsampled (round(boot_sub * N)).

Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)

Optional alias for within_latent. If both within and within_latent are explicitly specified, within_latent takes precedence and a warning is issued.

Optional alias for between_latent. If both between and between_latent are explicitly specified, between_latent takes precedence and a warning is issued.

Numeric penalty strength for penalized ML estimation (PML/PFIML). NA (default) triggers automatic selection via EBIC-based grid search when a penalized estimator is used; set to a specific numeric value to use a fixed penalty strength (0 = no penalty). See find_penalized_lambda and penalize.

Elastic net mixing parameter: 1 = LASSO (default), 0 = ridge.

Character vector of matrix names to penalize. If missing, defaults are selected based on the model type.

Arguments sent to dlvm1.

Number of rows and columns in the original matrix

Logical indicating if the diagonal should be included (set to FALSE for derivative of vech(x))

A psychonetrics model.

Output of a dlvm1 model

Manual within-person variance-covariance matrix

Manual between-person variance-covariance matrix

Cutoff used to determine ergodicity

Logical, should the reference be printed?

Should ergodicity of observed or latent variables be plotted?

Should the between-persons variance-covariance matrix be based on exected cross-sectional or between-person relations

Not used

Ergodicity values of each component

Component loadings

Between persons subspace

Ergodic subspace

Within person subspace

Cutoff value used

Dataset to compute factor scores for

A psychonetrics model

The method to use: "regression" or "bartlett"

A psychonetrics model with estimator = "PML" or estimator = "PFIML". The model should contain parameters with penalty_lambda = NA (auto-select), which is the default when specifying estimator = "PML" or "PFIML" in model constructors such as ggm, lvm, etc.

Character string specifying the information criterion used to select the best lambda. One of:

"bic"

BIC (equivalent to EBIC with gamma = 0)

"ebic.25"

EBIC with gamma = 0.25

"ebic.5"

EBIC with gamma = 0.5 (default; recommended for network models)

"ebic.75"

EBIC with gamma = 0.75

"ebic1"

EBIC with gamma = 1.0

Higher gamma values penalize model complexity more strongly and tend to produce sparser solutions. Gamma = 0.5 is widely recommended for network models (Foygel & Drton, 2010).

Integer, the number of lambda values in the search grid. Default is 50. The grid is log-spaced between lambda_max and lambda_max * lambda_min_ratio.

Numeric, the ratio of the smallest to the largest lambda in the grid. Default is 1e-4, so that lambda_min = lambda_max * 1e-4.

Threshold for zeroing out small penalized parameter estimates. Parameters with absolute value below beta_min are set to zero, both during EBIC evaluation (for parameter counting) and in the final model. Can be:

"numerical"

Uses a fixed threshold of 1e-05 (default).

"theoretical"

Uses sqrt(log(p) / n) where p is the number of penalized parameters and n is the total sample size.

numeric value

Uses the provided value directly as the threshold.

Logical, should progress messages be printed? Defaults to the model's verbose setting.

A psychonetrics model.

A psychonetrics model.

String indicating the matrix of the parameter

Integer or string indicating the row of the matrix of the parameter

Integer or string indicating the column of the matrix of the parameter

Used in fixpar to indicate the value to which a parameters is constrained

Used in freepar to indicate the starting value of the parameter

Integer indicating the group of the parameter to be constrained

Logical, should messages be printed?

Logical, should the log be updated?

Logical, should the model be updated?

Logical, should the starting value be set at the expected parameter change?

Arguments sent to runmodel

A 'psychonetrics' model

The factor with which problematic parameters are reduced in each iteration.

Maximum difference between analytic and numeric gradient to be considered problematic.

Average absolute difference between analytic and numeric gradient that is considered acceptable.

Maximum number of iterations to attempt to fix starting values.

A psychonetrics model.

Number of cases to sample per group.

A psychonetrics model.

Logical, should standard errors be approximated? If true, an approximate matrix inverse of the Fischer information is used to obtain the standard errors.

A psychonetrics model.

String indicating the matrix to be extracted.

Integer indicating the group for the matrix to be extracted.

Logical. Should the matrix be thresholded (non-significant values set to zero? Can also be a value with an absolute threshold below wich parameters are set to zero.)

Significance level to use.

p-value adjustment method to use. See p.adjust.

Mode for adjusting for multiple comparisons. Should all parameters be considered as the total number of tests or only the tested parameters (parameters of interest)?

Set to FALSE to set diagonal elements to zero.

A psychonetrics model.

String indicating the matrix of the parameter

Integer or string indicating the row of the matrix of the parameter

Integer or string indicating the column of the matrix of the parameter

Logical, should messages be printed?

Logical, should the log be updated?

Logical, should the model be updated?

Logical, should the model be identified?

Arguments sent to runmodel

Different from in other psychonetrics models, this must be a *matrix* with each row indicating a variable and each column indicating a measurement. The matrix must be filled with names of the variables in the dataset corresponding to variable i at wave j. NAs can be used to indicate missing waves. The rownames of this matrix will be used as variable names.

A vector with the encoding of each measurement (e.g., 0, 1, 2, 3).

A vector with strings indicating names of between-person covariate variables in the data

Should covariates be included as regressions or actual covariates?

Arguments sent to lvm

A 'psychonetrics' object.

Logical, should the entry that the logbook is accessed be added?

An expression (code block) to evaluate on each iteration. Typically enclosed in curly braces {...}. The expression should return a single object (e.g., a psychonetrics model).

Number of replications (default: 1000).

Number of CPU cores to use. Set to 1 for sequential execution (default), or higher for parallel execution via makePSOCKcluster.

Character vector of additional variable names to export to parallel workers. Usually not needed because variables referenced in expr are auto-detected and exported.

Character vector of packages to load on parallel workers (default: c("psychonetrics", "dplyr")).

Logical; if TRUE (default), shows a progress bar via pbapply.

The environment in which to look up variables for auto-export and to evaluate the expression in sequential mode. Defaults to the calling environment.

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

A model matrix encoding the factor loading structure. Each row indicates an indicator and each column a latent. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

The type of latent model used. See description.

The type of residual model used. See description.

Only used when latent = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when latent = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when latent = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when latent = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when latent = "ggm". Either "diag" or "zero", or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "ggm". Either "diag" or "zero", or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

A model matrix encoding the structural relations between latent variables. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Optional vector encoding the intercepts of the observed variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Optional vector encoding the intercepts of the latent variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Optional matrix encoding the threshold parameters for ordinal variables. Each column corresponds to an observed variable and each row to a threshold. Use 0 for fixed elements, 1 for free elements, and higher integers for equality constrains. Typically set up automatically when ordered is specified.

Logical, should the model be automatically identified?

Type of identification used. "loadings" to fix the first factor loadings to 1, and "variance" to fix the diagonal of the latent variable model matrix (sigma_zeta, lowertri_zeta, delta_zeta or kappa_zeta) to 1.

An optional character vector encoding the variables used in the analysis. Must equal names of the dataset in data.

Character vector of variable names to treat as ordinal, or TRUE to treat all variables as ordinal. When ordinal variables are specified, polychoric correlations and thresholds are estimated from the data and the model is fit using the Muthen (1984) three-stage estimator. The estimator defaults to "DWLS" for ordinal models; "WLS" and "ULS" are also supported. Identification is automatically set to "variance".

An optional character vector with names of the latent variables.

Deprecated. Use groupvar instead. An optional string indicating the name of the group variable in data.

An optional string indicating the name of the group variable in data. Replaces the deprecated groups argument; if both are supplied, groupvar takes precedence with a warning.

A sample variance–covariance matrix, or a list/array of such matrices for multiple groups. Make sure covtype argument is set correctly to the type of covariances used.

A sample correlation matrix, or a list/array of such matrices for multiple groups. When supplied, the matrix is treated as a covariance matrix with a warning (appropriate for standardized data). Requires nobs. Note: corinput = TRUE is not supported in lvm; use varcov with type = "cor" for correlation-based models.

A vector of sample means, or a list/matrix containing such vectors for multiple groups.

The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.

Logical. Only supported for ordinal data in lvm(); defaults to FALSE for continuous data. Setting corinput = TRUE is not supported for lvm() and will produce an error; use varcov with type = "cor" instead.

How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "auto" (default) for automatic detection, "listwise" for listwise deletion, or "pairwise" for pairwise deletion. When "auto", the function checks for missing data and switches ML to FIML, PML to PFIML, or defaults to listwise for LS estimators.

A character vector indicating which matrices should be constrained equal across groups.

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation, "FIML" for full-information maximum likelihood estimation, "PML" for penalized maximum likelihood estimation, "PFIML" for penalized full-information maximum likelihood estimation, "ULS" for unweighted least squares estimation, "WLS" for weighted least squares estimation, and "DWLS" for diagonally weighted least squares estimation. "WLSMV" is accepted as a synonym for "DWLS" (both use DWLS estimation with a mean-and-variance adjusted scaled test statistic). When missing = "auto" (default), "ML" is automatically switched to "FIML" and "PML" to "PFIML" if missing data is detected. Defaults to "ML" for continuous data and "DWLS" when ordinal variables are specified via ordered.

The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), "nloptr_TNEWTON" (preconditioned truncated Newton via nloptr), and "LBFGS++" (pure C++ L-BFGS-B). Defaults to "nlminb".

Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).

Logical, should progress be printed to the console?

The weights matrix used in WLS estimation (experimental)

An optional sample statistics object. Mostly used internally.

If 'covs' is used, this is the type of covariance (maximum likelihood or unbiased) the input covariance matrix represents. Set to "ML" for maximum likelihood estimates (denominator n) and "UB" to unbiased estimates (denominator n-1). The default will try to find the type used, by investigating which is most likely to result from integer valued datasets.

Which standardization method should be used? "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution.

Logical, should simple start values be used for lambda? Setting this to TRUE can avoid some estimation problems.

Controls the starting values for the beta (structural regression) matrix. Can be "default" (default) for OLS-based starting values derived from the factor-analysis-implied latent covariance matrix, "simple" for the previous behavior (all free elements set to 0.001), or a fitted psychonetrics object to extract beta estimates as starting values. The OLS-based starts compute regression coefficients between latent variables using the FA-derived covariance matrix, which typically leads to faster convergence for models with structural paths. Guard rails automatically fall back to simple starts if numerical issues are detected (e.g., near-singular matrices, extreme coefficients).

Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).

Proportion of cases to be subsampled (round(boot_sub * N)).

Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)

Numeric penalty strength for penalized ML estimation (PML/PFIML). NA (default) triggers automatic selection via EBIC-based grid search when a penalized estimator is used; set to a specific numeric value to use a fixed penalty strength (0 = no penalty). See find_penalized_lambda and penalize.

Elastic net mixing parameter: 1 = LASSO (default), 0 = ridge.

Character vector of matrix names to penalize. If missing, defaults are selected based on the model type.

Arguments sent to varcov

A list of covariance or correlation matrices. Must contain rows and columns with NAs for variables not included in a study.

A vector with the number of observations per study.

Optional data frame. When supplied together with studyvar, covariance matrices and sample sizes are computed internally per study. Cannot be used together with covs, cors, or nobs.

A list of correlation matrices. When supplied, the matrices are treated as covariance matrices with a warning (appropriate for standardized data). Requires nobs.

A string indicating the column name in data that identifies the study. Required when data is supplied.

Deprecated. Use groupvar instead. Multi-group support is not yet included for meta-analytic models.

Not yet supported for meta-analytic models. Supplying this argument will produce an error.

Logical. Defaults to FALSE. Controls whether the input is treated as correlation matrices.

Optional list with 'V' matrices (sampling error variance approximations).

Which method should be used to approximate the sampling error variance?

How should the sampling error estimates be evaluated?

A matrix encoding the factor loading structure. Can be a pattern matrix with TRUE/FALSE or 0/1 entries, or a matrix with starting values and higher integer codes for equality constraints.

Structural (regression) matrix among latent variables. Defaults to "zero".

Parameterization of the latent covariance structure. One of "cov", "chol", "prec", or "ggm".

Latent covariance matrix specification (used when latent = "cov").

Latent precision matrix specification (used when latent = "prec").

Latent partial correlation matrix specification (used when latent = "ggm").

Latent Cholesky factor specification (used when latent = "chol").

Latent scaling matrix specification (used when latent = "ggm").

Parameterization of the residual covariance structure. One of "cov", "chol", "prec", or "ggm".

Residual covariance matrix specification (used when residual = "cov"). Defaults to "diag". Diagonal elements are always fixed (derived from the correlation constraint).

Residual precision matrix specification (used when residual = "prec").

Residual partial correlation matrix specification (used when residual = "ggm").

Residual Cholesky factor specification (used when residual = "chol").

Residual scaling matrix specification (used when residual = "ggm").

Logical, should the model be identified? Defaults to TRUE.

How to identify the model. "loadings" or "variance".

Parameterization of the random effects covariance structure.

Random effects covariance matrix specification (used when randomEffects = "cov").

Random effects precision matrix specification (used when randomEffects = "prec").

Random effects partial correlation matrix specification (used when randomEffects = "ggm").

Random effects Cholesky factor specification (used when randomEffects = "chol").

Random effects scaling matrix specification (used when randomEffects = "ggm").

Random effects correlation matrix specification (used when randomEffects = "cor").

Random effects standard deviation matrix specification (used when randomEffects = "cor").

Character vector of observed variable names. If missing, names are taken from the correlation matrices.

Character vector of latent variable names.

Logical indicating if baseline and saturated models should be included.

The optimizer to be used. Defaults to "nlminb".

The estimator to be used. "ML" or "FIML" (default).

Optional sample statistics object.

Logical, should progress be printed?

Should the data be bootstrapped?

Proportion of cases to subsample for bootstrap.

Logical, should bootstrap be with replacement?

A data frame containing time-series data for multiple studies. Use together with studyvar to identify studies.

A list of pre-computed Toeplitz covariance matrices (one per study). Each matrix should have dimension 2p x 2p where p is the number of variables, with the first p rows/columns being lagged variables and the second p being current variables. Alternative to data.

A vector with the number of observations per study. Required when covs is supplied.

Character vector of observed variable names. If missing, inferred from data.

A string indicating the column name in data that identifies the study. Required when data is supplied. If not supplied but idvar is, idvar is used as studyvar with a warning.

Optional string indicating the subject ID column within each study. When both studyvar and idvar are supplied, data is first split by studyvar, then idvar is used within each study to collate time series (as in var1).

Optional string indicating the day variable (passed to tsData per study).

Optional string indicating the beep variable within day (passed to tsData per study).

Parameterization of the contemporaneous (residual innovation) covariance structure. One of "cov", "chol", "prec", or "ggm".

Temporal lag-1 regression matrix specification. Defaults to "full" (all elements free, including diagonal autoregressive effects).

Contemporaneous partial correlation matrix specification (used when contemporaneous = "ggm").

Contemporaneous scaling matrix specification (used when contemporaneous = "ggm").

Contemporaneous precision matrix specification (used when contemporaneous = "prec").

Contemporaneous covariance matrix specification (used when contemporaneous = "cov").

Contemporaneous Cholesky factor specification (used when contemporaneous = "chol").

Parameterization of the random effects covariance structure.

Random effects covariance matrix specification (used when randomEffects = "cov").

Random effects precision matrix specification (used when randomEffects = "prec").

Random effects partial correlation matrix specification (used when randomEffects = "ggm").

Random effects Cholesky factor specification (used when randomEffects = "chol").

Random effects scaling matrix specification (used when randomEffects = "ggm").

Random effects correlation matrix specification (used when randomEffects = "cor").

Random effects standard deviation matrix specification (used when randomEffects = "cor").

Optional list with 'V' matrices (sampling error variance approximations).

Which method should be used to approximate the sampling error variance? "individual" or "pooled".

How should the sampling error estimates be evaluated? "averaged" or "per_study".

Logical indicating if baseline and saturated models should be included.

The optimizer to be used. Defaults to "nlminb".

The estimator to be used. "ML" or "FIML" (default).

Optional sample statistics object.

Logical, should progress be printed?

Should the data be bootstrapped?

Proportion of cases to subsample for bootstrap.

Logical, should bootstrap be with replacement?

Arguments sent to meta_var1.

A list of correlation matrices. Must contain rows and columns with NAs for variables not included in a study.

A vector with the number of observations per study.

Optional data frame. When supplied together with studyvar, correlation matrices and sample sizes are computed internally per study. Cannot be used together with cors, covs, or nobs.

A list of covariance matrices. Alternative to cors; when supplied, corinput defaults to FALSE.

A string indicating the column name in data that identifies the study. Required when data is supplied.

Deprecated. Use groupvar instead. Multi-group support is not yet included for meta-analytic models.

Not yet supported for meta-analytic models. Supplying this argument will produce an error.

Logical. Defaults to TRUE when cors is used, FALSE when covs is used. Controls whether the input is treated as correlation matrices.

Optional list with 'V' matrices (sampling error variance approximations).

Which method should be used to apprixomate the sampling error variance?

How should the sampling error estimates be evaluated?

What to model? Currently only "cor" and "ggm" are supported.

Only used when type = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "ggm". Either "diag" or "zero" (not recommended), or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "cor". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "cor". Either "diag" or "zero", or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

What to model for the random effects?

Only used when type = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when randomEffects = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when randomEffects = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when randomEffects = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when randomEffects = "ggm". Either "diag" or "zero", or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when randomEffects = "cor". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when randomEffects = "cor". Either "diag" or "zero", or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Variables to be included.

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), "nloptr_TNEWTON" (preconditioned truncated Newton via nloptr), and "LBFGS++" (pure C++ L-BFGS-B). Defaults to "nlminb".

The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation or "FIML" for full-information maximum likelihood estimation.

An optional sample statistics object. Mostly used internally.

Logical, should progress be printed to the console?

Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).

Proportion of cases to be subsampled (round(boot_sub * N)).

Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)

Arguments sent to meta_varcov

A data frame encoding the data used in the analysis. Must be a raw dataset.

A model matrix encoding the factor loading structure. Each row indicates an indicator and each column a latent. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Could also be the result of simplestructure.

A string indicating the variable in the dataset that describes group membership.

The type of within-person latent contemporaneous model to be used.

The type of within-person residual model to be used.

The type of between-person latent model to be used.

The type of between-person residual model to be used.

A model matrix encoding the within-cluster structural. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Defaults to "zero".

A model matrix encoding the between-cluster structural. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Defaults to "zero".

Only used when within_latent = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_latent = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_latent = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_latent = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_latent = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when within_residual = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_latent = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "ggm". Can be "full", "zero", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "ggm". Can be "diag", "zero" (not recommended), or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "prec". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "cov". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when between_residual = "chol". Can be "full", "diag", or a typical model matrix with 0s indicating parameters constrained to zero, 1s indicating free parameters, and higher integers indicating equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Optional vector encoding the intercepts of the observed variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Optional vector encoding the intercepts of the latent variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Logical, should the model be automatically identified?

Type of identification used. "loadings" to fix the first factor loadings to 1, and "variance" to fix the diagonal of the latent variable model matrix (sigma_zeta, lowertri_zeta, delta_zeta or kappa_zeta) to 1.

An optional character vector with names of the variables used.

An optional character vector with names of the latent variables.

An optional string indicating the name of the group variable in data.

A character vector indicating which matrices should be constrained equal across groups.

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

Estimator used. Currently only "FIML" is supported.

The optimizer to be used. Usually either "nlminb" (with box constrains) or "ucminf" (ignoring box constrains), but any optimizer supported by optimr can be used.

Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).

Logical, should progress be printed to the console?

Which standardization method should be used? "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution.

An optional sample statistics object. Mostly used internally.

Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).

Proportion of cases to be subsampled (round(boot_sub * N)).

Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)

Arguments sent to 'ml_lvm'

The data to be used. Must be raw data in long format (each row indicates one person at one time point).

Optional string indicating assessment beep per day. Adding this argument will cause non-consecutive beeps to be treated as missing!

String indicating the subject ID

Vectors of variables to include in the analysis

An optional string indicating the name of the group variable in data.

Estimator to be used. Must be "FIML".

Which standardization method should be used? "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution.

The type of within-person latent contemporaneous model to be used.

The type of between-person latent model to be used.

Arguments sent to dlvm1

A psychonetrics model.

String indicating the criterion to minimize. Any criterion from fit can be used.

Vector of strings indicating which matrices should be searched. Will default to network structures and factor loadings.

Minimal alpha used to consider edges to be removed

Maximum alpha used to consider edges to be added

Logical, should messages be printed?

Arguments sent to runmodel

A psychonetrics model.

A psychonetrics model.

Arguments sent to runmodel

Parameter indices of parameters to be constrained equal

Logical, should messages be printed?

Logical, should the log be updated?

Logical, should the model be updated?

A psychonetrics model.

Significance level to use.

Vector of strings indicating which matrices should be pruned. Will default to network structures.

Logical, should messages be printed?

Function used to combine models of different groups.

What model to return? "best" for best fitting model (according to BIC), "partialprune" for the partialpruned model, "union_equal" for the union model with equality constraints, and "prune" for the originally pruned model without equality constraints.

Should the lowest or the highest index of criterion be used to select the final model?

What criterion to use for the model selection in the last step? Defaults to "bic" for BIC selection.

Should the last prune step be based on removing edges not significant in the last model in the partialprune algorithm or the first model (saturated model) in the algorithm? Defaults to "saturated". Set to "partialprune" to mimic psychonetrics < 0.13.1 behavior.

Arguments sent to prune.

A psychonetrics model.

Character vector of matrix name(s) to penalize/unpenalize. If missing, defaults to the matrices returned by defaultPenalizeMatrices(x) for penalize, or all currently penalized matrices for unpenalize.

Integer or character indicating specific row(s) of the matrix. If missing, the entire matrix is penalized/unpenalized.

Integer or character indicating specific column(s) of the matrix. If missing, the entire matrix is penalized/unpenalized.

Numeric, the penalty strength per parameter. If missing, uses the global x@penalty$lambda.

Integer indicating specific group(s). If missing, all groups are included.

Logical, should messages be printed?

Logical, should the log be updated?

For refit: threshold below which penalized parameters are considered zero and fixed.

Additional arguments passed to runmodel in refit.

A psychonetrics model.

Significance level to use.

p-value adjustment method to use. See p.adjust.

Vector of strings indicating which matrices should be pruned. Will default to network structures.

Logical, should the model be evaluated after pruning?

Logical, should the pruning process be repeated?

Logical, should messages be printed?

Logical, should the log be updated?

Logical, should models be identified automatically?

A numeric value indicating a factor with which the starting values should be reduced. Can be useful when encountering numeric problems.

The maximum number of parameters to be pruned.

Mode for adjusting for multiple comparisons. Should all parameters be considered as the total number of tests or only the tested parameters (parameters of interest)?

Arguments sent to runmodel

A psychonetrics model.

Optional vector of matrices to include in MIs.

String indicating which modification indices should be updated.

Logical, should messages be printed?

Logical indicating if an analytic Fisher information matrix should be used.

Logical, should standard errors be approximated? If true, an approximate matrix inverse of the Fischer information is used to obtain the standard errors.

A psychonetrics model.

The part of the model to implement stationarity on.

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

Required argument. Different from in other psychonetrics models, this must be a *matrix* with each row indicating a variable and each column indicating a measurement. The matrix must be filled with names of the variables in the dataset corresponding to variable i at wave j. NAs can be used to indicate missing waves. The rownames of this matrix will be used as variable names.

A model matrix encoding the factor loading structure. Each row indicates an indicator and each column a latent. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

The type of model to model innovation

Logical, should progress be printed to the console?

Arguments sent to lvm

A psychonetrics model.

Level at which the model should be estimated. Defaults to "gradient" to indicate the analytic gradient should be used.

Logical, should fit measures be added?

Logical, should modification indices be added?

Logical, should standard errors be added?

Logical, should the Fisher information be added?

Logical, should the log be updated?

Logical, should messages be printed?

A list with options for optimr

Logical, should the analytic Fisher information be used? If FALSE, numeric information is used instead.

Should a result in which improper computation was used be return? Improper computation can mean that a pseudoinverse of small spectral shift was used in computing the inverse of a matrix.

Logical. Should a warning be given when at some point in the estimation a pseudoinverse was used?

Logical. Should a warning be given when the average absolute gradient is > 1?

Logical. Should bounded estimation be used (e.g., variances should be positive)?

Logical, should standard errors be approximated? If true, an approximate matrix inverse of the Fischer information is used to obtain the standard errors.

Should a warning be given when a parameter is estimated near its bounds?

Character string specifying the information criterion for automatic lambda selection in PML/PFIML models. One of "bic", "ebic.25", "ebic.5" (default), "ebic.75", or "ebic1". Only used when the model contains auto-select penalty parameters (penalty_lambda = NA). See find_penalized_lambda for details.

Threshold for zeroing out small penalized parameters during automatic lambda selection. Can be "numerical" (default; uses 1e-05), "theoretical" (uses sqrt(log(p)/n)), or a numeric value. See find_penalized_lambda for details.

A psychonetrics model.

A string indicating the estimator to be used

The optimizer to be used. The following optimizers are available:

R-based optimizers:

"nlminb"

Port trust-region Newton-like optimizer from base R. Uses analytic gradients and a trust-region method that adaptively controls step sizes, making it very stable for SEM problems. This is the default and recommended optimizer for most models.

"ucminf"

Unconstrained minimization using a quasi-Newton method (via the optimr package). Does not support box constraints. Fast for unconstrained problems.

C++ based optimizer:

"LBFGS++"

L-BFGS-B optimizer from the LBFGSpp library (Yixuan Qiu). This is a pure C++ implementation that computes the objective function and gradient in a single combined call, avoiding redundant computation via internal caching. Supports box constraints. Recommended when speed is important.

NLopt-based optimizer (via nloptr):

"nloptr_TNEWTON"

Preconditioned truncated Newton with restarts from the NLopt library (via the nloptr package). Uses the NLOPT_LD_TNEWTON_PRECOND_RESTART algorithm, which builds a local quadratic model of the objective and solves the Newton system approximately using a preconditioned conjugate-gradient method. Supports box constraints and uses analytic gradients. Can handle large parameter spaces efficiently. See Dembo & Steihaug (1982) for the underlying method.

Defaults to "nlminb".

Logical indicating if C++ should be used (currently only used in FIML)

List of arguments to sent to the optimizer.

A psychonetrics model.

Logical indicating if verbose should be enabled

A vector indicating which factor each indicator loads on.

A psychonetrics model.

Significance level to use.

String indicating the criterion to minimize. Any criterion from fit can be used.

Vector of strings indicating which matrices should be searched. Will default to network structures and factor loadings.

String indicating which kind of modification index should be used ("mi" is the typical MI, "mi_free" is the modification index free from equality constrains across groups, and "mi_equal" is the modification index if the parameter is added constrained equal across all groups).

String indicating which p-value adjustment should be used in greedy start. Any method from p.adjust can be used.

An R expression, using objects from fit, which will break stepup search if it evaluates to TRUE. For example, stopif = rmsea < 0.05 will lead to search to stop if rmsea is below 0.05.

Logical, should a greedy start be used? If TRUE, the first step adds any parameter that is significant (after adjustement)

Logical, should messages be printed?

Logical, should the Fisher information be checked for potentially non-identified models?

String indicating how to proceed if the information matrix is singular. "tryfix" will adjust starting values to try to fix the proble, "skip" will lead to the algorithm to skip the current parameter, "continue" will ignore the situation, and "stop" will break the algorithm and return a list with the last two models.

Logical, should the starting value be set at the expected parameter change?

Arguments sent to runmodel

A psychonetrics model

Named arguments with the new types to use (e.g., between = "ggm" or y = "cov")

Logical, should messages be printed?

Logical, should the model be stated to be uncomputed adter the transformation? In general, a model does not need to be re-computed as transformed parameters should be at the maximum likelihood estimate.

Logical, should a logbook entry be made?

Logical, should the model be identified after transforming?

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

A model matrix encoding the factor loading structure. Each row indicates an indicator and each column a latent. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

The type of contemporaneous model used. See description.

The type of residual model used. See description.

A model matrix encoding the temporal relationships (transpose of temporal network) between latent variables. A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Can also be "full" for a full temporal network or "zero" for an empty temporal network.

Only used when contemporaneous = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when contemporaneous = "ggm". Either "diag" or "zero" (not recommended), or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when contemporaneous = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when contemporaneous = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when contemporaneous = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "ggm". Either "diag" or "zero" (not recommended), or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when residual = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Optional vector encoding the intercepts of the observed variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Optional vector encoding the means of the latent variables. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Logical, should the model be automatically identified?

Type of identification used. "loadings" to fix the first factor loadings to 1, and "variance" to fix the diagonal of the latent variable model matrix (sigma_zeta, lowertri_zeta, delta_zeta or kappa_zeta) to 1.

An optional character vector with names of the latent variables.

Optional string indicating assessment beep per day. Adding this argument will cause non-consecutive beeps to be treated as missing!

Optional string indicating assessment day. Adding this argument makes sure that the first measurement of a day is not regressed on the last measurement of the previous day. IMPORTANT: only add this if the data has multiple observations per day.

Optional string indicating the subject ID

An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in data.

Deprecated. Use groupvar instead. An optional string indicating the name of the group variable in data.

An optional string indicating the name of the group variable in data. Replaces the deprecated groups argument; if both are supplied, groupvar takes precedence with a warning.

A sample variance–covariance matrix, or a list/array of such matrices for multiple groups. Make sure covtype argument is set correctly to the type of covariances used.

A sample correlation matrix, or a list/array of such matrices for multiple groups. When supplied, the matrix is treated as a covariance matrix with a warning (appropriate for standardized data). Requires nobs. Note: corinput = TRUE is not supported in tsdlvm1.

A vector of sample means, or a list/matrix containing such vectors for multiple groups.

The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.

Logical. Not supported in tsdlvm1() and will produce an error if set to TRUE.

How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "auto" (default) for automatic detection, "listwise" for listwise deletion, or "pairwise" for pairwise deletion. When "auto", the function checks for missing data and switches ML to FIML, PML to PFIML, or defaults to listwise for LS estimators.

A character vector indicating which matrices should be constrained equal across groups.

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation, "FIML" for full-information maximum likelihood estimation, "PML" for penalized maximum likelihood estimation, "PFIML" for penalized full-information maximum likelihood estimation, "ULS" for unweighted least squares estimation, "WLS" for weighted least squares estimation, and "DWLS" for diagonally weighted least squares estimation. When missing = "auto" (default), "ML" is automatically switched to "FIML" and "PML" to "PFIML" if missing data is detected.

The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), "nloptr_TNEWTON" (preconditioned truncated Newton via nloptr), and "LBFGS++" (pure C++ L-BFGS-B). Defaults to "nlminb".

Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).

Which standardization method should be used? "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution.

An optional sample statistics object. Mostly used internally.

Logical, should data be within-person centered?

If 'covs' is used, this is the type of covariance (maximum likelihood or unbiased) the input covariance matrix represents. Set to "ML" for maximum likelihood estimates (denominator n) and "UB" to unbiased estimates (denominator n-1). The default will try to find the type used, by investigating which is most likely to result from integer valued datasets.

Logical, should messages be printed?

Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).

Proportion of cases to be subsampled (round(boot_sub * N)).

Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)

Numeric penalty strength for penalized ML estimation (PML/PFIML). NA (default) triggers automatic selection via EBIC-based grid search when a penalized estimator is used; set to a specific numeric value to use a fixed penalty strength (0 = no penalty). See find_penalized_lambda and penalize.

Elastic net mixing parameter: 1 = LASSO (default), 0 = ridge.

Character vector of matrix names to penalize. If missing, defaults are selected based on the model type.

Arguments sent to tsdlvm1

A psychonetrics model.

Logical, should the model be updated?

Logical, should messages be printed?

Logical, should the log be updated?

Logical, should the model be identified?

Which matrices should be used to form the union/intersection model?

Arguments sent to runmodel

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

The type of contemporaneous model used. See description.

A model matrix encoding the temporal relationships (transpose of temporal network). A 0 encodes a fixed to zero element, a 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix. Can also be "full" for a full temporal network or "zero" for an empty temporal network.

Only used when contemporaneous = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when contemporaneous = "ggm". Either "diag" to estimate all scalings or "zero" (not recommended) to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when contemporaneous = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when contemporaneous = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when contemporaneous = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Optional vector encoding the mean structure. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free means, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Optional string indicating assessment beep per day. Adding this argument will cause non-consecutive beeps to be treated as missing!

Optional string indicating assessment day. Adding this argument makes sure that the first measurement of a day is not regressed on the last measurement of the previous day. IMPORTANT: only add this if the data has multiple observations per day.

Optional string indicating the subject ID

An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in data.

Deprecated. Use groupvar instead. An optional string indicating the name of the group variable in data.

An optional string indicating the name of the group variable in data. Replaces the deprecated groups argument; if both are supplied, groupvar takes precedence with a warning.

A sample variance–covariance matrix, or a list/array of such matrices for multiple groups. Make sure covtype argument is set correctly to the type of covariances used.

A sample correlation matrix, or a list/array of such matrices for multiple groups. When supplied, the matrix is treated as a covariance matrix with a warning (appropriate for standardized data). Requires nobs. Note: corinput = TRUE is not supported in var1.

A vector of sample means, or a list/matrix containing such vectors for multiple groups.

The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.

Logical. Not supported in var1() and will produce an error if set to TRUE.

How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "auto" (default) for automatic detection, "listwise" for listwise deletion, or "pairwise" for pairwise deletion. When "auto", the function checks for missing data and switches ML to FIML, PML to PFIML, or defaults to listwise for LS estimators.

A character vector indicating which matrices should be constrained equal across groups.

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation, "FIML" for full-information maximum likelihood estimation, "PML" for penalized maximum likelihood estimation, "PFIML" for penalized full-information maximum likelihood estimation, "ULS" for unweighted least squares estimation, "WLS" for weighted least squares estimation, and "DWLS" for diagonally weighted least squares estimation. When missing = "auto" (default), "ML" is automatically switched to "FIML" and "PML" to "PFIML" if missing data is detected.

The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), "nloptr_TNEWTON" (preconditioned truncated Newton via nloptr), and "LBFGS++" (pure C++ L-BFGS-B). Defaults to "nlminb".

Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).

Which standardization method should be used? "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution.

An optional sample statistics object. Mostly used internally.

If 'covs' is used, this is the type of covariance (maximum likelihood or unbiased) the input covariance matrix represents. Set to "ML" for maximum likelihood estimates (denominator n) and "UB" to unbiased estimates (denominator n-1). The default will try to find the type used, by investigating which is most likely to result from integer valued datasets.

Logical, should messages be printed?

Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).

Proportion of cases to be subsampled (round(boot_sub * N)).

Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)

Numeric penalty strength for penalized ML estimation (PML/PFIML). NA (default) triggers automatic selection via EBIC-based grid search when a penalized estimator is used; set to a specific numeric value to use a fixed penalty strength (0 = no penalty). See find_penalized_lambda and penalize.

Elastic net mixing parameter: 1 = LASSO (default), 0 = ridge.

Character vector of matrix names to penalize. If missing, defaults are selected based on the model type.

Arguments sent to var1

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

The type of model used. See description.

Only used when type = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "ggm". Either "diag" or "zero" (not recommended), or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "cor". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Only used when type = "cor". Either "diag" or "zero" (not recommended), or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

Optional vector encoding the mean structure. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free means, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

Optional list encoding the thresholds per variable.

An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in data.

Deprecated. Use groupvar instead. An optional string indicating the name of the group variable in data.

An optional string indicating the name of the group variable in data. Replaces the deprecated groups argument; if both are supplied, groupvar takes precedence with a warning.

A sample variance–covariance matrix, or a list/array of such matrices for multiple groups. Make sure covtype argument is set correctly to the type of covariances used.

A sample correlation matrix, or a list/array of such matrices for multiple groups. When supplied, corinput defaults to TRUE and the matrix is used in place of covs. Requires nobs.

A vector of sample means, or a list/matrix containing such vectors for multiple groups.

The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.

If 'covs' is used, this is the type of covariance (maximum likelihood or unbiased) the input covariance matrix represents. Set to "ML" for maximum likelihood estimates (denominator n) and "UB" to unbiased estimates (denominator n-1). The default will try to find the type used, by investigating which is most likely to result from integer valued datasets.

How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "auto" (default) for automatic detection, "listwise" for listwise deletion, or "pairwise" for pairwise deletion. When "auto", the function checks for missing data and switches ML to FIML, PML to PFIML, or defaults to listwise for LS estimators.

A character vector indicating which matrices should be constrained equal across groups.

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation, "FIML" for full-information maximum likelihood estimation, "PML" for penalized maximum likelihood estimation, "PFIML" for penalized full-information maximum likelihood estimation, "ULS" for unweighted least squares estimation, "WLS" for weighted least squares estimation, and "DWLS" for diagonally weighted least squares estimation. "WLSMV" is accepted as a synonym for "DWLS" (both use DWLS estimation with a mean-and-variance adjusted scaled test statistic). When missing = "auto" (default), "ML" is automatically switched to "FIML" and "PML" to "PFIML" if missing data is detected. Defaults to "ML" for continuous data and "DWLS" when ordinal variables are specified via ordered.

The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), "nloptr_TNEWTON" (preconditioned truncated Newton via nloptr), and "LBFGS++" (pure C++ L-BFGS-B). Defaults to "nlminb".

Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).

Which standardization method should be used? "none" (default) for no standardization, "z" for z-scores, and "quantile" for a non-parametric transformation to the quantiles of the marginal standard normal distribution.

Optional WLS weights matrix.

An optional sample statistics object. Mostly used internally.

Logical, should progress be printed to the console?

A vector with strings indicating the variables that are ordered catagorical, or set to TRUE to model all variables as ordered catagorical.

Logical, should the meanstructure be modeled explicitly?

Logical, is the input a correlation matrix?

Logical, should row-wise FIML be used? Not recommended!

Should the data be bootstrapped? If TRUE the data are resampled and a bootstrap sample is created. These must be aggregated using aggregate_bootstraps! Can be TRUE or FALSE. Can also be "nonparametric" (which sets boot_sub = 1 and boot_resample = TRUE) or "case" (which sets boot_sub = 0.75 and boot_resample = FALSE).

Proportion of cases to be subsampled (round(boot_sub * N)).

Logical, should the bootstrap be with replacement (TRUE) or without replacement (FALSE)

Numeric penalty strength for penalized ML estimation (PML/PFIML). Default is NA, which triggers automatic lambda selection via EBIC grid search when estimator = "PML" or "PFIML" (see find_penalized_lambda). Set to a specific numeric value (e.g., 0.1) for manual lambda, or 0 for no penalty.

Elastic net mixing parameter: 1 = LASSO (default), 0 = ridge.

Character vector of matrix names to penalize. If missing, defaults are selected based on the model type.

Arguments sent to varcov

A psychonetrics model.

Character string specifying the path to the output file. Defaults to "psychonetrics_output.txt".

Logical. Should the full estimated model matrices be included in the output? Defaults to TRUE. Set to FALSE for more compact output.

Logical. Should modification indices be included? Only included if modification indices have been computed via addMIs. Defaults to TRUE.

Logical. Should the model logbook be included? Defaults to TRUE.