Utility functions in CDM.
## requireNamespace with package message for needed installation
CDM_require_namespace(pkg)
## attach internal function in a package
cdm_attach_internal_function(pack, fun)## print function in summary
cdm_print_summary_data_frame(obji, from=NULL, to=NULL, digits=3, rownames_null=FALSE)
## print summary call
cdm_print_summary_call(object, call_name="call")
## print computation time
cdm_print_summary_computation_time(object, time_name="time", time_start="s1",
time_end="s2")
## string vector of matrix entries
cdm_matrixstring( matr, string )
## mvtnorm::rmvnorm with vector conversion for n=1
CDM_rmvnorm(n, mean=NULL, sigma, ...)
## fit univariate and multivariate normal distribution
cdm_fit_normal(x, w)
## fit unidimensional factor analysis by unweighted least squares
cdm_fa1(Sigma, method=1, maxit=50, conv=1E-5)
## another rbind.fill implementation
CDM_rbind_fill( x, y )
## fills a vector row-wise into a matrix
cdm_matrix2( x, nrow )
## fills a vector column-wise into a matrix
cdm_matrix1( x, ncol )
## SCAD thresholding operator
cdm_penalty_threshold_scad(beta, lambda, a=3.7)
## lasso thresholding operator
cdm_penalty_threshold_lasso(val, eta )
## ridge thresholding operator
cdm_penalty_threshold_ridge(beta, lambda)
## elastic net threshold operator
cdm_penalty_threshold_elnet( beta, lambda, alpha )
## SCAD-L2 thresholding operator
cdm_penalty_threshold_scadL2(beta, lambda, alpha, a=3.7)
## truncated L1 penalty thresholding operator
cdm_penalty_threshold_tlp( beta, tau, lambda )
## MCP thresholding operator
cdm_penalty_threshold_mcp(beta, lambda, a=3.7)
## general thresholding operator for regularization
cdm_parameter_regularization(x, regular_type, regular_lam, regular_alpha=NULL,
regular_tau=NULL )
## values of penalty function
cdm_penalty_values(x, regular_type, regular_lam, regular_tau=NULL,
regular_alpha=NULL)
## thresholding operators regularization
cdm_parameter_regularization(x, regular_type, regular_lam, regular_alpha=NULL,
regular_tau=NULL)
## utility functions for P-EM acceleration
cdm_pem_inits(parmlist)
cdm_pem_inits_assign_parmlist(pem_pars, envir)
cdm_pem_acceleration( iter, pem_parameter_index, pem_parameter_sequence, pem_pars,
PEM_itermax, parmlist, ll_fct, ll_args, deviance.history=NULL )
cdm_pem_acceleration_assign_output_parameters(res_ll_fct, vars, envir, update)
## approximation of absolute value function and its derivative
abs_approx(x, eps=1e-05)
abs_approx_D1(x, eps=1e-05)
## information criteria
cdm_calc_information_criteria(ic)
cdm_print_summary_information_criteria(object, digits_crit=0, digits_penalty=2)
## string pasting
cat_paste(...)
An R package
An R package
An R function
Object
Integer
Integer
Number of digits used for printing
Logical
Character
Character
Character
Character
Matrix
String
Object
Integer
Mean vector or matrix if separate means for cases are provided. In this case,
n can be missing.
Covariance matrix
More arguments to be passed (or a list of arguments)
Matrix or vector
Matrix or vector
Vector of sampling weights
Integer
Integer
Covariance matrix
Method 1 indicates estimation of different
item loadings, method 2 estimation of same item loadings.
Maximum number of iterations
Convergence criterion
Numeric
Regularization parameter
Regularization parameter
Parameter
Regularization parameter
Numeric
Regularization parameter
Type of regularization
Regularization parameter \(\lambda\)
Regularization parameter \(\tau\)
Regularization parameter \(\alpha\)
List containing parameters
Vector containing parameter names
Environment
Logical
Iteration number
List with parameter indices
List with updated parameter sequence
Maximum number of iterations for PEM
Name of log-likelihood function
Arguments of log-likelihood function
Deviance history, a data frame.
Result of maximized log-likelihood function
Vector containing parameter names
Numeric
List
Integer
Integer