## RISE item fit statistic of two models
IRT.RISE( mod_p, mod_np, use_probs=TRUE )

## information about used package version
tam_packageinfo(pack)
## call statement in a string format
tam_print_call(CALL)
## information about R session
tam_rsessinfo()
## grep list of arguments for a specific variable
tam_args_CALL_search(args_CALL, variable, default_value)
## requireNamespace with message of needed installation
require_namespace_msg(pkg)
## add leading zeroes
add.lead(x, width=max(nchar(x)))
## round some columns in a data frame
tam_round_data_frame(obji, from=1, to=ncol(obji), digits=3, rownames_null=FALSE)
## round some columns in a data frame and print this data frame
tam_round_data_frame_print(obji, from=1, to=ncol(obji), digits=3, rownames_null=FALSE)
## copy of CDM::osink
tam_osink(file, suffix="__SUMMARY.Rout")
## copy of CDM::csink
tam_csink(file)

## base::matrix function with argument value byrow=TRUE
tam_matrix2(x, nrow=NULL, ncol=NULL)
## more efficient base::outer functions for operations "*", "+" and "-"
tam_outer(x, y, op="*")
## row normalization of a matrix
tam_normalize_matrix_rows(x)
## row normalization of a vector
tam_normalize_vector(x)
## aggregate function for mean and sum based on base::rowsum
tam_aggregate(x, group, mean=FALSE, na.rm=TRUE)
## column index when a value in a matrix is exceeded (used in TAM::tam.pv)
tam_interval_index(matr, rn)
## cumulative sum of row entries in a matrix
tam_rowCumsums(matr)
## extension of mvtnorm::dmvnorm to matrix entries of mean
tam_dmvnorm(x, mean, sigma, log=FALSE )
## Bayesian bootstrap in TAM (used in tam.pv.mcmc)
tam_bayesian_bootstrap(N, sample_integers=FALSE, do_boot=TRUE)
## weighted covariance matrix
tam_cov_wt(x, wt=NULL, method="ML")
## weighted correlation matrix
tam_cor_wt(x, wt=NULL, method="ML")
## generalized inverse
tam_ginv(x, eps=.05)
## generalized inverse with scaled matrix using MASS::ginv
tam_ginv_scaled(x, use_MASS=TRUE)

## remove items or persons with complete missing entries
tam_remove_missings( dat, items, elim_items=TRUE, elim_persons=TRUE )
## compute AXsi given A and xsi
tam_AXsi_compute(A, xsi)
## fit xsi given A and AXsi
tam_AXsi_fit(A, AXsi)

## maximum absolute difference between objects
tam_max_abs( list1, list2, label )
tam_max_abs_list( list1, list2)

## trimming increments in iterations
tam_trim_increment(increment, max.increment, trim_increment="cut",
     trim_incr_factor=2, eps=1E-10, avoid_na=FALSE)
## numerical differentiation by central difference
tam_difference_quotient(d0, d0p, d0m, h)
## assign elements of a list in an environment
tam_assign_list_elements(x, envir)