References

ustat_var.varcovar(origX, origY[, w, quiet])	U-stat estimator of variance / covariance for teacher effects
ustat_var.ustat_samp_covar.ustat_samp_covar(...)	Estimates the sampling covariance between the estimate of \(\operatorname{Cov}(a^A, a^B)\) and the estimates of \(\operatorname{Cov}(a^C, a^D)\).
ustat_var.ustat_samp_covar.ustat_samp_covar_fast(...)	Estimates the sampling covariance between the estimate of \(\operatorname{Cov}(a^A, a^B)\) and the estimates of \(\operatorname{Cov}(a^C, a^D)\).
ustat_var.ustat_samp_covar.vcv_samp_covar_XXXX(Xtmp)	Estimates the sampling \(Var(Var(a^X))\).
ustat_var.ustat_samp_covar.vcv_samp_covar_XXXY(...)	Estimates sampling \(Cov(Var(a^X), Cov(a^X,a^Y))\).
ustat_var.ustat_samp_covar.vcv_samp_covar_XYXY(...)	Estimates sampling \(Var(Cov(a^X, a^Y))\).
ustat_var.ustat_samp_covar.vcv_samp_covar_XXYY(...)	Estimates the sampling \(Cov(Var(a^X), Var(a^Y))\).
ustat_var.sampc
ustat_var.makec.makec(X, Y[, w])	Generates C-weights for U-statistic estimator.
ustat_var.makec.makec_spec(X[, w])	Generates C-weights for U-statistic estimator in special case when X = Y.
ustat_var.lamb_sum
ustat_var.lamb_sum.lamb_sum_spec(X, C_jjX, C_jkX)	Computes special case bias-corrected product \(\lambda = (\sum_{k \neq i} C_{ik}^X a^X)^2\).
ustat_var.generate_test_data.generate_unique_nan_arrays(...)	Generates unique arrays with either balanced or unbalanced NaN patterns.
ustat_var.generate_test_data.generate_data(...)	Generates n_arrays arrays of size (n_teachers, n_time), all with fixed variance and covariance structure.