Routine Name |
Mark of Introduction |
Purpose |
G07AAF
Example Text Example Data |
15 | nagf_univar_ci_binomial Computes confidence interval for the parameter of a binomial distribution |
G07ABF
Example Text Example Data |
15 | nagf_univar_ci_poisson Computes confidence interval for the parameter of a Poisson distribution |
G07BBF
Example Text Example Data |
15 | nagf_univar_estim_normal Computes maximum likelihood estimates for parameters of the Normal distribution from grouped and/or censored data |
G07BEF
Example Text Example Data |
15 | nagf_univar_estim_weibull Computes maximum likelihood estimates for parameters of the Weibull distribution |
G07BFF
Example Text Example Data |
23 | nagf_univar_estim_genpareto Estimates parameter values of the generalized Pareto distribution |
G07CAF
Example Text Example Data |
15 | nagf_univar_ttest_2normal Computes -test statistic for a difference in means between two Normal populations, confidence interval |
G07DAF
Example Text Example Data |
13 | nagf_univar_robust_1var_median Robust estimation, median, median absolute deviation, robust standard deviation |
G07DBF
Example Text Example Data |
13 | nagf_univar_robust_1var_mestim Robust estimation, -estimates for location and scale parameters, standard weight functions |
G07DCF
Example Text Example Data |
13 | nagf_univar_robust_1var_mestim_wgt Robust estimation, -estimates for location and scale parameters, user-defined weight functions |
G07DDF
Example Text Example Data |
14 | nagf_univar_robust_1var_trimmed Computes a trimmed and winsorized mean of a single sample with estimates of their variance |
G07EAF
Example Text Example Data |
16 | nagf_univar_robust_1var_ci Robust confidence intervals, one-sample |
G07EBF
Example Text Example Data |
16 | nagf_univar_robust_2var_ci Robust confidence intervals, two-sample |
G07GAF
Example Text Example Data |
23 | nagf_univar_outlier_peirce_1var Outlier detection using method of Peirce, raw data or single variance supplied |
G07GBF
Example Text Example Data |
23 | nagf_univar_outlier_peirce_2var Outlier detection using method of Peirce, two variances supplied |