g07 Chapter Introduction – a description of the Chapter and an overview of the algorithms available
| Function Name |
Mark of Introduction |
Purpose |
| g07aac
Example Text Example Data |
7 | nag_binomial_ci Computes confidence interval for the parameter of a binomial distribution |
| g07abc
Example Text Example Data |
7 | nag_poisson_ci Computes confidence interval for the parameter of a Poisson distribution |
| g07bbc
Example Text Example Data |
7 | nag_censored_normal Computes maximum likelihood estimates for parameters of the Normal distribution from grouped and/or censored data |
| g07bec
Example Text Example Data |
7 | nag_estim_weibull Computes maximum likelihood estimates for parameters of the Weibull distribution |
| g07bfc
Example Text Example Data |
9 | nag_estim_gen_pareto Estimates parameter values of the generalized Pareto distribution |
| g07cac
Example Text Example Data |
4 | nag_2_sample_t_test Computes -test statistic for a difference in means between two Normal populations, confidence interval |
| g07dac
Example Text Example Data |
3 | nag_median_1var Robust estimation, median, median absolute deviation, robust standard deviation |
| g07dbc
Example Text Example Data |
4 | nag_robust_m_estim_1var Robust estimation, -estimates for location and scale parameters, standard weight functions |
| g07dcc
Example Text Example Data |
7 | nag_robust_m_estim_1var_usr Robust estimation, -estimates for location and scale parameters, user-defined weight functions |
| g07ddc
Example Text Example Data |
4 | nag_robust_trimmed_1var Trimmed and winsorized mean of a sample with estimates of the variances of the two means |
| g07eac
Example Text Example Data |
7 | nag_rank_ci_1var Robust confidence intervals, one-sample |
| g07ebc
Example Text Example Data |
7 | nag_rank_ci_2var Robust confidence intervals, two-sample |
| g07gac
Example Text Example Data |
23 | nag_outlier_peirce Outlier detection using method of Peirce, raw data or single variance supplied |
| g07gbc
Example Text Example Data |
23 | nag_outlier_peirce_two_var Outlier detection using method of Peirce, two variances supplied |