G07 (univar) Chapter Introduction – a description of the Chapter and an overview of the algorithms available
Routine Name |
Mark of Introduction |
Purpose |
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15 | nagf_univar_ci_binomial Computes confidence interval for the parameter of a binomial distribution |
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15 | nagf_univar_ci_poisson Computes confidence interval for the parameter of a Poisson distribution |
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15 | nagf_univar_estim_normal Computes maximum likelihood estimates for parameters of the Normal distribution from grouped and/or censored data |
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15 | nagf_univar_estim_weibull Computes maximum likelihood estimates for parameters of the Weibull distribution |
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23 | nagf_univar_estim_genpareto Estimates parameter values of the generalized Pareto distribution |
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15 | nagf_univar_ttest_2normal Computes -test statistic for a difference in means between two Normal populations, confidence interval |
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13 | nagf_univar_robust_1var_median Robust estimation, median, median absolute deviation, robust standard deviation |
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13 | nagf_univar_robust_1var_mestim Robust estimation, -estimates for location and scale parameters, standard weight functions |
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13 | nagf_univar_robust_1var_mestim_wgt Robust estimation, -estimates for location and scale parameters, user-defined weight functions |
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14 | nagf_univar_robust_1var_trimmed Computes a trimmed and winsorized mean of a single sample with estimates of their variance |
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16 | nagf_univar_robust_1var_ci Robust confidence intervals, one-sample |
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16 | nagf_univar_robust_2var_ci Robust confidence intervals, two-sample |
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23 | nagf_univar_outlier_peirce_1var Outlier detection using method of Peirce, raw data or single variance supplied |
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23 | nagf_univar_outlier_peirce_2var Outlier detection using method of Peirce, two variances supplied |