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This function calculated the maximum-likelihood estimates parameters
● Data Source:
CranContrib
● Keywords:
● Alias: Predict
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This function is used to identify outliers in circular data sets. and with do the procedure Huberized on this outliers, the results improve. Huberizing the outliers will improve the results. circular and sd.circular are mean and standard deviation of circular data.
● Data Source:
CranContrib
● Keywords:
● Alias: Huberized
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Mean circular error
● Data Source:
CranContrib
● Keywords:
● Alias: MCE
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This function calculates the absolute values of the difference between the values of MCE and MCe statistic. Then, it draws the scatter plot of them and estimates the concentration parameter of k. Given the sample size and the estimated value of K, cut-off point from the table DMCE in the significance level of 0.05 or 0.1 will be found. Outliers are locatedon the top of teh line corresponding to the cut-off point.
● Data Source:
CranContrib
● Keywords:
● Alias: DMCEE
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The data used in here, recorded over a period of 22.7 days along the Holderness coastline(the Humberside coast of the North sea, UK) by using two different instruments: a high frequency (HF) radar system and an anchored wave buoy.
● Data Source:
CranContrib
● Keywords:
● Alias: wind2
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Removal of the ith observation from the data set calculate mean circular error for reduced data set
● Data Source:
CranContrib
● Keywords:
● Alias: MCe
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The data used in here, obtained by using Monte-Carlo simulation method.
● Data Source:
CranContrib
● Keywords:
● Alias: DMCE
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The data used in here, obtained after doing some calculations on the data to be recorded of Holderness coastline(the Humberside coast of the North sea, UK).
● Data Source:
CranContrib
● Keywords:
● Alias: wind
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providing 
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