Neu-DA is a handy and reliable Excel addin designed to provide measurements for distance correlation and maximal information coefficient, thus enabling you to detect functional dependencies between variables.
Neu-DA is able to identify associations and to calculate the Maximum Information Coefficient (MIC).
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Neu-DA Crack+ Download [Updated-2022]
Neu-DA is a handy and reliable Excel addin designed to provide measurements for distance correlation and maximal information coefficient, thus enabling you to detect functional dependencies between variables.
Neu-DA is able to identify associations and to calculate the Maximum Information Coefficient (MIC).
Neu-DA the Quick Start
Neu-DA provides a dashboard where each dashboard has four worksheets that are related to your dataset.
The Neu-DA Dashboards:
The Neu-DA Dashboards have the following four worksheets.
1. Scatterplot
2. Distance correlation and MIC worksheet
3. Visualization pane
4. Help
See
Screenshot of the Neu-DA Dashboard
1. Scatterplot
2. Distance correlation and MIC worksheet
3. Visualization pane
4. Help
This is a quick start to understanding Neu-DA.
Create a new spreadsheet and copy the code provided below into a new spreadsheet.
See
CODE
‘Variables
Dim x_All As Range
Dim y_All As Range
Dim x_All2 As Range
Dim y_All2 As Range
‘ Create a new worksheet
Dim ws As Worksheet
On Error Resume Next
With Worksheets(“Resultate”)
.UsedRange
End With
‘ Set variables
Set x_All = Range(“A11:C22”)
Set y_All = Range(“D11:D22”)
Set x_All2 = Range(“B11:B22”)
Set y_All2 = Range(“C11:C22”)
‘ Copy the Data
x_All.Copy
y_All.Copy
x_All2.Copy
y_All2.Copy
‘ Calculate the Distance correlation and the MIC
wks = Worksheets(“Resultate”)
Formula = “=DistanceCorrelation(” & wks.Name & “!” & Range(“C11”) & “,” & Range(“C22”) & “,” & Range(“D11”) & “,” & Range(“D22”) & “)”
MsgBox “The
Neu-DA Crack + [32|64bit]
Excel Addin Compatible with Excel 2007, 2010, 2013, 2016, 365 & 365
Add NeuDA to Excel worksheet, it appears in the Data menu as an Addin.
The dll file should be placed in your Excel folder.
At Excel/Data Menu, choose from Addins menu to open it.
There will be the NeuDA menu when Excel is started, select the NeuDA item to use it.
To add a new NeuDA measurement to an Excel worksheet, go to the Data menu and select NeuDA item from the Addins menu.
Click the NeuDA button to open the NeuDA Parameters dialog box.
Enter the parameters to identify the correlation to measure, which are:
– Variable 1 (From Left to Right): Type of measurement to use
– Variable 2 (From Top to Bottom): Type of measurement to use
– Scaling factor (From Left to Right & Top to Bottom): Type of measurement to use
In the NeuDA dialog box, set these parameters, then select Apply to open the list of available measurements.
The list of available measurements is in a dialog box with the help of the user.
In the first column of the list (Type), the available measurements are listed from Left to Right.
Select a measurement and in the column (Scaling) the applicable scaling factor (X, Y, or Z).
Next, select from the list of required measurements, the one corresponding to your measurement.
For example, if you require X/Y type, select X/Y from the dropdown list of the Type column.
Also, the column (Scaling) can be filled automatically with the selected scaling factor (X, Y, or Z).
After selecting the measurement, select OK to open the dialog box, when all the parameters are filled.
Select OK to create the measurement.
To apply the measurement to your worksheet, select the worksheet and go to the Data menu, select the NeuDA item and then OK.
The result will appear in the appropriate cell of the worksheet.
Notes:
– There are no error handling in case the NeuDA dialog box is not opened.
If the NeuDA dialog box is opened but you don’t enter all the required data, the dialog box will remain opened until you enter the missing data.
– All calculation are not definite.
Every calculation
09e8f5149f
Neu-DA Crack License Code & Keygen
It is a utility spreadsheet for Microsoft Excel.
Kudos. I’m in the same boat. I’ve done all the work you describe, but with a Python implementation.
I’m still interested in the functionality discussed here:
A:
I found a great package for Python:
Python MIC Calculator
It has a very user-friendly GUI and works for small and big samples.
A:
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–
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What’s New in the?
Neu-DA is an Excel Addin devoted to detecting Pearson’s correlation as well as the Maximum Information Coefficient (MIC).
To calculate the MIC, it is necessary to have two data sets or more. The first one is the reference set and the other one is the query set.
The query set contains data that needs to be compared against the reference set.
Then, using this data set, the similarity of the reference set to the query set can be measured.
Each sample of the reference set is associated with a score of similarity to each sample of the query set.
Finally, the similarity of each sample in the reference set to the query set can be measured with the mean of these scores.
On the spreadsheet it looks like this:
Here, the sample A0 is the one that represents the reference set, while the sample A3 is the one that represents the query set. The formula to calculate the MIC between these samples is:
=HargreavesMIC(A0,A3)
You can find the explanation of HargreavesMIC in the Microsoft Excel Help.
Pearson’s correlation coefficient (r) and Fisher’s Z transformation is a measurement used for interdependence.
Just like the MIC, its calculation is based on two data sets, one of them is called reference set (R) and the other one is called query set (Q). These two sets consist of a pair of variables.
The r(R, Q) gives a score to the similarity between R and Q.
The difference between the r and MIC is that r is used to calculate the correlation between two variables, while MIC is used to calculate the association between two variables.
Also, r and MIC have different values and ranges.
To calculate the r, the formulas are:
r = sqrt(D[1]*D[2]);
Where,
D[1] is the difference between the actual and the expected values of the variable
D[2] is the difference between the actual and the expected values of the other variable
The expected value is the expected value in case where there is no association between the two variables.
If there is an association between the variables, the expected value of the variable that depends on the other one is lower than its actual value.
The MIC is used when r is not enough. As we have seen, the MIC is determined by the values of the variables.
MIC should be calculated and compared between
System Requirements For Neu-DA:
NOTE: All DLC items are only available in the US and Canada. No other languages are supported at this time.
Microsoft Windows
Minimum:
OS: Windows 7 64-bit
Processor: Intel Core 2 Duo E8600 (2.66 GHz) or AMD Phenom II X2 5500
Memory: 4 GB RAM
Graphics: NVIDIA Geforce 8800 GT 512MB or AMD Radeon HD 5670 1GB
DirectX: Version 9.0
Network: Broadband Internet connection
Hard Drive:
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