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Registration pull request created: JuliaRegistries/General/38987

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git tag -a v0.0.1 -m "<description of version>" 11dbc05bab3aa47306ea5ab747ad70086807b4b8
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Registration pull request updated: JuliaRegistries/General/38987

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

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git tag -a v0.0.1 -m "<description of version>" 1b122ed4cb37073376c948e3bbf0602feee7e621
git push origin v0.0.1

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Registration pull request created: JuliaRegistries/General/39037

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git tag -a v0.1.0 -m "<description of version>" e331f331dc75b54b9ffd4acfd6b766cf5461c8cc
git push origin v0.1.0

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Registration pull request updated: JuliaRegistries/General/39037

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

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git tag -a v0.1.0 -m "<description of version>" 91fdf22cc333990e7bb35508c7d37ed0915c894a
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Registration pull request created: JuliaRegistries/General/40176

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git tag -a v0.1.1 -m "<description of version>" 35c0cd24ae2869f177eb43c76769f3136cd08746
git push origin v0.1.1

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Registration pull request created: JuliaRegistries/General/50652

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git tag -a v0.2.0 -m "<description of version>" d900485364553345aee2b889097c88ab58c1047b
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Registration pull request created: JuliaRegistries/General/103526

Tip: Release Notes

Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.

@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v1.0.0 -m "<description of version>" 3ec954e796434379e32ccc4589dcc9b1cafd92f1
git push origin v1.0.0

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Release notes:

Latest Update
v1.0
----- New entropy methods -----
Two new base entropy functions (and their multiscale versions) have been added:

Diversity Entropy
Range Entropy

----- New fuzzy membership functions -----
Several new fuzzy membership functions have been added to FuzzEn, XFuzzEn and FuzzEn2D to provide more options for mapping the degree of similarity between embedding vectors.
These include trapezoidal, triangular and gaussian, among others.
Further info on these membership functions can be found here.

----- Phase Permutation Entropy -----
A new variant - 'phase' permutation entropy - has been added to PermEn.
This method employs a hilbert transformation of the data sequence, based on the methods outlined here.

----- Cross-Entropy with different length sequences -----
EntropyHub v1.0 now allows for cross-entropy (and multiscale cross-entropy) estimation with different length signals (except XCondEn and XPermEn).
As a result, the new cross-entropy functions require a separate input for each sequence (Sig1, Sig2).

----- Refined-Composite Multiscale Fuzzy Entropy -----
In addition to the refined-composite multiscale sample entropy that was available in earlier versions, now one can estimate the refined-composite multiscale fuzzy entropy based on the method outlined here.
What's more, refined-composite multicale cross-fuzzy entropy is also available, and both can be estimated using any of the fuzzy membership functions in FuzzEn or XFuzzEn.

----- Generalized Multiscale Entropy -----
Generaized multiscale entropy and generalized multiscale cross-entropy can now be estimated. Just choose the 'generalized' as the graining procedure in MSEn or XMSEn.

----- Variance of sample entropy estimate -----
Based on the method outlined by Lake et al., it is now possible to obtain a measure of the variance in the sample entropy estimate.
This is achieved by approximating the number of overlapping embedding vectors.
To do so, just set the parameter 'Vcp'==true in SampEn and XSampEn, but note that doing so requires a lot of computer memory.

Several little bugs and inconsistencies have also been fixed in this release. We want to thank all of you who have identified and alerted us to these bugs.
Most of these bugs have been noted via the GitHub issues portal.

----- Bug fixes -----

• The DispEn2D function in python has now fixed Bug in _DispEn2D.py EntropyHub#8.
• The type hint for FuzzEn in python has been updated The type hint of the parameter r of EntropyHub._FuzzEn.FuzzEn is confusing. EntropyHub#1.
• Compatibility Issues #3 are now resolved.
• A bug in the K2En python function led to incorrect entropy estimates for data sequences with many equal values. This has been corrected.

----- Other Changes -----

• The 'equal' method for discretizing data in DispEn and DispEn2D has been updated to be consistent across Python, MatLab and Julia. This is unlikely to have impacted any users previously.
• The zeroth dimension (m=0) estimate of ApEn and XApEn has been changed to -phi(1).
• The default radius threshold distance for XApEn, XSampEn and XK2En has been changed to use the pooled standard deviation [i.e. 0.2*SDpooled(X,Y)].

[JuliaRegistrator]

Registration pull request updated: JuliaRegistries/General/103526

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v1.0.0 -m "<description of version>" a820eb6d9b0312d9181fda34a41aa8d614d2312a
git push origin v1.0.0

[MattWillFlood]

@JuliaRegistrator register

Release notes:

v1.0

----- New entropy methods -----
Two new base entropy functions (and their multiscale versions) have been added:
> Diversity Entropy
> Range Entropy

----- New fuzzy membership functions -----
Several new fuzzy membership functions have been added to FuzzEn, XFuzzEn and FuzzEn2D to provide more options for mapping the degree of similarity between embedding vectors.
These include trapezoidal, triangular and gaussian, among others.
Further info on these membership functions can be found here.

----- Phase Permutation Entropy -----
A new variant - 'phase' permutation entropy - has been added to PermEn.
This method employs a hilbert transformation of the data sequence, based on the methods outlined here.

----- Cross-Entropy with different length sequences -----
EntropyHub v1.0 now allows for cross-entropy (and multiscale cross-entropy) estimation with different length signals (except XCondEn and XPermEn).
As a result, the new cross-entropy functions require a separate input for each sequence (Sig1, Sig2).

----- Refined-Composite Multiscale Fuzzy Entropy -----
In addition to the refined-composite multiscale sample entropy that was available in earlier versions, now one can estimate the refined-composite multiscale fuzzy entropy based on the method outlined here.
What's more, refined-composite multicale cross-fuzzy entropy is also available, and both can be estimated using any of the fuzzy membership functions in FuzzEn or XFuzzEn.

----- Generalized Multiscale Entropy -----
Generaized multiscale entropy and generalized multiscale cross-entropy can now be estimated. Just choose the 'generalized' as the graining procedure in MSEn or XMSEn.

----- Variance of sample entropy estimate -----
Based on the method outlined by Lake et al., it is now possible to obtain a measure of the variance in the sample entropy estimate.
This is achieved by approximating the number of overlapping embedding vectors.
To do so, just set the parameter 'Vcp'==true in SampEn and XSampEn, but note that doing so requires a lot of computer memory.

Several little bugs and inconsistencies have also been fixed in this release. We want to thank all of you who have identified and alerted us to these bugs.
Most of these bugs have been noted via the GitHub issues portal.

----- Bug fixes -----
- The DispEn2D function in python has now fixed this issue.
- The type hint for FuzzEn in python has been updated as requested.
- Compatbility issues with EntropyHub.jl are now resolved.
- A bug in the K2En python function led to incorrect entropy estimates for data sequences with many equal values. This has been corrected.

----- Other Changes -----
- The 'equal' method for discretizing data in DispEn and DispEn2D has been updated to be consistent across Python, MatLab and Julia. This is unlikely to have impacted any users previously.
- The zeroth dimension (m=0) estimate of ApEn and XApEn has been changed to -phi(1).
- The default radius threshold distance for XApEn, XSampEn and XK2En has been changed to use the pooled standard deviation [i.e. 0.2*SDpooled(X,Y)].

[JuliaRegistrator]

Registration pull request updated: JuliaRegistries/General/103526

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v1.0.0 -m "<description of version>" e00f2b836c482507148166cb1a7c56720155a36b
git push origin v1.0.0

[MattWillFlood]

@JuliaRegistrator register

Release notes:

v1.0
----- New entropy methods -----
Two new base entropy functions (and their multiscale versions) have been added:

Diversity Entropy
Range Entropy

----- New fuzzy membership functions -----
Several new fuzzy membership functions have been added to FuzzEn, XFuzzEn and FuzzEn2D to provide more options for mapping the degree of similarity between embedding vectors.
These include trapezoidal, triangular and gaussian, among others.
Further info on these membership functions can be found here.

----- Phase Permutation Entropy -----
A new variant - 'phase' permutation entropy - has been added to PermEn.
This method employs a hilbert transformation of the data sequence, based on the methods outlined here.

----- Cross-Entropy with different length sequences -----
EntropyHub v1.0 now allows for cross-entropy (and multiscale cross-entropy) estimation with different length signals (except XCondEn and XPermEn).
As a result, the new cross-entropy functions require a separate input for each sequence (Sig1, Sig2).

----- Refined-Composite Multiscale Fuzzy Entropy -----
In addition to the refined-composite multiscale sample entropy that was available in earlier versions, now one can estimate the refined-composite multiscale fuzzy entropy based on the method outlined here.
What's more, refined-composite multicale cross-fuzzy entropy is also available, and both can be estimated using any of the fuzzy membership functions in FuzzEn or XFuzzEn.

----- Generalized Multiscale Entropy -----
Generaized multiscale entropy and generalized multiscale cross-entropy can now be estimated. Just choose the 'generalized' as the graining procedure in MSEn or XMSEn.

----- Variance of sample entropy estimate -----
Based on the method outlined by Lake et al., it is now possible to obtain a measure of the variance in the sample entropy estimate.
This is achieved by approximating the number of overlapping embedding vectors.
To do so, just set the parameter 'Vcp'==true in SampEn and XSampEn, but note that doing so requires a lot of computer memory.

Several little bugs and inconsistencies have also been fixed in this release. We want to thank all of you who have identified and alerted us to these bugs.
Most of these bugs have been noted via the GitHub issues portal.

----- Bug fixes -----

• The DispEn2D function in python has now fixed Bug in _DispEn2D.py EntropyHub#8.
• The type hint for FuzzEn in python has been updated The type hint of the parameter r of EntropyHub._FuzzEn.FuzzEn is confusing. EntropyHub#1.
• Compatibility Issues #3 are now resolved.
• A bug in the K2En python function led to incorrect entropy estimates for data sequences with many equal values. This has been corrected.

----- Other Changes -----

• The 'equal' method for discretizing data in DispEn and DispEn2D has been updated to be consistent across Python, MatLab and Julia. This is unlikely to have impacted any users previously.
• The zeroth dimension (m=0) estimate of ApEn and XApEn has been changed to -phi(1).
• The default radius threshold distance for XApEn, XSampEn and XK2En has been changed to use the pooled standard deviation [i.e. 0.2*SDpooled(X,Y)].

[JuliaRegistrator]

Registration pull request updated: JuliaRegistries/General/103526

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v1.0.0 -m "<description of version>" f9dc31668e63727c8e5ad4754266fdeba65463df
git push origin v1.0.0

[MattWillFlood]

@JuliaRegistrator register

Release notes:

v1.0
----- New entropy methods -----
Two new base entropy functions (and their multiscale versions) have been added:

Diversity Entropy
Range Entropy

----- New fuzzy membership functions -----
Several new fuzzy membership functions have been added to FuzzEn, XFuzzEn and FuzzEn2D to provide more options for mapping the degree of similarity between embedding vectors.
These include trapezoidal, triangular and gaussian, among others.
Further info on these membership functions can be found here.

----- Phase Permutation Entropy -----
A new variant - 'phase' permutation entropy - has been added to PermEn.
This method employs a hilbert transformation of the data sequence, based on the methods outlined here.

----- Cross-Entropy with different length sequences -----
EntropyHub v1.0 now allows for cross-entropy (and multiscale cross-entropy) estimation with different length signals (except XCondEn and XPermEn).
As a result, the new cross-entropy functions require a separate input for each sequence (Sig1, Sig2).

----- Refined-Composite Multiscale Fuzzy Entropy -----
In addition to the refined-composite multiscale sample entropy that was available in earlier versions, now one can estimate the refined-composite multiscale fuzzy entropy based on the method outlined here.
What's more, refined-composite multicale cross-fuzzy entropy is also available, and both can be estimated using any of the fuzzy membership functions in FuzzEn or XFuzzEn.

----- Generalized Multiscale Entropy -----
Generaized multiscale entropy and generalized multiscale cross-entropy can now be estimated. Just choose the 'generalized' as the graining procedure in MSEn or XMSEn.

----- Variance of sample entropy estimate -----
Based on the method outlined by Lake et al., it is now possible to obtain a measure of the variance in the sample entropy estimate.
This is achieved by approximating the number of overlapping embedding vectors.
To do so, just set the parameter 'Vcp'==true in SampEn and XSampEn, but note that doing so requires a lot of computer memory.

Several little bugs and inconsistencies have also been fixed in this release. We want to thank all of you who have identified and alerted us to these bugs.
Most of these bugs have been noted via the GitHub issues portal.

----- Bug fixes -----

The DispEn2D function in python has now fixed MattWillFlood/EntropyHub#8.
The type hint for FuzzEn in python has been updated MattWillFlood/EntropyHub#1.
#3 are now resolved.
A bug in the K2En python function led to incorrect entropy estimates for data sequences with many equal values. This has been corrected.
----- Other Changes -----

The 'equal' method for discretizing data in DispEn and DispEn2D has been updated to be consistent across Python, MatLab and Julia. This is unlikely to have impacted any users previously.
The zeroth dimension (m=0) estimate of ApEn and XApEn has been changed to -phi(1).
The default radius threshold distance for XApEn, XSampEn and XK2En has been changed to use the pooled standard deviation [i.e. 0.2*SDpooled(X,Y)].

[JuliaRegistrator]

Registration pull request updated: JuliaRegistries/General/103526

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v1.0.0 -m "<description of version>" 998d251d4d2d1c8d6ba90d0d9cbfb5aceebe4549
git push origin v1.0.0

[MattWillFlood]

@JuliaRegistrator register

Release notes:

v1.0
----- New entropy methods -----
Two new base entropy functions (and their multiscale versions) have been added:

Diversity Entropy
Range Entropy

----- New fuzzy membership functions -----
Several new fuzzy membership functions have been added to FuzzEn, XFuzzEn and FuzzEn2D to provide more options for mapping the degree of similarity between embedding vectors.
These include trapezoidal, triangular and gaussian, among others.
Further info on these membership functions can be found here.

----- Phase Permutation Entropy -----
A new variant - 'phase' permutation entropy - has been added to PermEn.
This method employs a hilbert transformation of the data sequence, based on the methods outlined here.

----- Cross-Entropy with different length sequences -----
EntropyHub v1.0 now allows for cross-entropy (and multiscale cross-entropy) estimation with different length signals (except XCondEn and XPermEn).
As a result, the new cross-entropy functions require a separate input for each sequence (Sig1, Sig2).

----- Refined-Composite Multiscale Fuzzy Entropy -----
In addition to the refined-composite multiscale sample entropy that was available in earlier versions, now one can estimate the refined-composite multiscale fuzzy entropy based on the method outlined here.
What's more, refined-composite multicale cross-fuzzy entropy is also available, and both can be estimated using any of the fuzzy membership functions in FuzzEn or XFuzzEn.

----- Generalized Multiscale Entropy -----
Generaized multiscale entropy and generalized multiscale cross-entropy can now be estimated. Just choose the 'generalized' as the graining procedure in MSEn or XMSEn.

----- Variance of sample entropy estimate -----
Based on the method outlined by Lake et al., it is now possible to obtain a measure of the variance in the sample entropy estimate.
This is achieved by approximating the number of overlapping embedding vectors.
To do so, just set the parameter 'Vcp'==true in SampEn and XSampEn, but note that doing so requires a lot of computer memory.

Several little bugs and inconsistencies have also been fixed in this release. We want to thank all of you who have identified and alerted us to these bugs.
Most of these bugs have been noted via the GitHub issues portal.

----- Bug fixes -----

The DispEn2D function in python has now fixed MattWillFlood/EntropyHub#8.
The type hint for FuzzEn in python has been updated MattWillFlood/EntropyHub#1.
#3 are now resolved.
A bug in the K2En python function led to incorrect entropy estimates for data sequences with many equal values. This has been corrected.
----- Other Changes -----

The 'equal' method for discretizing data in DispEn and DispEn2D has been updated to be consistent across Python, MatLab and Julia. This is unlikely to have impacted any users previously.
The zeroth dimension (m=0) estimate of ApEn and XApEn has been changed to -phi(1).
The default radius threshold distance for XApEn, XSampEn and XK2En has been changed to use the pooled standard deviation [i.e. 0.2*SDpooled(X,Y)].

[JuliaRegistrator]

Error while trying to register: Version 1.0.0 already exists

[MattWillFlood]

@JuliaRegistrator register

Release notes:

v1.0.1

----- New Windowing Tool -----
A new function WindowData() has been added to allow users to calculate entropy within subsequence windows of their data.

[JuliaRegistrator]

Registration pull request created: JuliaRegistries/General/105285

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v1.0.1 -m "<description of version>" 1045532517b7a281eed360399d1ab315d0222ff1
git push origin v1.0.1

[MattWillFlood]

@JuliaRegistrator register

Release notes:

v2.0

----- New multivariate methods -----

Five new multivariate entropy functions incorporating several method-specific variations

• Multivariate Sample Entropy
• Multivariate Fuzzy Entropy [++ many fuzzy functions]
• Multivariate Dispersion Entropy [++ many symbolic sequence transforms]
• Multivariate Cosine Similarity Entropy
• Multivariate Permutation Entropy [++ amplitude-aware, edge, phase, weighted and modified variants]

----- New multivariate multiscale methods -----

Two new multivariate multiscale entropy functions

• Multivariate Multiscale Entropy [++ coarse, modified and generalized graining procedures]
• Composite and Refined-composite Multivariate Multiscale Entropy

----- Extra signal processing tools -----

WindowData() is a new function that allows users to segment data (univariate or multivariate time series) into windows with/without overlapping samples! This allows users to calculate entropy on subsequences of their data to perform analyses with greater time resolution.

Other little fixes...

----- Docs edits -----

• Examples in the www.EntropyHub.xyz documentation were updated to match the latest package syntax.

[JuliaRegistrator]

Registration pull request created: JuliaRegistries/General/105565

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v2.0.0 -m "<description of version>" 9f87e4909c88f15436bea2868125e93c32981574
git push origin v2.0.0

[MattWillFlood]

@JuliaRegistrator register

Release notes:

v2.0

----- New multivariate methods -----

Five new multivariate entropy functions incorporating several method-specific variations

• Multivariate Sample Entropy
• Multivariate Fuzzy Entropy [++ many fuzzy functions]
• Multivariate Dispersion Entropy [++ many symbolic sequence transforms]
• Multivariate Cosine Similarity Entropy
• Multivariate Permutation Entropy [++ amplitude-aware, edge, phase, weighted and modified variants]

----- New multivariate multiscale methods -----

Two new multivariate multiscale entropy functions

• Multivariate Multiscale Entropy [++ coarse, modified and generalized graining procedures]
• Composite and Refined-composite Multivariate Multiscale Entropy

----- Extra signal processing tools -----

WindowData() is a new function that allows users to segment data (univariate or multivariate time series) into windows with/without overlapping samples! This allows users to calculate entropy on subsequences of their data to perform analyses with greater time resolution.

Other little fixes...

----- Docs edits -----

• Examples in the www.EntropyHub.xyz documentation were updated to match the latest package syntax.

[JuliaRegistrator]

Registration pull request updated: JuliaRegistries/General/105565

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v2.0.0 -m "<description of version>" fe222215c1e9595748118da0d1986ec7a1425f74
git push origin v2.0.0