IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - JOSS version (v0.6.4)

- Julia
Published by duodenum96 10 months ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.6.4

IntrinsicTimescales v0.6.4

Diff since v0.6.3

• Updates for paper.md after review
• Release for JOSS paper

Merged pull requests: - [joss] editing: update paper.md (#46) (@britta-wstnr)

- Julia
Published by github-actions[bot] 10 months ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.6.3

IntrinsicTimescales v0.6.3

Diff since v0.6.2

• Clarification of docstrings in *_model functions.
• Bug fix related to freqlims that was making informedpriors unusable for summarystatistic=:psd

Merged pull requests: - 41 joss confusing code block in one time scale model doc (#43) (@duodenum96)

Closed issues: - [joss] Figure 1 (#33) - "Should I averageovertrials?" (#35) - [joss] Typos (#39) - [joss] Confusing code block in One Time Scale Model doc (#41)

- Julia
Published by github-actions[bot] 11 months ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.6.2

IntrinsicTimescales v0.6.2

Diff since v0.6.1

• Fixing of typos and replicability issues in documentation

Merged pull requests: - fix reproducibility issue and warning message in README and Quickstart (#40) (@duodenum96) - 39 joss typos (#42) (@duodenum96)

Closed issues: - [joss] Warning and error in Getting Started example using simulation-based methods (#38)

- Julia
Published by github-actions[bot] 12 months ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.6.1

IntrinsicTimescales v0.6.1

Diff since v0.6.0

• Documentation overhaul: The documentation is now divided into sections of "Explanation", "Tutorials" and "Reference". Also added two tutorials for using the package with MNE and FieldTrip.

Merged pull requests: - Documentation overhaul (#37) (@duodenum96)

Closed issues: - [joss] Statement of Need (#23) - [joss] Documentation (#24) - [joss] add rng for random function (#25)

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.6.0

IntrinsicTimescales v0.6.0

Diff since v0.5.1

Changelog:

• Parallelism via OhMyThreads.jl. Instead of mapslices, now the functions use map or tmap depending on the kwarg parallel. This change affects the functions acw, comp_ac_fft, comp_psd, comp_psd_adfriendly, comp_ac_time, comp_ac_time_missing, fit_expdecay, fit_expdecay_3parameters, acw50, acw0, acweuler, acw_romberg, find_knee_frequency, fooof_fit.

Merged pull requests: - Parallel acw (#34) (@duodenum96)

Closed issues: - [joss] error in the Quickstart example (#31) - [joss] problems with Quickstart/Documentation (#32)

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.5.1

IntrinsicTimescales v0.5.1

Diff since v0.5.0

• Corrections in documentation and readme addressing issues #31 and #32.

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.5.0

IntrinsicTimescales v0.5.0

Diff since v0.4.1

Breaking Changes:

• Plotting capabilities now live inside an extension. What this means is if the user intends to use acwplot and posterior_predictive functions, they would need to first using Plots.

New Features:

• Added skipzerolag option to acw function. When true, ignores the zero-lag in :tau setting and fits a 3-parameter exponential decay function of the form acf(lag) = A * (exp(-lag / tau) + B).
• Option for setting rng and seed for reproducibility in the Ornstein-Uhlenbeck process generation.

Example usage: ```julia tau = 1.0 trueD = 1.0 dt = 0.01 duration = 10.0 numtrials = 100 deq_seed = 42

ou, _ = generateouprocesssciml(tau, trueD, dt, duration, numtrials, true, rng=Xoshiro(42), deqseed=deq_seed) `` -acwplotnow indicates the estimated knee frequency with a vertical line. Should make it easier to see ifacwis working properly for:knee` option.

Minor changes

• Added the tutorial "Navigating the Forest of INT metrics"
• Added "Contribution Guidelines" and "Developer Documentation" to the documentation
• Updated all the docstrings to match the code.

Merged pull requests: - 25 joss add rng for random function (#26) (@duodenum96) - 22 acw docstring (#27) (@duodenum96) - add skipzerolag option to acw (#28) (@duodenum96) - 24 joss documentation (#29) (@duodenum96)

Closed issues: - [joss] dependencies (#20) - acw DocString (#22)

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.4.1

IntrinsicTimescales v0.4.1

Diff since v0.4.0

Minor changes: - Removed dependencies BenchmarkTools, DifferentiationInterfaceTest and LogExpFunctions (should've done these in 0.4.0).

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.4.0

IntrinsicTimescales v0.4.0

Diff since v0.3.0

Breaking Changes: - Plotting functions are now in an extension. The functions acwplot and posterior_predictive can only be used after using Plots.

Other Changes: - Documentation improvements - Changed DifferentialEquations.jl dependency to much smaller StochasticDiffEq.jl dependency

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.3.0

IntrinsicTimescales v0.3.0

Diff since v0.2.3

New Features:

• Variable exponent and constrained optimization options for FOOOF style fitting! Now the user can use the keyword arguments allow_variable_exponent and constrained to use these options.

Breaking Changes: - The function fit is renamed to int_fit to avoid naming clashes with other packages.

Other Changes: - Documentation improvements - The output of find_knee_frequency is now uniformized so that it always returns [amplitude, knee, optionally exponent]. Previously it was returning [amplitude, knee] for vector inputs and only knee for array inputs.

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.2.3

IntrinsicTimescales v0.2.3

Diff since v0.2.2

cov_scale used to be a free parameter. This is inconsistent with the theory, hence now is a fixed constant.

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.2.2

IntrinsicTimescales v0.2.2

Diff since v0.2.1

Bug fixes:

Bug fix related to indexing of thetastar in drawtheta_pmc

- Julia
Published by github-actions[bot] about 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.2.1

IntrinsicTimescales v0.2.1

Diff since v0.2.0

Bug fixes: - Fixed the bug when abc method is used with verbose=false option

Others: - Removed some of the unnecessary dependencies - Added random seed in tests for replicability

- Julia
Published by github-actions[bot] over 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.2.0

IntrinsicTimescales v0.2.0

Diff since v0.1.0

New features:

• Added :auc option to acw to get the area under the curve of ACF before it hits 0
• Finished the tutorials
• FOOOF style knee frequency estimation. Previously, to get the knee frequency only a single lorentzian fitting was performed. With the new release, the algorithm first fits a lorentzian, subtracts the lorentzian from PSD, fits a gaussian to each oscillation peak, subtracts the gaussians from original PSD, fits the lorentzian again.
• Finished the Practice section of documentation involving tutorials on various ways to estimate INT

Breaking changes: - Updates on compatibilities with the help of CompatHelper

Additionally various minor bug fixes

Merged pull requests: - CompatHelper: add new compat entry for Reexport at version 1, (keep existing compat) (#1) (@github-actions[bot]) - CompatHelper: add new compat entry for FFTW at version 1, (keep existing compat) (#2) (@github-actions[bot]) - CompatHelper: add new compat entry for StatsBase at version 0.34, (keep existing compat) (#3) (@github-actions[bot]) - CompatHelper: add new compat entry for NonlinearSolve at version 4, (keep existing compat) (#4) (@github-actions[bot]) - CompatHelper: add new compat entry for DifferentialEquations at version 7, (keep existing compat) (#5) (@github-actions[bot]) - CompatHelper: add new compat entry for Statistics at version 1, (keep existing compat) (#6) (@github-actions[bot]) - CompatHelper: add new compat entry for Turing at version 0.36, (keep existing compat) (#7) (@github-actions[bot]) - CompatHelper: add new compat entry for Revise at version 3, (keep existing compat) (#8) (@github-actions[bot]) - CompatHelper: add new compat entry for Distributions at version 0.25, (keep existing compat) (#9) (@github-actions[bot]) - Bump julia-actions/cache from 1 to 2 (#12) (@dependabot[bot])

Closed issues: - TagBot trigger issue (#11)

- Julia
Published by github-actions[bot] over 1 year ago

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - v0.1.0

IntrinsicTimescales v0.1.0

Merged pull requests: - CompatHelper: bump compat for NonlinearSolve to 4, (keep existing compat) (#10) (@github-actions[bot])

- Julia
Published by github-actions[bot] over 1 year ago