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JuliaDynamics / Associations.jl

Algorithms for quantifying associations, independence testing and causal inference from data.
https://juliadynamics.github.io/Associations.jl/stable/
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typos #360

Closed spaette closed 8 months ago

spaette commented 10 months ago

What's the fix here?

$ grep -nr uncontably CausalityTools.jl
CausalityTools.jl/test/independence/SurrogateTest/pmi.jl:56:        return "fell uncontably many times"
CausalityTools.jl/test/independence/SurrogateTest/MutualInformation.jl:46:        return "fell uncontably many times"
CausalityTools.jl/test/independence/SurrogateTest/ConditionalMutualInformation.jl:56:        return "fell uncontably many times"
CausalityTools.jl/docs/src/examples/examples_independence.md:192:        return "fell uncontably many times"
$

Explanation Indicates Initialise Initialise Simultaneously all together artificial assumptions compatible dedicated directly efficient estimation excluding independence independence independent information information instantaneous least length likelihood much positions probabilities quantity synchronize the above they unfortunately

$ grep -nr Exaplanation CausalityTools.jl
CausalityTools.jl/src/contingency_matrices.jl:302:# Exaplanation of the algorithm
$ grep -nr Indicatates CausalityTools.jl
CausalityTools.jl/src/methods/crossmappings/estimators/RandomSegment.jl:9:Indicatates that cross mapping is performed on contiguous time series
CausalityTools.jl/src/methods/crossmappings/estimators/ExpandingSegment.jl:7:Indicatates that cross mapping is performed on a contiguous time series segment/window,
$ grep -nr Initalise CausalityTools.jl
CausalityTools.jl/src/example_systems/continuous/deprecate.jl:635:Initalise a dynamical system consisting of three coupled Lorenz attractors with
CausalityTools.jl/src/example_systems/continuous/LorenzTransitive9.jl:15:Initalise a dynamical system consisting of three coupled Lorenz attractors with
$ grep -nr Intitialise CausalityTools.jl
CausalityTools.jl/src/example_systems/discrete/deprecate.jl:961:Intitialise a 3D system where the response X is a highly nonlinear combination
$ grep -nr Simulatenously CausalityTools.jl
CausalityTools.jl/docs/src/examples/examples_cross_mappings.md:247:Simulatenously, we also compute the [`PairwiseAsymmetricInference`](@ref) measure
$ grep -nr alltogether CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/mutualinfo/estimators/wip/tests.ipynb:96:    "    cycle = Cycle([:color, :linestyle, :marker], covary=true) # alltogether\n",
CausalityTools.jl/src/methods/infomeasures/mutualinfo/estimators/wip/tests.ipynb:378:    "    cycle = Cycle([:color, :linestyle, :marker], covary=true) # alltogether\n",
CausalityTools.jl/docs/src/examples/examples_mi.md:217:    cycle = Cycle([:color, :linestyle, :marker], covary=true) # alltogether
$ grep -nr artifical CausalityTools.jl
CausalityTools.jl/src/methods/crossmappings/crossmappings.jl:163:        # To circumvent zero-division, we simply add some artifical small distance
$ grep -nr assuptions CausalityTools.jl
CausalityTools.jl/src/contingency_matrices.jl:325:# They present multiple alternatives, based of different prior assuptions of the data,
$ grep -nr compatiable CausalityTools.jl
CausalityTools.jl/src/deprecations/predictive_asymmetry.jl:65:`predictive_asymmetry`. Check the online documentation for compatiable estimators.
$ grep -nr dedicatd CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/transferentropy/transferentropy.jl:144:# When using any estimator except dedicatd `TransferEntropyEstimator`s,
$ grep -nr directyly CausalityTools.jl
CausalityTools.jl/src/contingency_matrices.jl:24:The contingency matrix can be constructed directyly from an `N`-dimensional `frequencies`
$ grep -nr eficient CausalityTools.jl
CausalityTools.jl/docs/refs.bib:28:    abstract = {The PC-algorithm was shown to be a powerful method for estimating the equivalence class of a potentially very high-dimensional acyclic directed graph (DAG) with the corresponding Gaussian distribution. Here we propose a computationally eficient robustification of the PC-algorithm and prove its consistency. Furthermore, we compare the robustified and standard version of the PC-algorithm on simulated data using the new corresponding R package pcalg. }
$ grep -nr "estimatio " CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/various/probabilities/LoftsGaarden.jl:8:density estimatio from Loftsgaarden & Quesenberry (1965).
$ grep -nr exluding CausalityTools.jl
CausalityTools.jl/src/deprecations/predictive_asymmetry.jl:58:mean transfer entropy over prediction lags ``-\\eta, ..., \\eta`` (exluding lag 0).
$ grep -nr indendence CausalityTools.jl
CausalityTools.jl/src/causal_graphs/pc/PC.jl:26:- **`max_depth`**. The maximum level of conditional indendence tests to be
$ grep -nr indepencence CausalityTools.jl
CausalityTools.jl/README.md:25:    and the `LocalPermutationTest` for conditional indepencence testing.
CausalityTools.jl/src/deprecations/joint_distance_distribution.jl:9:Use [`JointDistanceDistributionTest`](@ref) to perform a formal indepencence test.
$ grep -nr independendent CausalityTools.jl
CausalityTools.jl/src/independence_tests/local_permutation/LocalPermutationTest.jl:43:conditionally independendent given a third variable `Z` (all of which may be multivariate).
CausalityTools.jl/src/independence_tests/surrogate/SurrogateTest.jl:17:are independendent, potentially conditioned on a third variable `Z`, based on
$ grep -nr infomation CausalityTools.jl
CausalityTools.jl/docs/src/examples/examples_mi.md:94:ax = Axis(fig[1, 1], xlabel = "k / N", ylabel = "Mutual infomation (nats)")
$ grep -nr infromation CausalityTools.jl
CausalityTools.jl/docs/src/index.md:84:    provided mutual infromation and transfer entropy estimators. These have been
$ grep -nr intantaneous CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/transferentropy/embedding.jl:42:the intantaneous lag.
$ grep -nr leats CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/mutualinfo/estimators/GaoOhViswanath.jl:40:        error("Need at leats two input StateSpaceSets to compute mutual information between them.")
CausalityTools.jl/src/methods/infomeasures/mutualinfo/estimators/KSG2.jl:78:        error("Need at leats two input StateSpaceSets to compute mutual information between them.")
CausalityTools.jl/src/methods/infomeasures/mutualinfo/estimators/wip/Evans.jl:24:        error("Need at leats two input StateSpaceSets to compute mutual information between them.")
$ grep -nr lenght CausalityTools.jl
CausalityTools.jl/src/deprecations/crossmap.jl:33:    (each of lenght `L`) of embedding vectors in ``M_x`` (time ordering matters). This is
CausalityTools.jl/src/deprecations/crossmap.jl:93:    (each of lenght `L`) of embedding vectors in ``M_x`` (time ordering matters). This is
$ grep -nr likehood CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/mutualinfo/estimators/wip/gao2017.ipynb:114:    "Estimate the density around point `xᵢ` using a local likehood estimator, which is \n",
CausalityTools.jl/src/methods/infomeasures/various/probabilities/LocalLikelihood.jl:64:Estimate the density around point `xᵢ` using a local likehood estimator, which is
CausalityTools.jl/src/methods/infomeasures/various/entropies/Gao2017.jl:23:    # In the case of a multivariate Gaussian, maximum likehood estimation simply
$ grep -nr muuuch CausalityTools.jl
CausalityTools.jl/src/contingency_matrices.jl:206:# The following commented-out code below is equivalent to theabove, but muuuch faster.
CausalityTools.jl/src/contingency_matrices.jl:252:    # The following is equivalent to the commented-out code above, but muuuch faster.
$ grep -nr posisions CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/transferentropy/utils.jl:153:    # Get lags and posisions separately for each marginal
CausalityTools.jl/src/methods/infomeasures/transferentropy/utils.jl:190:    # Get lags and posisions separately for each marginal
$ grep -nr probabilites CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/infomeasures.jl:55:# Contingency matrices and its computation based on various probabilites
$ grep -nr quantitity CausalityTools.jl
CausalityTools.jl/src/methods/recurrence/RMCD.jl:49:case the following mutual information-like quantitity is computed (not
$ grep -nr syncronize CausalityTools.jl
CausalityTools.jl/src/example_systems/continuous/deprecate.jl:528:        c_xy = 1.0, c_yx = 1.0, c_zx = 1.0, c_zy = 1.0, # beyond c = 2, systems syncronize
CausalityTools.jl/src/example_systems/continuous/LorenzForced9.jl:49:    c_zy::CZY = 1.0 # beyond c = 2, systems syncronize
$ grep -nr theabove CausalityTools.jl
CausalityTools.jl/src/contingency_matrices.jl:206:# The following commented-out code below is equivalent to theabove, but muuuch faster.
$ grep -nr " ther " CausalityTools.jl
CausalityTools.jl/docs/src/examples/examples_predictive_asymmetry.md:48:some information transfer $X \to Z$, even though ther are not directly linked, because
$ grep -nr unfurtunately CausalityTools.jl
CausalityTools.jl/src/methods/infomeasures/marginal_encodings.jl:96:        # Don't know how to make this faster unfurtunately...
$
spaette commented 10 months ago

@kahaaga

What's the fix here?

I will be submitting a pull.

Would uncountably be the fix?

spaette commented 8 months ago

Closed due to age.