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Update benchmarks in Windows and documentation
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‎NEWS.md‎

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# RecurrenceAnalysis.jl News
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## 30-06-2018 - commit ea915e9
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* Break compatibility with 0.6
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* Breaking modifications of RQA functions, in order to match the outputs of
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**crqa** (R) and **CRPToolbox** (Matlab):
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- The denominator of RR is fixed to N<sup>2</sup>.
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- Theiler window added to the calculation of LAM and TT.
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- Theiler window defaults to 0 in all functions.
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* Bugfix in the calculation of diagonal structures
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## 23-06-2018 - v0.2.0
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* Update to support Julia 0.6-0.7 (breaking compatibility with 0.5).

‎README.md‎

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Note: In order to keep the functions simpler and avoid confusion with the scaling of the distances, there is no option to normalize the input, although that is a customary procedure in RQA. This can be done *prior* to using the functions of this package.
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The function `rqa` also accepts all those keyword arguments, which are passed down to the corresponding elementary functions. That function also accepts specific values for the Theiler window and the minimum length of recurrent structures, which are applied only in the calculation of some parameters:
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The function `rqa` also accepts all those keyword arguments, which are passed down to the corresponding elementary functions. That function also accepts other keyword arguments for the Theiler window and the minimum length of recurrent structures, which are applied only in the calculation of some parameters:
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* `theilerdiag` overrides `theiler` in the calculation of parameters related to diagonal structures, i.e. DET, L, Lmax, DIV, ENT and TND.
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* `theilervert` overrides `theiler` in the calculation of parameters related to vertical structures, i.e. LAM, TT and Vmax.
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* `lmindiag` overrides `lmin` in the calculation of parameters related to diagonal structures.
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* `lminvert` overrides `vmin` in the calculation of parameters related to vertical structures.
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### Comparison with other RQA software packages
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After version 0.2.0 some RQA methods and the defaults of their options have been changed to make their results comparable with those provided by other software packages. Look at the [News](NEWS.md) of this package for details.
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## Auxiliary functions
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**Functions to estimate the optimal delay for a time series:**
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```julia
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@windowed determinism(rmat, theiler=2, lmin=3) width=1000 step=100
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```
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will return a 91-element vector, such that each value is the determinism associated to a 1000-point fragment, starting at every 100 points (i.e. at `1`, `101`, &ldots; `9001`).
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will return a 91-element vector, such that each value is the determinism associated to a 1000-point fragment, starting at every 100 points (i.e. at `1`, `101`, &hellip; `9001`).
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The general syntax of that macro is:
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```julia
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* `w` is the width of the window for relevant data around each point.
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* `s` is the step or distance between points where the calculations are done (starting in the first point).
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To prevent syntax failures in the expansion of the macro, identify the RQA function (`rqa`, `recurrencerate`, `determinism`,&ldots;) directly by its name (avoid aliases), and use simple variable names (not complex expressions) for the arguments. On the other hand, the windowing options `w` and `s` can be given in any order. If `s` is ommitted, the calculations are done at every point, and the keyword `width` may be ommitted. (However, using `step=1` may be computationally very expensive, and that will provide just overly redundant results around each point, so it is advisable to set `step` a relatively big fraction of the window `width`.)
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To prevent syntax failures in the expansion of the macro, identify the RQA function (`rqa`, `recurrencerate`, `determinism`,&hellip;) directly by its name (avoid aliases), and use simple variable names (not complex expressions) for the arguments. On the other hand, the windowing options `w` and `s` can be given in any order. If `s` is ommitted, the calculations are done at every point, and the keyword `width` may be ommitted. (However, using `step=1` may be computationally very expensive, and that will provide just overly redundant results around each point, so it is advisable to set `step` a relatively big fraction of the window `width`.)
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The value returned by the macro will normally be a vector with the same type of numbers as expected by `expr`. In the case of `@windowed rqa(...) ...`, it will return a dictionary with a similar structure as in the default `rqa` function, but replacing scalar values by vectors.
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## To-do list:
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* FAN method to define recurrence plots
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* Fast approximations to RQA
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* Recurrence Network analysis

‎benchmarks/test_linux_180718/system.txt‎

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Intel(R) Core(TM) i3-4100M CPU @ 2.50GHz, 8GiB SODIMM DDR3 1600 MHz, running Ubuntu 16.04-LTS
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Software versions:
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Julia 0.7.0-beta2
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Python 2.7.12
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R 3.4.4
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Package versions
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Julia: RecurrenceAnalysis commit 81c20f81add0d1c0e558874211415b0147ad2f79
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Pyunicorn 0.5.1
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R: crqa 1.0.7

‎benchmarks/test_win_180718/benchmark_rqa_julia_euclidean.txt‎

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3 15.756243 0.039923326062528915 0.9984363502575423 19.488330341113105 394 3.5923889991213565 0.9820180279617365 5.057555660824254
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6 80.57940500000001 0.03365183113654758 0.9979063212991545 17.44580009385265 605 3.56908455971288 0.9843913568648504 5.21448883833357
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1 2.146225 0.037921050773250475 0.9990689013035382 18.1864406779661 237 3.044979404476836 0.9548417132216015 3.8408239700374533
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2 8.848425 0.033777882289707066 0.9975136747886624 16.716666666666665 326 3.4088254760664225 0.9696668324216807 4.4042913608131
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3 18.194012999999998 0.039923326062528915 0.9984363502575423 19.488330341113105 394 3.5923889991213565 0.9820180279617365 5.057555660824254
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7 203.101432 0.03207263086944374 0.997811725846408 17.750275431509365 605 3.5821178605334514 0.9844756399669694 5.137685843568197
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8 161.343917 0.0326815014837516 0.9979870240473204 17.79431253451132 605 3.5866803854616194 0.9869543673835958 5.258735200693041
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9 205.18275699999998 0.03285559924003383 0.9980675976227805 17.763789746917585 605 3.5935472394747543 0.9873664637036497 5.258009190343667
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10 259.051803 0.03333234251093351 0.9980710699261385 17.907304347826088 605 3.5959854774554074 0.9885185041583467 5.324570563358221
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11 407.349629 0.033422155300425097 0.9980842453123877 17.81153846153846 605 3.5983414662419833 0.9881981528931887 5.331775700934579
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12 496.002316 0.034486002913931495 0.998194195442617 18.085206543485935 758 3.6108687203622942 0.9889573102606075 5.380358341873697

‎benchmarks/test_win_180718/benchmark_rqa_julia_max.txt‎

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3 16.624396 0.057924075175711104 0.99416761759858 21.13477088948787 394 3.584723001745066 0.9917585900849499 6.481872798839859
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4 35.594252 0.05339171269812651 0.9934763421466671 18.6117900790798 605 3.5067202186814597 0.9895429602056871 6.538988208444275
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5 80.21868 0.05350622845226941 0.9932200082920762 19.467017208413 605 3.5822456987579514 0.9902202277882106 6.695580474934037
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6 80.707997 0.04999584489536363 0.9926466602829319 18.455492106147126 605 3.561546025430616 0.9887712515131258 6.645316009956894
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7 107.14710500000001 0.0479103131650072 0.9926616915422886 18.818181818181817 605 3.5455325123414814 0.9881633499170812 6.555744017603374
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10 325.41742899999997 0.049507410489965986 0.9927755189944463 19.187941473259333 672 3.5779896835339424 0.9899823140528229 6.832755281293635
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1 1.9720520000000001 0.0546218487394958 0.9941822882999354 17.477272727272727 237 3.1418178182603116 0.9786683904330963 4.791139240506329
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12 491.64889 0.051020010429939304 0.9932296263122539 19.575545171339563 758 3.588422583846597 0.9908784362280852 6.912188548063523

‎benchmarks/test_win_180718/system.txt‎

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Intel(R) Core(TM) i3-3240 CPU @ 3.40GHz CPU, 4GB DDR3 RAM 1333 MHz, running Windows 10 Enterprise.
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Software versions:
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Julia 0.7.0-beta2
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Matlab R2013b
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R 3.5.1
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Package versions
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Julia: RecurrenceAnalysis commit 81c20f81add0d1c0e558874211415b0147ad2f79
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Matlab: CRPToolbox 5.22 (R32.2)
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R: crqa 1.0.7

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