correct docs

README.md

@@ -198,16 +198,16 @@ julia> rxdetect(x, 0.95)
 ```
 ## Data generation and tests
 
-In folder `test\data_outliers` and `test\jkfsdata_select` there are Julia executable files testing selection and detection algorithms on artificial data.
+In folder `test\outliers_detect` and `test\features_select` there are Julia executable files testing selection and detection algorithms on artificial data.
 
 ### Features selection
 
- The executable file `jkfs_select.jl` generates multivariate data with non-Gaussian subset of marginals modelled by the t-Student copula. This file is parametrised by an integer being a number of degrees of freedom of the t-Student copula. Returns a `.jld2` file with data. Run `jkfs_data_analysis.jl` to achieve results of features selection given different methods.
+ The executable file `gendat4selection.jl` generates multivariate data with non-Gaussian subset of marginals modelled by the t-Student copula. This file is parametrised by an integer being a number of degrees of freedom of the t-Student copula. Returns a `.jld2` file with data. Run `jkfs_selection.jl` to achieve results of features selection given different methods.
 
 ### Outlier detection
 
- The executable file `jkfs_outliers.jl` generates multivariate data with non-Gaussian outliers subset of realisations modeled by the t-Student copula.
- This file is parametrised by an integer being a number of degrees of freedom of the t-Student copula. Returns a `.jld2` file with data. Run `detect.jl` to detect outliers and compare the "HOSVD" based method with the "RX" detector.
+ The executable file `gendat4detection.jl` generates multivariate data with non-Gaussian outliers subset of realisations modeled by the t-Student copula.
+ This file is parametrised by an integer being a number of degrees of freedom of the t-Student copula. Returns a `.jld2` file with data. Run `detect_outliers.jl` to detect outliers and compare the "HOSVD" based method with the "RX" detector.
 
 # Citing this work