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Library to convert the three ids, viz trace-id , span-id, parent-span-id in UUID format to zipkin B3 format in terms of hex char encodings.

java.lang.CharSequence is woefully under-used and under-supported IMO... Tiny module named "charstar" in homage to C's char*

The Array Utils library provides implementations for converting and modifying arrays (e.g. array sanitation, byte[] to char[] conversion).

Parso is a lightweight Java library designed to read SAS7BDAT datasets. The Parso interfaces are analogous to libraries designed to read table-storing files, for example, CSVReader library. Despite its small size, the Parso library is the only full-featured open-source solution to process SAS7BDAT datasets, both uncompressed, CHAR-compressed and BIN-compressed. It is effective in processing clinical and statistical data often stored in SAS7BDAT format. Parso allows converting data into CSV format.

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.