Software Tools for Applied Mathematics are categorized into these modules:
- Arrays
- Format
- Generators
- Lists
- Numbers
- Pairs
- Statistics
Choose The Modules that you want to import:
def mt_version = "0.7.5"
dependencies {
implementation 'io.github.dk96-os:arrays:$mt_version'
implementation 'io.github.dk96-os:arrays-ktx:$mt_version'
implementation 'io.github.dk96-os:format:$mt_version'
implementation 'io.github.dk96-os:generators:$mt_version'
implementation 'io.github.dk96-os:lists:$mt_version'
implementation 'io.github.dk96-os:lists-ktx:$mt_version'
implementation 'io.github.dk96-os:numbers:$mt_version'
implementation 'io.github.dk96-os:pairs:$mt_version'
implementation 'io.github.dk96-os:statistics:$mt_version'
}
Each module is published as a package that can be downloaded from GitHub packages. Some modules depend on each other, but cross-module dependencies have been minimized.
Numerical array operations not included in the standard library.
- Whole Number types only
Extension methods on Numerical Array Types.
- Whole Number types only
Tools that help format numerical data.
- Rounding
- Percentages
- Serialization
Generate Numbers of different types within specified ranges, and with optional relative probability. Also contains number counting data structures of different capacities for different requirements.
For specialized list operations, with an emphasis on Number types.
- Search within a range of indices in a List.
Kotlin extensions for the Lists module.
Specialized Numerical Operations, Data Structures, Factoring, and Prime Number Caches.
Simple Data Structures containing pairs of numbers of the same type.
- Fixed Pairs
- (Mutable) Pairs
Process lists and arrays of primitive Number types, to determine statistical characteristics.
- Determine the average, min, max, and standard deviation, simultaneously in 1 pass.
- Outlier Detection Policy
For additional information, see the Project Wiki