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Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

📦 R package to simulate neutral landscape models 🏔

Fixed income tools for R

Image processing Toolkit in R

No description.

bring sf to spark in production

R package for (down)loading data from IBGE (Instituto Brasileiro de Geografia e Estatística)

An ecological niche model workflow based on dismo

UAV related Remote Sensing Toolbox

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). This repository mirrors https://gitlab.com/NMOF/NMOF .

An R package to perform Spatial Fuzzy C-means

A Unified Framework for Random Forest Prediction Error Estimation

R package with helper functions for developers and researchers familiar with Tidy Finance

R package polyclip: a port of the Clipper library for polygon geometry

NetCDF-CF Geometry and Timeseries Tools for R: https://code.usgs.gov/water/ncdfgeom

Classes for analysing and implementing equity portfolios in R.

Financial Market Building Blocks

Multiscalar Territorial Analysis

An R Package to compile data sets of historic results from thoroughbred sales