Techtonique

Welcome to Techtonique/Packages.

Techtonique = tech, statistics, machine learning, computer simulation, numerical optimization.
Here, you'll find examples + documentation for Techtonique's tools.

Currently available:

• nnetsauce for Python   and nnetsauce for R   Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks   |  
• mlsauce for Python   and   mlsauce for R   Miscellaneous Statistical/Machine Learning stuff  | 
• ahead (for R)   and   ahead (for Python)   Univariate and Multivariate time series forecasting  | 
• gpopt   Bayesian optimization using Gaussian Process Regression  | 
• bcn (for R)   and   BCN (for Python)   Boosted Configuration (neural) Networks for supervised learning  | 
• teller   Model-agnostic Statistical/Machine Learning explainability  | 
• querier   A query language for Python Data Frames  | 
• esgtoolkit   A toolkit for Monte Carlo Simulations in Finance, Economics, Insurance, Physics
• rtopy   Calling R functions in Python  | 
• crossvalidation   Generic R functions for cross-validation
• learningmachine (for R)   and   learningmachine (for Python) Machine Learning with Explanations and Uncertainty Quantification | 
• unifiedbooster   Unified interface for Gradient Boosted Decision Trees algorithms | 
• misc   Miscellaneous useful R functions
• Techtonique API calls in Python   and   Techtonique API calls in R  | 
• survivalist   and survivalist for R   Model agnostic Survival analysis with Machine Learning and uncertainty quantification  | 
• tisthemachinelearner (R)   tisthemachinelearner (Python)   Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor.  | 
• genbooster   A fast gradient boosting and bagging (RandomBagClassifier, similar to RandomForestClassifier) implementation using Rust and Python. Any base learner can be employed.  | 
• matern32   Interpretable Kernel Ridge Regression using Matern 3/2 kernels in R.
• bayesianrvfl   Adaptive Bayesian (NON)Linear regression in R.
• after   Univariate and multivariate time series forecasting with multiple models.
• rvfl   Random Vector Functional Link (RVFL) networks in R.
• mlreserving   Machine Learning Reserving for (longitudinal) triangle data.  | 
• GLMNet   Elastic Net regression and classification using GLMNet.  | 
• cybooster   Generic Boosting (can use GPUs).  | 
• ftsatoo   Functions for visualizing, modeling, (generic) forecasting and hypothesis testing of functional time series.
• stochasticscenarios A Package for Asset Projection Using Stochastic Simulation
• synthe Synthetic Data Simulation/Generation | 
• unifiedml A R6 Unified Machine Learning Interface for R (any R model you can think of)
• mlS3 R package `mlS3` providing an S3 interface to Machine Learning packages
• rvflnet R package `rvflnet` a nonlinear glmnet; Random Vector Functional Link (RVFL) networks using glmnet for elastic-net regularized output layer training.