davidruegamer
Professor of Data Science @ LMU Munich; PI @ Munich Center for Machine Learning; PI @ LMU Machine Learning Consulting Unit
LMU MunichMunich
Pinned Repositories
FDboost
Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).
BoostingSignalSynchro
Online Supplementary Material to Detecting Synchronisation in EEG- and EMG-Signals via Boosted Functional Historical Models
cAIC4dev
Conditional Akaike information criterion for lme4
deepregression_tutorial
Code for deepregression Tutorial Paper
DRIFT
FDA_tutorial
generalized_orthogonalization
pymgcv
semi-structured_distributional_regression
deepregression
davidruegamer's Repositories
davidruegamer/FDA_tutorial
davidruegamer/generalized_orthogonalization
davidruegamer/BoostingSignalSynchro
Online Supplementary Material to Detecting Synchronisation in EEG- and EMG-Signals via Boosted Functional Historical Models
davidruegamer/cAIC4dev
Conditional Akaike information criterion for lme4
davidruegamer/deepregression_tutorial
Code for deepregression Tutorial Paper
davidruegamer/DRIFT
davidruegamer/pymgcv
davidruegamer/SDDR_code
Code to reproduce results of Ruegamer et al. (2021)
davidruegamer/davidruegamer.github.io
davidruegamer/TUD_Workshop_2023
davidruegamer/airbnb
davidruegamer/coinflibs
R Package for selective inference in linear models after likelihood- or test-based model selection
davidruegamer/semi-structured_distributional_regression
davidruegamer/check_mboost
Diagnostic function for mboost models
davidruegamer/effortless
davidruegamer/FDboost_Tests
Generic tests for FDboost
davidruegamer/google-research
Google Research
davidruegamer/HessianFreeOptimization
Hessian Free Optimization with Tensorflow
davidruegamer/iboost
Add-on package for the R package mboost to calculate p-values and confidence intevals for model parameters. Package currently supports models fitted with Gaussian family including linear, group and spline base-learners.
davidruegamer/inference_boosting
Repository for 'Valid Inference for L2-Boosting'
davidruegamer/mboost
Boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data. The current relase version can be found on CRAN (http://cran.r-project.org/package=mboost).
davidruegamer/NormalizingFlowNetwork
Bayesian and Maximum Likelihood Implementation of the Normalizing Flow Network (NFN)
davidruegamer/PHO
davidruegamer/qgam
Additive quantile regression R package
davidruegamer/selfmade
SELective inFerence for Mixed and Additive model Estimators
davidruegamer/Sequence-to-Sequence-and-Attention-from-scratch-using-Tensorflow
Sequence to Sequence and attention from scratch using Tensorflow
davidruegamer/shap
A unified approach to explain the output of any machine learning model.
davidruegamer/shinytest
Automated testing for shiny apps
davidruegamer/StatisticalComputing2017
davidruegamer/TransferLearning_MTSC