AndreaCappozzo
Assistant Professor in Statistics @ Politecnico di Milano. Interests: Mixture models, Model-Based Clustering and Classification, Penalized estimation, ds4sg
University of MilanMilan, Italy
Pinned Repositories
academic-website
Repo for academic style website
brand-public_repo
Public repo for BRAND: Bayesian Robust Adaptive Novelty Detector. A two-stage Bayesian Nonparametric model for novelty detection with robust prior information.
CWRMmonitor
R package providing graphical and computational tools to guide parameter choice for the cluster weighted robust model. Associated paper Cappozzo, Garcìa Escudero, Greselin, Mayo-Iscar (2022+) Graphical and computational tools to guide parameter choice for the cluster weighted robust model.
emlmm
R package for fitting penalised linear mixed-effect models via EM-type algorithms
labs_NPS_AY_2022_2023
Nonparametric statistics POLIMI AY 2022/2023: lab notebooks
mixedelnet
An R package for fitting high dimensional linear mixed-effects models with elastic-net penalty
netReg
:bar_chart: Generalized linear regression models with network-regularization in R.
raedda
Model-based framework for robust classification that jointly accounts for outliers, label noise and unobserved classes in the test set.
rupclass
Robust model-based classification based on trimming and constraints
sparsemix
AndreaCappozzo's Repositories
AndreaCappozzo/labs_NPS_AY_2022_2023
Nonparametric statistics POLIMI AY 2022/2023: lab notebooks
AndreaCappozzo/academic-website
Repo for academic style website
AndreaCappozzo/mixedelnet
An R package for fitting high dimensional linear mixed-effects models with elastic-net penalty
AndreaCappozzo/raedda
Model-based framework for robust classification that jointly accounts for outliers, label noise and unobserved classes in the test set.
AndreaCappozzo/sparsemix
AndreaCappozzo/emlmm
R package for fitting penalised linear mixed-effect models via EM-type algorithms
AndreaCappozzo/netReg
:bar_chart: Generalized linear regression models with network-regularization in R.
AndreaCappozzo/rupclass
Robust model-based classification based on trimming and constraints
AndreaCappozzo/brand-public_repo
Public repo for BRAND: Bayesian Robust Adaptive Novelty Detector. A two-stage Bayesian Nonparametric model for novelty detection with robust prior information.
AndreaCappozzo/CWRMmonitor
R package providing graphical and computational tools to guide parameter choice for the cluster weighted robust model. Associated paper Cappozzo, Garcìa Escudero, Greselin, Mayo-Iscar (2022+) Graphical and computational tools to guide parameter choice for the cluster weighted robust model.
AndreaCappozzo/erum2018
"Building a package that lasts" — eRum 2018 workshop
AndreaCappozzo/sparsemixmat
AndreaCappozzo/erum2020program
e-Rum2020 program and materials
AndreaCappozzo/intRinsic
Public repository for the R package intRinsic
AndreaCappozzo/lsgl
Multivariate Linear Sparse Group Lasso
AndreaCappozzo/MatTransMix
:exclamation: This is a read-only mirror of the CRAN R package repository. MatTransMix — Clustering with Matrix Gaussian and Matrix Transformation Mixture Models
AndreaCappozzo/parfm
:exclamation: This is a read-only mirror of the CRAN R package repository. parfm — Parametric Frailty Models
AndreaCappozzo/quarto-web
Quarto website
AndreaCappozzo/reticulate
R Interface to Python
AndreaCappozzo/rstudio-conf-2018
Slide, code and data for "Applied Machine Learning" at Rstudio-conf 2018
AndreaCappozzo/STATS-monitoring_CWRM
This repository is associated with the paper Cappozzo, Garcìa Escudero, Greselin, Mayo-Iscar (2021) Parameter choice, stability and validity for robust cluster weighted modeling.
AndreaCappozzo/tlemix
Trimmed Maximum Likelihood Estimation
AndreaCappozzo/varselEMST
Robust variable selection for REDDA models via EMST algorithm
AndreaCappozzo/varselTBIC
Robust variable selection for REDDA models via TBIC approximation to the integrated likelihood. It is a robust adaptation of the greedy forward search algorithm for discriminant analysis. Based on clustvarsel R package.
AndreaCappozzo/vistime
Pretty timelines in R.