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adaslope

High dimensional linear regression with missing via adaptive SLOPE

bioconductor-workshop-1

dispeer_data

Centroids, geodesic distances and structural connectivity data used within disPEER paper

gsoc

Easy: download the development version of the R package SLOPE (devtools::install_github("jolars/SLOPE")). Fit SLOPE and lasso (hint: see the lambda argument in SLOPE()) models using the SLOPE package to the abalone data set that comes with SLOPE. Plot the results. What are the similarities and differences? Medium: write a function using RcppArmadillo that computes the proximal operator for SLOPE using Algorithm 3 (FastProxSL1) from Bogdan et al 2015 (SLOPE: adaptive variable selection via convex optimization). Compare the result with SLOPE:::prox_sorted_L1() (observe that this function uses a different algorithm than the one you are supposed to implement) Hard: write an R package using RcppArmadillo (as a backend) that uses FISTA or ADMM to solve ordinary least squares regression using SLOPE. Make use of the function to compute the proximal operator that you implemented in the previous test.

lab2020

logreghd

Modern Logistic Regression for High Dimensional Data

r_workshops

Materials for the workshops on statistical packages development in R at Mathematical Institute, University of Wrocław

wyklad2020

zps2018

Zespolowy Projekt Specjalnosciowy