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abc2018

My notes, presentation and project for the ABC course I took at Chalmers University of Technology during spring 2018.

advbayeslearncourse-coursematerial

Course material for the PhD course in Advanced Bayesian Learning

cmf

R package for finding a joint low-rank representation for a collection of matrices with shared row or column entities.

d-cca

A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets (JASA-20 paper)

d-gcca

Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data (JMLR-22 paper)

dt8122-2021

Project assignment for the Probabilistic AI course (DT8122) at NTNU.

homebrew-zathura

Homebrew formulae to build Zathura on macOS

ithmbrdr

Reader for iPod ithmb files

keiths_ipod_photo_reader

Keith's iPod Photo Reader decodes images from the .ithmb file format used by Apple iPods

lscmf

mmpca

R package for integrative analysis of several related data matrices

mofapy2

Multi-omics factor analysis v2

multivariatestats.jl

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

nnls_antilopsided

probai-2019

Materials of the Nordic Probabilistic AI School 2019.

probai-2021-pyro

Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

rfast

A collection of Rfast functions for data analysis. Note 1: The vast majority of the functions accept matrices only, not data.frames. Note 2: Do not have matrices or vectors with have missing data (i.e NAs). We do no check about them and C++ internally transforms them into zeros (0), so you may get wrong results. Note 3: In general, make sure you give the correct input, in order to get the correct output. We do no checks and this is one of the many reasons we are fast.

smc2017

Collected code and materials from the intensive course preparing for the workshop on Sequential Monte Carlo (SMC) methods at Uppsala University, August 2017

solrcmf

srbtr

Transliteration between the Serbian latin and cyrillic alphabets

stan.vim

Vim syntax highlighting for Stan (mc-stan.org) modeling lauguage

variational-smc

Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)