siero5335's starred repositories
ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
causal-learn
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
ublacklist-stackoverflow-translation
Exclude machine-translated sites of Stack Exchange from Google search results
TidyTuesday
My contributions to the #TidyTuesday challenge, a weekly data visualization challenge. All plots are 💯 created in R with ggplot2.
stan-statespace
Stan models for state space time series
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.
tokupon_ds
中高生向け「データ分析入門」資料
chemical_representation_learning_for_toxicity_prediction
Chemical representation learning paper in Digital Discovery
metabolome-lipidome-MSDIAL
Guide to processing raw LCMS metabolomic and lipidomic data using MS-DIAL, followed by data pre-processing and secondary annotation (of metabolites) in R.
CausalModels
An R library for estimating causal effects
mspcompiler
Compile Mass Spectral Libraries from Various Sources
Waters2mzML
Waters2mzML converts & subsequently annotates Waters .raw MSn data (both MSe & DDA) into functional .mzML files. Obtained .mzML files can be processed in MZmine 3. It would be interesting to see if it works for all Waters .raw data and other processing streamlines.
GlobalFoodomics
Figures and code for "Reference data based insights expand understanding of human metabolomes"
kspub-dataviz
講談社サイエンティフィク 「データ分析のためのデータ可視化入門」サポートページ /.補足情報と正誤表
LCMRL_calculator
OW/OGWDW/SRMD/TSC - This repository contains the code and instructions for using the LCMRL calculator