smac-group Goto Github PK

irg

R Package for Granger-Causal Analysis of Irregularly Sampled Signals

jinipaper

mgmwm

:chart_with_upwards_trend: :chart_with_downwards_trend: :chart_with_upwards_trend: :chart_with_upwards_trend: :chart_with_downwards_trend: Multisignal GMWM estimation and model selection for IMU

navigation

🛰 The navigation R package implements a framework to analyze the impact of sensor error modeling on performance of integrated navigation (sensor fusion) based on IMU, GPS, and barometer data.

navigationdata

ns-gmwm

occupancy-profiling

odyn

A free Dynamic Network (DN) solver that runs online in the browser. ODyN fuses information from cameras, GNSS and inertial sensors in a single adjustment and it can be used to estimate a high-frequency trajectory for precise direct geo-referencing, to improve photogrammetric reconstructions in challenging scenarios or to determine several types of system calibration parameters.

pairbond-net

panning

An implementation of the Panning Algorithm

pdc

ptds

ptds2018_website

ptdspkg

r_lunch_rcpp

rcppspg

scis

:notebook_with_decorative_cover: This document is currently under development and has been designed as an introductory course on Inertial Sensor Stochastic Calibration

seeder-talk

showcase_intro_ds

simts

:hourglass_flowing_sand: Time Series Tools R package provides a series of tools to simulate, plot, estimate, select and forecast different time series models.

smac-group.github.io

Repository containing the source code for the SMAC Group website.

smacdata

Data sets used in various packages.

st_petersburg_game

stat_conf_24

Website for the Data Analytics Lab Workshop 2024 in Ovronnaz, Switzerland

stocapp

swag

swag is a R package that trains a meta-learning procedure which combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations.

swag_breast_cancer

We provide the codes necessary to replicate the findings presented in "Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks" authored by Cesare Miglioli, Gaetan Bakalli, Samuel Orso, Mucyo Karemera, Roberto Molinari, Stephane Guerrier and Nabil Mili.

test

test0

ts

:notebook: "An Introduction to Time Series Analysis with R" is a text which is currently under development and aims at giving readers a general overview of the main aspects that characterise time series and the most common tools used for their analysis.