Random Approximate Elastic Net (RAEN) Variable Selection Method
The Proportional Subdistribution Hazard (PSH) model has been popular for estimating the effects of the covariates on the cause of interest in Competing Risks analysis. The fast accumulation of large scale datasets has posed a challenge to classical statistical methods. Current penalized variable selection methods show unsatisfactory performance in ultra-high dimensional data. We propose a novel method, the Random Approximate Elastic Net (RAEN), with a robust and generalized solution to the variable selection problem for the PSH model. Our method shows improved sensitivity for variable selection compared with current methods.

Fits AFM by Metropolis-Hastings, calculates theta and FST
out of AFM parameters

Data fusion filters for Attitude Heading Reference System (AHRS)
with several variants of the Kalman filter and the Mahoney and
Madgwick filters.
c(person(given = "Jose'", family = "Gama", role = c("aut","cre","trl"),email = "jgama@abo.fi"),
person(given = "Vlad", family = "Maximov", role = "aut"),
person(given = "Sebastian", family = "O.H. Madgwick", role = "aut"),
person(given = "Alain", family = "Barraud", role = "ctb"),
person(given = "Göran", family = "Högnäs", role = "ths"))
R implementation of data fusion filters for Attitude Heading Reference System (AHRS) based on
Vlad Maximov's GyroLib AHRS library (quaternion based linearized/extended/unscented Kalman filter,
Euler based LKF, gyro-free with vector matching, SVD calibration and EKF calibration),
Sebastian O.H. Madgwick AHRS algorithms and Sebastian O.H. Madgwick implementation of Mayhony et al AHRS algorithm.
Jose' Gama [aut, cre, trl],
Vlad Maximov [aut],
Sebastian O.H. Madgwick [aut],
Alain Barraud [ctb],
Göran Högnäs [ths]

Data Fusion Filters for Attitude Heading Reference System (AHRS)
with Several Variants of the Kalman Filter and the Mahoney and
Madgwick Filters
c(person(given = "Jose", family = "Gama", role = c("aut","cre","trl"),email = "rxprtgama@gmail.com"),
person(given = "Vlad", family = "Maximov", role = "aut"),
person(given = "Sebastian", family = "O.H. Madgwick", role = "aut"),
person(given = "Alain", family = "Barraud", role = "ctb"))
Data fusion filters for Attitude Heading Reference System (AHRS) based on
Vlad Maximov's GyroLib AHRS library (quaternion based linearized/extended/unscented Kalman filter,
Euler based LKF, gyro-free with vector matching, SVD calibration and EKF calibration),
Sebastian O.H. Madgwick AHRS algorithms and Sebastian O.H. Madgwick implementation of Mayhony et al AHRS algorithm.
Jose Gama [aut, cre, trl],
Vlad Maximov [aut],
Sebastian O.H. Madgwick [aut],
Alain Barraud [ctb]