this problem are implemented including alternating direction method of multipliers ('ADMM'),
proximal gradient (PG) and accelerated proximal gradient ('APG') (See Atkins 'et al'. <arXiv:1705.07194>).
Sparse zero variance discriminant analysis ('SZVD') is also included in the package
(See Ames and Hong, <arXiv:1401.5492>). See the 'github' wiki for a more extended description.
Packaged: 2022-09-02 08:02:23 UTC; gudmundureinarsson

Title: A Canonical Correlation Analysis with Inferential Guaranties
Description: It performs Canonical Correlation Analysis and provides inferential guaranties on the correlation components. The p-values are computed following the resampling method developed in Winkler, A. M., Renaud, O., Smith, S. M., & Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, <doi:10.1016/j.neuroimage.2020.117065>. Furthermore, it provides plotting tools to visualize the results.

Title: Functions for processing uniaxial minute-to-minute accelerometer
Maintainer: Dane R. Van Domelen <vandomed@gmail.com>
Description: This package contains a collection of functions that perform operations on time-series accelerometer data, such as identify non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented. Most of the functions are written in C++ and therefore execute very quickly.

Title: Functions for processing minute-to-minute accelerometer data

Description: This package contains a collection of functions that perform operations on time-series accelerometer data, such as identify non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented. Most of the functions are written in C++ for efficiency.

Title: Functions for Processing Minute-to-Minute Accelerometer Data