Predicts the presence of signal peptides in eukaryotic protein
    using hidden semi-Markov models. The implemented algorithm can be accessed from
    both the command line and GUI.

Provides a comprehensive toolkit for extracting latent signals from
    panel data through multivariate time series analysis. Implements spectral
    decomposition methods including wavelet multiresolution analysis via maximal
    overlap discrete wavelet transform, Percival and Walden (2000)
    <doi:10.1017/CBO9780511841040>, empirical mode decomposition for
    non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>,
    and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott
    filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features
    Bayesian variable selection through regularized Horseshoe priors, Piironen
    and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, for identifying structurally
    relevant predictors from high-dimensional candidate sets. Includes dynamic
    factor model estimation, principal component analysis with bootstrap
    significance testing, and automated technical interpretation of signal
    morphology and variance topology.

Extract the signed backbones of intrinsically dense weighted networks based on the significance filter and vigor filter as described in the following paper. Please cite it if you find this software useful in your work.	
	Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. <arXiv:2012.05216>.

Add significance marks to any R Boxplot, including a given significance niveau.

Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.