An user-friendly framework to preprocess raw item
             scores of questionnaires into factors or scores and standardize
             them. Standardization can be made either by their normalization
             in representative sample, or by import of premade scoring table.

Starting from a Regression Model, it provides a stepwise 
              procedure to select the linear predictor.

Collection of model estimation, and model plotting functions
    related to the STEPCAM family of community assembly models. STEPCAM is
    a STEPwise Community Assembly Model that infers the relative
    contribution of Dispersal Assembly, Habitat Filtering and Limiting
    Similarity from a dataset consisting of the combination of trait and
    abundance data. See also <doi:10.1890/14-0454.1> for more information.

Interfaces the 'stepcount' Python 
    module <https://github.com/OxWearables/stepcount> to estimate
    step counts and other activities from 'accelerometry' data.

Provides a step-down procedure for controlling the False
  Discovery Proportion (FDP) in a competition-based setup, implementing 
  Dong et al. (2020) <arXiv:2011.11939>. Such setups include target-decoy 
  competition (TDC) in computational mass spectrometry and the knockoff 
  construction in linear regression.

An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.