It estimates the parameters of a censored or missing data in spatio-temporal models using the SAEM algorithm (Delyon et al., 1999). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982). To examine the performance of the fitted model, case-deletion measure are provided.

Provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.

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 <http://onlinelibrary.wiley.com/wol1/doi/10.1890/14-0454.1/abstract> 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.