Description: Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model.  The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.

Title: Monotonize point and interval functional estimates by
Author: Wesley Graybill, Mingli Chen, Victor Chernozhukov, Ivan
Description: This package implements the rearrangement operator (Hardy,
Littlewood, and Polya 1952) for univariate, bivariate, and
trivariate point estimates of monotonic functions. It
additionally provides a function that creates simultaneous
confidence intervals for univariate functions and applies the
rearrangement operator to these confidence intervals.

Title: Monotonize Point and Interval Functional Estimates by
Description: The rearrangement operator (Hardy,
trivariate point estimates of monotonic functions. The package

Title: Recursive Mining for Frequent Pattern and Confident Association
Description: Provides functions allowing the user to recursively extract frequent patterns and confident rules according to indicators of minimal support and minimal confidence. These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) <arXiv:2011.14195>.
Packaged: 2020-12-01 23:15:02 UTC; lraimbault

Title: Ecocrop: Estimating Environmental Suitability for Plants
Description: The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).
Authors@R: c(person("Robert J.", "Hijmans", role = c("cre", "aut"), email = "r.hijmans@gmail.com"), person("Rhys", "Manners", role = "ctb"))
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>