principal components ('odpc') introduced in Smucler, Peña and Yohai (2018)
Imports: methods, Rcpp (>= 0.12.7), forecast, parallel, doParallel,

principal components ('odpc') introduced in Peña, Smucler and Yohai (2019)

Package: odprism
Type: Package
Title: Optimal design and performance of random intercept and slope
models.
Version: 1.0
Date: 2011-06-09
Author: Martijn van de Pol
Maintainer: Martijn van de Pol <m.van.de.pol@myscience.eu>
Description: Simulation functions to assess the optimal design and
performance of random intercept and slope models (i.e. mixed
models), which can be used to a priori determine adequate
sampling designs for e.g. reaction norm studies. Functions
allow users to vary the sampling design in terms of number of
grouping units sampled (e.g. individuals, schools, populations,
etc.) and replicates per grouping unit (unbalanced as well
balanced datasets) and also allow users to vary the parameter
conditions used to generate the data. Subsequently, the
performance of mixed models (based on lme4 package) fitted on
these datasets is assessed in terms of the accuracy and the
precision of estimates of fixed and random parameter, as well
as the statistical power.
License: GPL-2
Encoding: UTF-8
Depends: R (>= 2.11.0), lme4, mvtnorm, fields, graphics
LazyLoad: yes
Packaged: 2011-06-16 00:02:55 UTC; Martijn
Repository: CRAN
Date/Publication: 2011-06-17 06:45:17

Package: odprism
Type: Package
Title: Optimal design and performance of random intercept and slope
models.
Version: 1.1
Date: 2011-11-07
Author: Martijn van de Pol
Maintainer: Martijn van de Pol <m.van.de.pol@myscience.eu>
Description: Simulation functions to assess the optimal design and
performance of random intercept and slope models (i.e. mixed
models), which can be used to a priori determine adequate
sampling designs for e.g. reaction norm studies. Functions
allow users to vary the sampling design in terms of number of
grouping units sampled (e.g. individuals, schools, populations,
etc.) and replicates per grouping unit (unbalanced as well
balanced datasets) and also allow users to vary the parameter
conditions used to generate the data. Subsequently, the
performance of mixed models (based on lme4 package) fitted on
these datasets is assessed in terms of the accuracy and the
precision of estimates of fixed and random parameter, as well
as the statistical power.
License: GPL-2
Encoding: UTF-8
Depends: R (>= 2.11.0), stats, graphics, lme4, mvtnorm
Imports: Matrix, fields
Packaged: 2013-03-14 07:09:34 UTC; ripley
Repository: CRAN
Date/Publication: 2013-03-14 08:23:34
NeedsCompilation: no