Calculate the Number of Required Flows for Semiconductor
Sequencing
Two methods for calculation of the number of required flows for semiconductor sequencing: 1. Using a simulation, the number of flows can be calculated for a concrete list of amplicons. 2. An exact combinatorial model is evaluated to calculate the number of flows for a random ensemble of sequences.

c(
person("David", "Gohel", role = c("aut", "cre"),
email = "david.gohel@ardata.fr"),
person("Drifty", role = "cph") )
Provides icons from the 'Ionicons' icon pack (<http://ionicons.com/>).
Functions are provided to get icons as png files or as raw matrices. This is useful
when you want to embed raster icons in a report or a graphic.

Uku Vainik <ukuvainik@gmail.com>, Rene Mottus <rene.mottus@ed.ac.uk>
Provides item exclusion procedure, which is a formal method to
test 'Indifference Of iNdicator' (ION). When a latent personality
trait-outcome association is assumed, then the association strength
should not depend on which subset of indicators (i.e. items) has been
chosen to reflect the trait. Personality traits are  often measured
(reflected) by  a sum-score of a certain set of indicators.
Item exclusion procedure randomly excludes items from a sum-score and
tests, whether the sum-score - outcome correlation changes. ION has been
achieved, when any item can be excluded from the sum-score without the
sum-score - outcome correlation substantially changing . For more details,
see Vainik, Mottus et. al, (2015) "Are Trait-Outcome Associations Caused
by Scales or Particular Items? Example Analysis of Personality Facets and
BMI",European Journal of Personality  DOI: <10.1002/per.2009> .

Allen Goebl <goebl005@umn.edu>, Jeff Jones <jone1087@umn.ed>, and Adam Beatty <abeatty@humrro.org>
Collection of functions for IO Psychologists.

Functions for smoothing with infinite order flat-top kernels
Density, spectral density, and regression estimation using infinite order kernels.

Functions for Smoothing with Infinite Order Flat-Top Kernels
Density, spectral density, and regression estimation using infinite
order flat-top kernels.