Author: Andre Schuetzenmeister [cre, aut],
person(given="Andre", family="Schuetzenmeister", email="andre.schuetzenmeister@roche.com", role = c("cre", "aut")),
person(given="Florian", family="Dufey", email="florian.dufey@roche.com", role = c("aut")),
person(given="Andrea", family="Geistanger", email="andrea.geistanger@roche.com", role = c("ctb"))
Imports: MASS, utils, stats, graphics, grDevices
Description: Variance function estimation for models proposed by W. Sadler in his variance function program
('VFP', <http://www.aacb.asn.au/resources/useful-tools/variance-function-program-v14>). Here, the idea is
to fit multiple variance functions to a data set and consequently assess which function reflects
the relationship 'Var ~ Mean' best. For 'in-vitro diagnostic' ('IVD') assays modeling this relationship
is of great importance when individual test-results are used for defining follow-up treatment of patients.

Imports: VCA, MASS, utils, stats, graphics, grDevices
Suggests: RUnit, knitr, rmarkdown, prettydoc
Variance function estimation for models proposed by W. Sadler in his variance function program

Imports: VCA, MASS, utils, stats, graphics, grDevices, methods

Title: Vegetated Filter Strip and Erosion Model
Authors@R: c(person("Sarah", "Goslee", role = c("aut", "cre"),
person("Heather", "Gall", role = "aut"),
Imports: stats, graphics, e1071, nleqslv (>= 3.3.0)
Description: Empirical models for runoff, erosion, and phosphorus loss
across a vegetated filter strip, given slope, soils, climate, and
It also includes functions for deriving climate parameters from
measured daily weather data, and for simulating rainfall. Models
implemented include MUSLE (Williams, 1975) and APLE (Vadas et al.,

Title: Vector Generalized Linear and Additive Models
Author: Thomas W. Yee <t.yee@auckland.ac.nz>
Depends: R (>= 2.4.0), splines, methods, stats, stats4
Description: Vector generalized linear and additive models, and
associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs,
Reduced-Rank VGAMs). This package fits many models and
distribution by maximum likelihood estimation (MLE) or
penalized MLE. Also fits constrained ordination models in