epiphy v0.3.4

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Analysis of Plant Disease Epidemics

A toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and mapcomp methods). See Madden LV, Hughes G, van den Bosch F (2007, ISBN: 978-089054-354-2) for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package.

Readme

epiphy

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Overview

epiphy makes it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and mapcomp methods). Several data sets that were mainly published in plant disease epidemiology literature are also included in this package. The online documentation is available at epiphy.org.

Installation

Install the latest released version from CRAN:

install.packages("epiphy")

Install the latest development version from GitHub:

# install.packages("devtools")
devtools::install_github("chgigot/epiphy")

Functions in epiphy

Name Description
aphids Counts of aphids.
sadie Spatial Analysis by Distance IndicEs (SADIE).
fit_two_distr Maximum likelihood fitting of two distributions and goodness-of-fit comparison.
z.test Z-test.
simulated_epidemics Examples of simulated epidemic data.
mapping Construct data mappings.
mapped_var Existing variable mappings.
offspring_survival Offspring survival of rats experiencing different diets.
vcov.smle Calculate Variance-Covariance Matrix for a Fitted Model Object
smle Simple maximum likelihood estimation
onion_bacterial_blight Incidence of bacterial blight of onion.
pyrethrum_ray_blight Incidence of ray blight disease of pyrethrum.
power_law Taylor's and binary power laws.
smle_wrappers Wrappers using maximum likelihood estimation for some distributions
tomato_tswv Incidence of tomato spotted wilt virus (TSWV) disease in field trials.
tobacco_viruses Incidence of tobacco plants infected with viruses.
logLik.smle Extract log-likelihood
threshold To go to higher level in the hierarchy.
mapcomp Map Comparison procedure.
spatial_hier Spatial hierarchy analysis.
split.intensity Divide into groups and reassemble.
agg_index Several aggregation indices.
chisq.test Chi-squared test.
clump Regroup observational data into even clumps of individuals.
BetaBinomial The beta-binomial distribution.
as.data.frame.intensity Coerce to a data frame.
calpha.test C(alpha) test.
a2a Easily switch between different power law formulations.
is.intensity Test if an object is of class intensity or one of its subclasses.
citrus_ctv Incidence of citrus tristeza virus (CTV) disease in three fields.
codling_moths Count of codling moth larvae.
arthropods Counts of arthropods.
link Some link functions.
indAndSub Retrieve vector or array indices
epiphy epiphy: An R package to analyze plant disease epidemics.
dogwood_anthracnose Incidence of dogwood anthracnose.
coef.smle Extract Model Coefficients
intensity Construct count, incidence and severity objects.
hop_viruses Incidence of three viruses in an Australian hop garden.
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Vignettes of epiphy

Name
biblio.bib
defs-and-eqns.Rmd
epiphy.Rmd
phytopathology_v2.csl
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Details

Type Package
License MIT + file LICENSE
URL http://epiphy.org, https://github.com/chgigot/epiphy
BugReports https://github.com/chgigot/epiphy/issues
Encoding UTF-8
LazyData true
RoxygenNote 6.0.1
LinkingTo Rcpp
Collate 'RcppExports.R' 'betabinom.R' 'data.R' 'utils.R' 'mle-factory.R' 'epiphy.R' 'intensity-classes.R' 'distr-fitting.R' 'indices.R' 'mapcomp.R' 'power-law.R' 'sadie.R' 'spatial-hier.R'
VignetteBuilder knitr
NeedsCompilation yes
Packaged 2018-05-22 19:20:57 UTC; tof
Repository CRAN
Date/Publication 2018-05-24 13:37:06 UTC

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