agridat

1.26

Agricultural Datasets

9packages depend

54.1Kdownloads / year

0.0%test coverage

13/13checks pass

Overview

About

Maintained by Kevin WrightFirst published 2011-04-2726 releasesCRAN page ↗GitHub ↗

Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("agridat", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("kwstat/agridat")

Development version from kwstat/agridat. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-agridat

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-agridat

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-agridat

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 6.6 min · r-oldrel-windows-x86_64

Code health

Yes

Tests · ratio 0.32

0.0%

Coverage · measured lines

100%

Documentation · exports

Dependencies · direct

Check history

OK2026-06-09
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-06-08
12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
OK2026-05-10
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
WARNING2026-05-09
12 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
OK2026-03-10
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMENoVignettesYes · dynamicpkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo

Examples that run: 7%
Documented parameters: not tracked
Return-value docs: 100%
References docs: 79%

Downloads

54.1K

CRAN downloads in the past year

Rank #1,958 · ~148/day · ~4.5K/mo

Releases R versions

Daily download trend is not available in this view yet.

2.3K30 days

12.7K90 days

54.1K1 year

Compare downloads with other packages →

Also on355 r2u60 autocran97 c2d4u

Repository

GitHub ↗ kwstat/agridat

147Stars

42Forks

0Open issues

0Open PRs

0Releases

313Commits

4Contributors

rstatsdata

313 commits · Last activity 2026-07-17 · +1.4% stars, 30d

Stars over time

2025-06-19 · 1312026-07-16 · 147

Repository practices

Upstream repositoryBeta

5 development-tooling and community-health practices detected across 4 families in the upstream repository

Checks run against github.com/kwstat/agridat on 2026-07-19.

Continuous integration (1)

GitHub Actions

Reproducibility and dev environment (1)

data-raw/

CRAN release process (1)

cran-comments.md

Lint, format, editor (2)

AirVS Code

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

40 external dependencies (excludes base and recommended)

Depends (0)

none

Imports (0)

none

LinkingTo (0)

none

Suggests (45)

AER agricolae betareg broom car coin corrgram desplot dplyr effects emmeans equivalence FrF2 gam gge ggplot2 gnm gstat HH knitrlatticelatticeExtra lme4 lucid mapproj mapsMASSMCMCglmm metaformgcvnlmenullabor ordinal pbkrtest pls pscl qicharts qtl reshape2 rmarkdown sp SpATSsurvivaltestthat vcd

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

9direct

0indirect

fbglm agriutilities connected desplot geneticae gge heritable janusplot spaMM

Code & Tests

Code Composition

Rd 33,475 (86%)Vignettes 5,320 (14%)Other 143 (0%)R 45 (0%)

Code characteristics

Cyclomatic complexity: 4.0 median / 4 max
Test cases: 1 / 0.32 per code line

Test coverage

Line coverage

Expression

0.0%

Tests / Examples

0.0% / – ex

Functions

1 1 exported

Complexity

4 avg / 4 max

Call network

1 nodes / 0 edges

Call graph

Open call graph →

Loading call graph…

Lowest coverage

1 functions

Function	Cyclo	Coverage
libs exp	4	0%

Datasets

Bundled datasets · 391

Name	Class	Rows × Cols	Also ships in
aastveit.barley.covs	data.frame	9 × 20	–
aastveit.barley.height	data.frame	135 × 3	–
acorsi.grayleafspot	data.frame	648 × 4	–
adugna.sorghum	data.frame	289 × 6	–
allcroft.lodging	data.frame	224 × 3	–
alwan.lamb	data.frame	340 × 11	–
ansari.wheat.uniformity	data.frame	768 × 3	–
archbold.apple	data.frame	120 × 8	–
ars.earlywhitecorn96	data.frame	540 × 9	–
australia.soybean	data.frame	464 × 10	–
bachmaier.nitrogen	data.frame	88 × 3	–
bailey.cotton.uniformity	data.frame	794 × 5	–
baker.barley.uniformity	data.frame	570 × 4	–
baker.strawberry.uniformity	data.frame	700 × 4	–
baker.wheat.uniformity	data.frame	225 × 3	–
bancroft.peanut.uniformity	data.frame	216 × 4	–
barrero.maize	data.frame	14,568 × 14	–
batchelor.apple.uniformity	data.frame	224 × 3	–
batchelor.lemon.uniformity	data.frame	364 × 3	–
batchelor.navel1.uniformity	data.frame	1,000 × 3	–
batchelor.navel2.uniformity	data.frame	495 × 3	–
batchelor.valencia.uniformity	data.frame	240 × 3	–
batchelor.walnut.uniformity	data.frame	280 × 3	–
battese.survey	data.frame	37 × 9	–
beall.webworms	data.frame	1,300 × 7	–
beaven.barley	data.frame	160 × 4	–
becker.chicken	data.frame	45 × 3	–
beckett.maize.uniformity	data.frame	83 × 8	–
belamkar.augmented	data.frame	2,700 × 9	–
besag.bayesian	data.frame	225 × 4	–
besag.beans	data.frame	152 × 6	–
besag.checks	data.frame	364 × 4	–
besag.elbatan	data.frame	150 × 4	–
besag.endive	data.frame	2,506 × 3	–
besag.met	data.frame	1,188 × 7	–
besag.triticale	data.frame	54 × 7	–
blackman.wheat	data.frame	168 × 5	–
bliss.borers	data.frame	48 × 3	–
bond.diallel	data.frame	36 × 11	–
bose.multi.uniformity	data.frame	1,170 × 5	–
box.cork	data.frame	112 × 3	–
bradley.multi.uniformity	data.frame	440 × 5	–
brandle.rape	data.frame	135 × 4	–
brandt.switchback	data.frame	30 × 5	–
bridges.cucumber	data.frame	32 × 5	–
broadbalk.wheat	data.frame	1,258 × 4	–
bryan.corn.uniformity	data.frame	1,728 × 4	–
buntaran.wheat	data.frame	1,069 × 6	–
burgueno.alpha	data.frame	48 × 6	–
burgueno.rowcol	data.frame	128 × 5	–
burgueno.unreplicated	data.frame	434 × 4	–
butron.maize	data.frame	245 × 5	–
byers.apple	data.frame	480 × 6	–
caribbean.maize	data.frame	612 × 10	–
carlson.germination	data.frame	120 × 3	–
carmer.density	data.frame	32 × 3	–
cate.potassium	data.frame	24 × 2	–
chakravertti.factorial	data.frame	405 × 7	–
chinloy.fractionalfactorial	data.frame	81 × 10	–
christidis.competition	data.frame	270 × 8	–
christidis.cotton.uniformity	data.frame	1,024 × 4	–
christidis.wheat.uniformity	data.frame	288 × 3	–
cleveland.soil	data.frame	8,641 × 5	–
cochran.beets	data.frame	42 × 4	–
cochran.bib	data.frame	52 × 3	–
cochran.crd	data.frame	32 × 4	–
cochran.eelworms	data.frame	48 × 10	–
cochran.factorial	data.frame	32 × 8	–
cochran.latin	data.frame	36 × 4	–
cochran.lattice	data.frame	80 × 5	–
cochran.wireworms	data.frame	25 × 4	–
connolly.potato	data.frame	80 × 6	–
coombs.rice.uniformity	data.frame	54 × 3	–
cornelius.maize	data.frame	180 × 3	–
corsten.interaction	data.frame	140 × 3	–
cox.stripsplit	data.frame	96 × 5	–
cramer.cucumber	data.frame	24 × 9	–
crampton.pig	data.frame	50 × 5	–
crossa.wheat	data.frame	450 × 5	–
crowder.seeds	data.frame	21 × 5	–
cullis.earlygen	data.frame	670 × 5	–
damesa.maize	data.frame	264 × 8	–
darwin.maize	data.frame	30 × 4	–
dasilva.maize	data.frame	1,485 × 4	–
dasilva.soybean.uniformity	data.frame	1,152 × 3	–
davidian.soybean	data.frame	412 × 5	–
davies.pasture.uniformity	data.frame	760 × 3	–
day.wheat.uniformity	data.frame	3,100 × 4	–
denis.missing	data.frame	130 × 3	–
denis.ryegrass	data.frame	147 × 3	–
depalluel.sheep	data.frame	32 × 5	–
devries.pine	data.frame	36 × 7	–
digby.jointregression	data.frame	134 × 3	–
diggle.cow	data.frame	598 × 5	–
draper.safflower.uniformity	data.frame	640 × 4	–
ducker.groundnut.uniformity	data.frame	215 × 3	–
durban.competition	data.frame	114 × 5	–
durban.rowcol	data.frame	544 × 5	–
durban.splitplot	data.frame	560 × 6	–
eden.nonnormal	data.frame	256 × 3	–
eden.potato	data.frame	225 × 9	–
eden.tea.uniformity	data.frame	144 × 4	–
edwards.oats	data.frame	3,694 × 7	–
engelstad.nitro	data.frame	60 × 4	–
evans.sugarcane.uniformity	data.frame	710 × 3	–
fan.stability	data.frame	260 × 5	–
federer.diagcheck	data.frame	180 × 4	–
federer.tobacco	data.frame	56 × 4	–
fisher.barley	data.frame	60 × 4	–
fisher.latin	data.frame	25 × 4	–
forster.wheat.uniformity	data.frame	160 × 3	–
foulley.calving	data.frame	54 × 4	–
fox.wheat	data.frame	308 × 4	–
garber.multi.uniformity	data.frame	3,060 × 6	–
gartner.corn	data.frame	4,949 × 8	–
gathmann.bt	data.frame	16 × 3	–
gauch.soy	data.frame	1,454 × 6	–
george.wheat	data.frame	13,996 × 5	–
giles.wheat	data.frame	247 × 4	–
gilmour.serpentine	data.frame	330 × 5	–
gilmour.slatehall	data.frame	150 × 5	–
gomez.fractionalfactorial	data.frame	64 × 12	–
gomez.groupsplit	data.frame	270 × 7	–
gomez.heterogeneity	data.frame	21 × 2	–
gomez.heteroskedastic	data.frame	105 × 4	–
gomez.multilocsplitplot	data.frame	108 × 5	–
gomez.nitrogen	data.frame	96 × 4	–
gomez.nonnormal1	data.frame	36 × 3	–
gomez.nonnormal2	data.frame	42 × 3	–
gomez.nonnormal3	data.frame	36 × 3	–
gomez.rice.uniformity	data.frame	648 × 3	–
gomez.seedrate	data.frame	24 × 3	–
gomez.splitplot.subsample	data.frame	192 × 5	–
gomez.splitsplit	data.frame	135 × 7	–
gomez.stripplot	data.frame	54 × 6	–
gomez.stripsplitplot	data.frame	108 × 7	–
gomez.wetdry	data.frame	30 × 4	–
gorski.oats.uniformity	data.frame	500 × 4	–
gotway.hessianfly	data.frame	64 × 6	–
goulden.barley.uniformity	data.frame	2,304 × 3	–
goulden.eggs	data.frame	240 × 2	–
goulden.latin	data.frame	25 × 4	–
goulden.splitsplit	data.frame	160 × 9	–
graybill.heteroskedastic	data.frame	52 × 3	–
gregory.cotton	data.frame	144 × 6	–
grover.diallel	data.frame	144 × 5	–
grover.rcb.subsample	data.frame	144 × 4	–
gumpertz.pepper	data.frame	800 × 6	–
hadasch.lettuce	data.frame	703 × 4	–
hadasch.lettuce.markers	data.frame	89 × 301	–
hanks.sprinkler	data.frame	108 × 7	–
hanover.whitepine	data.frame	112 × 4	–
hansen.multi.uniformity	data.frame	662 × 6	–
haritonenko.sugarbeet.uniformity	data.frame	416 × 3	–
harris.multi.uniformity	data.frame	1,058 × 5	–
harris.wateruse	data.frame	1,040 × 5	–
harrison.priors	data.frame	80 × 5	–
hartman.tomato.uniformity	data.frame	384 × 3	–
harvey.lsmeans	data.frame	65 × 7	–
harville.lamb	data.frame	62 × 4	–
hayman.tobacco	data.frame	256 × 5	–
hazell.vegetables	data.frame	6 × 5	–
heady.fertilizer	data.frame	648 × 6	–
heath.cabbage.uniformity	data.frame	48 × 3	–
heath.radish.uniformity	data.frame	400 × 4	–
henderson.milkfat	data.frame	35 × 2	in 2 packages
hernandez.nitrogen	data.frame	136 × 5	–
hessling.argentina	data.frame	30 × 16	–
hildebrand.systems	data.frame	56 × 4	–
holland.arthropods	data.frame	63 × 8	–
holshouser.splitstrip	data.frame	160 × 8	–
holtsmark.timothy.uniformity	data.frame	240 × 3	–
huehn.wheat	data.frame	200 × 3	–
hughes.grapes	data.frame	270 × 6	–
hunter.corn	data.frame	54 × 4	–
hutchinson.cotton.uniformity	data.frame	2,000 × 3	–
igue.sugarcane.uniformity	data.frame	1,512 × 3	–
ilri.sheep	data.frame	882 × 12	–
immer.sugarbeet.uniformity	data.frame	1,200 × 6	–
ivins.herbs	data.frame	78 × 4	–
iyer.wheat.uniformity	data.frame	2,000 × 3	–
jansen.apple	data.frame	36 × 5	–
jansen.carrot	data.frame	96 × 5	–
jansen.strawberry	data.frame	144 × 5	–
jayaraman.bamboo	data.frame	216 × 5	–
jegorow.oats.uniformity	data.frame	240 × 3	–
jenkyn.mildew	data.frame	38 × 4	–
john.alpha	data.frame	72 × 7	–
johnson.blight	data.frame	25 × 6	–
johnson.douglasfir	data.frame	1,600 × 3	–
jones.corn.uniformity	data.frame	144 × 3	–
jurowski.wheat.uniformity	data.frame	480 × 3	–
kadam.millet.uniformity	data.frame	240 × 4	–
kalamkar.potato.uniformity	data.frame	576 × 3	–
kalamkar.wheat.uniformity	data.frame	1,280 × 4	–
kang.maize	data.frame	204 × 5	–
kang.peanut	data.frame	590 × 4	–
karcher.turfgrass	data.frame	128 × 6	–
kayad.alfalfa	data.frame	8,628 × 4	–
keen.potatodamage	data.frame	1,152 × 6	–
kempton.barley.uniformity	data.frame	196 × 3	–
kempton.competition	data.frame	108 × 5	–
kempton.rowcol	data.frame	68 × 5	–
kempton.slatehall	data.frame	150 × 5	–
kenward.cattle	data.frame	660 × 4	–
kerr.sugarcane.uniformity	data.frame	564 × 4	–
khan.brassica.uniformity	data.frame	648 × 4	–
khin.rice.uniformity	data.frame	1,080 × 3	–
kiesselbach.oats.uniformity	data.frame	207 × 3	–
kirk.potato	data.frame	380 × 5	–
kirk.potato.uniformity	data.frame	36 × 3	–
kling.augmented	data.frame	68 × 7	–
kotowski.potato.uniformity	data.frame	152 × 5	–
kreusler.maize	data.frame	165 × 16	–
kristensen.barley.uniformity	data.frame	242 × 3	–
kulkarni.sorghum.uniformity	data.frame	480 × 4	–
lambert.soiltemp	data.frame	84 × 3	–
lander.multi.uniformity	data.frame	780 × 5	–
larsen.timothy.uniformity	data.frame	960 × 3	–
lasrosas.corn	data.frame	3,443 × 9	–
lavoranti.eucalyptus	data.frame	490 × 4	–
laycock.tea.uniformity	data.frame	86 × 4	–
lee.potatoblight	data.frame	14,570 × 7	–
lehmann.millet.uniformity	data.frame	396 × 5	–
lehmann.rice.uniformity	data.frame	153 × 5	–
lehner.soybeanmold	data.frame	382 × 9	–
lessman.sorghum.uniformity	data.frame	2,640 × 3	–
li.millet.uniformity	data.frame	600 × 3	–
lillemo.wheat	data.frame	408 × 4	–
lin.superiority	data.frame	396 × 4	–
lin.unbalanced	data.frame	405 × 4	–
linder.wheat	data.frame	252 × 4	–
little.splitblock	data.frame	80 × 6	–
loesell.bean.uniformity	data.frame	1,890 × 3	–
lonnquist.maize	data.frame	78 × 3	–
lord.rice.uniformity	data.frame	560 × 5	–
love.cotton.uniformity	data.frame	170 × 3	–
love.sugarcane.uniformity	data.frame	400 × 3	–
lu.stability	data.frame	120 × 4	–
lucas.switchback	data.frame	36 × 5	–
lyon.potato.uniformity	data.frame	204 × 3	–
lyons.wheat	data.frame	48 × 3	–
magistad.pineapple.uniformity	data.frame	137 × 6	–
masood.rice.uniformity	data.frame	288 × 3	–
mcclelland.corn.uniformity	data.frame	438 × 3	–
mcconway.turnip	data.frame	64 × 5	–
mckinstry.cotton.uniformity	data.frame	480 × 3	–
mcleod.barley	data.frame	40 × 10	–
mead.cauliflower	data.frame	14 × 3	–
mead.cowpea.maize	data.frame	72 × 6	–
mead.germination	data.frame	64 × 5	–
mead.lamb	data.frame	36 × 4	–
mead.strawberry	data.frame	32 × 5	–
mead.turnip	data.frame	60 × 4	–
mercer.mangold.uniformity	data.frame	200 × 4	–
mercer.wheat.uniformity	data.frame	500 × 4	–
miguez.biomass	data.frame	212 × 5	–
minnesota.barley.weather	data.frame	719 × 8	–
minnesota.barley.yield	data.frame	2,083 × 5	–
montgomery.wheat.uniformity	data.frame	448 × 4	–
moore.bushbean.uniformity	data.frame	576 × 3	–
moore.carrot.uniformity	data.frame	288 × 3	–
moore.fallcauliflower.uniformity	data.frame	240 × 4	–
moore.polebean.uniformity	data.frame	144 × 3	–
moore.springcauliflower.uniformity	data.frame	240 × 4	–
moore.sweetcorn.uniformity	data.frame	288 × 4	–
nagai.strawberry.uniformity	data.frame	432 × 3	–
nair.turmeric.uniformity	data.frame	864 × 3	–
narain.sorghum.uniformity	data.frame	160 × 3	–
nass.barley	data.frame	4,839 × 4	–
nass.corn	data.frame	6,381 × 4	–
nass.cotton	data.frame	2,338 × 4	–
nass.hay	data.frame	5,044 × 4	–
nass.rice	data.frame	662 × 4	–
nass.sorghum	data.frame	1,647 × 4	–
nass.soybean	data.frame	2,528 × 4	–
nass.wheat	data.frame	5,963 × 4	–
nebraska.farmincome	data.frame	93 × 4	–
nonnecke.peas.uniformity	data.frame	540 × 5	–
nonnecke.sweetcorn.uniformity	data.frame	1,728 × 4	–
obsi.potato.uniformity	data.frame	2,569 × 4	–
odland.soybean.uniformity	data.frame	1,540 × 3	–
odland.soyhay.uniformity	data.frame	1,008 × 3	–
oliveira.lupin.uniformity	data.frame	400 × 3	–
oliveira.potato.uniformity	data.frame	200 × 3	–
omer.sorghum	data.frame	432 × 4	–
onofri.winterwheat	data.frame	168 × 5	–
ortiz.tomato.covs	data.frame	18 × 19	–
ortiz.tomato.yield	data.frame	270 × 4	–
pacheco.soybean	data.frame	198 × 3	–
paez.coffee.uniformity	data.frame	4,190 × 5	–
panse.cotton.uniformity	data.frame	1,280 × 3	–
parker.orange.uniformity	data.frame	1,890 × 4	–
patterson.switchback	data.frame	36 × 4	–
payne.wheat	data.frame	480 × 4	–
pearce.apple	data.frame	24 × 4	–
pearl.kernels	data.frame	59 × 6	–
pederson.lettuce.repeated	data.frame	594 × 4	–
perry.springwheat	data.frame	560 × 6	–
petersen.sorghum.cowpea	data.frame	18 × 5	–
piepho.barley.uniformity	data.frame	1,080 × 3	–
piepho.cocksfoot	data.frame	111 × 3	–
polson.safflower.uniformity	data.frame	1,716 × 3	–
rangaswamy.groundnut.uniformity	data.frame	96 × 3	–
ratkowsky.onions	data.frame	84 × 3	–
rau.coffee.uniformity	data.frame	144 × 3	–
reid.grasses	data.frame	210 × 5	–
riddle.wheat	data.frame	650 × 7	–
ridout.appleshoots	data.frame	270 × 4	–
robinson.peanut.uniformity	data.frame	1,152 × 4	–
roemer.sugarbeet.uniformity	data.frame	192 × 4	–
rothamsted.brussels	data.frame	48 × 5	–
rothamsted.oats	data.frame	96 × 6	–
ryder.groundnut	data.frame	24 × 6	–
salmon.bunt	data.frame	400 × 4	–
saunders.maize.uniformity	data.frame	2,500 × 4	–
sawyer.multi.uniformity	data.frame	144 × 9	–
sayer.sugarcane.uniformity	data.frame	2,056 × 4	–
senshu.rice	data.frame	40 × 7	–
shafi.tomato.uniformity	data.frame	200 × 3	–
shafii.rapeseed	data.frame	648 × 5	–
sharma.met	data.frame	126 × 5	–
shaw.oats	data.frame	390 × 5	–
siao.cotton.uniformity	data.frame	858 × 4	–
silva.cotton	data.frame	250 × 9	–
sinclair.clover	data.frame	25 × 3	–
smith.beans.uniformity	data.frame	912 × 4	–
smith.corn.uniformity	data.frame	360 × 5	–
smith.wheat.uniformity	data.frame	1,080 × 4	–
snedecor.asparagus	data.frame	80 × 4	–
snijders.fusarium	data.frame	204 × 4	–
stephens.sorghum.uniformity	data.frame	2,000 × 3	–
steptoe.morex.pheno	data.frame	2,432 × 10	–
stickler.sorghum.uniformity	data.frame	1,600 × 4	–
stirret.borers	data.frame	60 × 4	–
streibig.competition	data.frame	135 × 8	–
strickland.apple.uniformity	data.frame	198 × 3	–
strickland.grape.uniformity	data.frame	155 × 3	–
strickland.peach.uniformity	data.frame	144 × 3	–
strickland.tomato.uniformity	data.frame	180 × 3	–
stroup.nin	data.frame	242 × 5	–
stroup.splitplot	data.frame	24 × 4	–
student.barley	data.frame	102 × 7	–
summerby.multi.uniformity	data.frame	2,600 × 6	–
tai.potato	data.frame	48 × 6	–
talbot.potato.traits	data.frame	126 × 3	–
talbot.potato.yield	data.frame	108 × 3	–
tesfaye.millet	data.frame	415 × 9	–
theobald.barley	data.frame	105 × 5	–
theobald.covariate	data.frame	256 × 5	–
thompson.cornsoy	data.frame	165 × 12	–
tulaikow.wheat.uniformity	data.frame	480 × 4	–
turner.herbicide	data.frame	12 × 4	–
urquhart.feedlot	data.frame	67 × 5	–
usda.gmoadoption	data.frame	145 × 3	–
usgs.herbicides	data.frame	184 × 19	–
vaneeuwijk.drymatter	data.frame	168 × 4	–
vaneeuwijk.fusarium	data.frame	560 × 4	–
vaneeuwijk.nematodes	data.frame	55 × 3	–
vargas.txe.covs	data.frame	10 × 28	–
vargas.txe.yield	data.frame	240 × 3	–
vargas.wheat1.covs	data.frame	6 × 17	–
vargas.wheat1.traits	data.frame	126 × 19	–
vargas.wheat2.covs	data.frame	21 × 14	–
vargas.wheat2.yield	data.frame	168 × 3	–
verbyla.lupin	data.frame	1,420 × 13	–
vishnaadevi.rice.uniformity	data.frame	288 × 3	–
vold.longterm	data.frame	76 × 3	–
vsn.lupin3	data.frame	1,236 × 5	–
wallace.iowaland	data.frame	99 × 10	–
walsh.cottonprice	data.frame	34 × 9	–
wassom.brome.uniformity	data.frame	3,888 × 4	–
waynick.soil	data.frame	200 × 6	–
wedderburn.barley	data.frame	90 × 3	–
wehner.cucumber.uniformity	data.frame	150 × 3	–
weiss.incblock	data.frame	186 × 5	–
weiss.lattice	data.frame	196 × 5	–
welch.bermudagrass	data.frame	64 × 4	–
wheatley.carrot	data.frame	36 × 6	–
wiebe.wheat.uniformity	data.frame	1,500 × 3	–
wiedemann.safflower.uniformity	data.frame	1,782 × 3	–
williams.barley.uniformity	data.frame	720 × 3	–
williams.cotton.uniformity	data.frame	288 × 3	–
williams.trees	data.frame	192 × 4	–
woodman.pig	data.frame	30 × 10	–
wyatt.multi.uniformity	data.frame	258 × 5	–
yan.winterwheat	data.frame	162 × 3	–
yang.barley	data.frame	108 × 3	–
yates.missing	data.frame	80 × 6	–
yates.oats	data.frame	72 × 8	–
zuidhof.broiler	data.frame	59 × 6	–

People & History

People (1)

Maintainer (1)

Kevin Wright

Author, Maintainer, Copyright holder

Authors (1)

Kevin Wright

Author, Maintainer, Copyright holder

Kevin Wright

Author, Maintainer, Copyright holder

Package Timeline

26 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
1.26Latest
2025-11-19 · current release · diff ↗
1.25
2025-10-08 · diff ↗
R
R 4.5.0 released · 2025-04-11
1.24
2024-10-27 · diff ↗
R
R 4.4.0 released · 2024-04-24
1.23
2024-02-06 · diff ↗
1.22
2023-08-25 · diff ↗
R
R 4.3.0 released · 2023-04-21
1.21
2022-06-15 · diff ↗
R
R 4.2.0 released · 2022-04-22
1.20
2021-12-20 · diff ↗
R
R 4.1.0 released · 2021-05-18
1.18
2021-01-12 · diff ↗
1.17
2020-08-03 · diff ↗
R
R 4.0.0 released · 2020-04-24

Show 27 earlier events

R
R 3.6.0 released · 2019-04-26
1.16
2018-07-06 · diff ↗
1.15
2018-06-28 · diff ↗
R
R 3.5.0 released · 2018-04-23
1.13
2017-11-30 · diff ↗
R
R 3.4.0 released · 2017-04-21
R
R 3.3.0 released · 2016-05-03
1.12
2015-06-29 · diff ↗
R
R 3.2.0 released · 2015-04-16
1.11
2015-03-03 · diff ↗
1.10
2014-11-26 · diff ↗
1.9
2014-07-02 · diff ↗
1.8.1
2014-06-21 · diff ↗
R
R 3.1.0 released · 2014-04-10
1.8
2013-09-23 · diff ↗
1.7
2013-09-06 · diff ↗
1.6
2013-06-04 · diff ↗
1.5
2013-04-26 · diff ↗
R
R 3.0.0 released · 2013-04-03
R
R 2.15.0 released · 2012-03-30
1.4
2012-03-14 · diff ↗
R
R 2.14.0 released · 2011-10-31
1.3
2011-10-20 · diff ↗
1.2
2011-06-30 · diff ↗
1.01
2011-05-02 · diff ↗
1.00
2011-04-27
R
R 2.13.0 released · 2011-04-13

Package metadata

First published: 2011-04-27
Total releases: 26 / 15 yrs
License: MIT + file LICENSE OSI
Bundled data: 7.2 MB / 392 files
Download size: 2.9 MB
Installed size: not tracked yet
With dependencies: not tracked yet

Appears in task views

Agricultural Science Design of Experiments (DoE) & Analysis of Experimental Data

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