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Papers: 10.1007/s00122-020-03716-8

https://doi.org/10.1007/s00122-020-03716-8

Linear models for diallel crosses: a review with R functions

Cited by: 11
Author(s): Andrea Onofri, Niccolò Terzaroli, Luigi Russi
Published: over 4 years ago

Software Mentions 19

cran: car
Companion to Applied Regression
Papers that mentioned: 1,651
Very Likely Science (100)
cran: DiallelAnalysisR
Diallel Analysis with R
Papers that mentioned: 1
Very Likely Science (85)
cran: EMMREML
Fitting Mixed Models with Known Covariance Structures
Papers that mentioned: 22
Very Likely Science (85)
cran: lmDiallel
Linear Fixed/Mixed Effects Models for Diallel Crosses
Papers that mentioned: 1
Very Likely Science (85)
cran: lme4
Linear Mixed-Effects Models using 'Eigen' and S4
Papers that mentioned: 9,267
Very Likely Science (100)
cran: MCMCglmm
MCMC Generalised Linear Mixed Models
Papers that mentioned: 477
Very Likely Science (83)
cran: multcomp
Simultaneous Inference in General Parametric Models
Papers that mentioned: 1,558
Very Likely Science (75)
cran: nlme
Linear and Nonlinear Mixed Effects Models
Papers that mentioned: 3,265
Very Likely Science (100)
cran: plyr
Tools for Splitting, Applying and Combining Data
Papers that mentioned: 468
Very Likely Science (100)
cran: rjags
Bayesian Graphical Models using MCMC
Papers that mentioned: 260
Very Likely Science (85)
cran: SAMM
Some Algorithms for Mixed Models
Papers that mentioned: 20
Very Likely Science (85)
cran: sommer
Solving Mixed Model Equations in R
Papers that mentioned: 39
Very Likely Science (75)
pypi: anova
uniform access to multiple ANOVA functions
Papers that mentioned: 95
Very Likely Science (80)
pypi: base
Papers that mentioned: 82
Very Likely Science (65)
pypi: car
Papers that mentioned: 1,651
Unlikely Science (0)
pypi: lm
End to End Language Model Pipeline built for training speed
Papers that mentioned: 267
Very Likely Science (65)
pypi: lme
Cotação London Metal Exchange
Papers that mentioned: 228
Very Likely Science (65)
pypi: plyr
UNKNOWN
Papers that mentioned: 468
Very Likely Science (65)
pypi: summary
Extractor to get main content from the web page.
Papers that mentioned: 12
Very Likely Science (65)