Simulation and Data Analysis for Plant Breeders

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Documentation for package ‘SelectionTools’ version 26.5.2

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A B C D E F G H I L M N O P R S T U V W misc

-- A --

AIC.st.lmm Gaussian Linear Mixed Models
alfalfa Datasets for Linear Mixed Models
anova.st.lmm Gaussian Linear Mixed Models
append.population append.population()
as.data.frame.st.gwas Single-Marker Association Scans with Mixed Models
as.data.frame.st.lmm.result Gaussian Linear Mixed Models
as.data.frame.st.lsm Least-squares means, contrasts and fixed-effect hypotheses
as.data.frame.st.predict Predict Random Effects and Combined Level Means
as.list.st.lmm.result Gaussian Linear Mixed Models
as.matrix.st.lmm.result Gaussian Linear Mixed Models
as.vector.st.lmm.result Gaussian Linear Mixed Models

-- B --

barley.lattice Datasets for Linear Mixed Models
barley.met Datasets for Linear Mixed Models
barley.seven.traits Datasets for Linear Mixed Models
be.quiet Switch SelectionTools output to a quiet mode
BIC.st.lmm Gaussian Linear Mixed Models

-- C --

chickens Datasets for Linear Mixed Models
climate Balanced Climate and Variety Experiment
coef.st.lmm Gaussian Linear Mixed Models
coef.st.lmm.by Fits Independent Gaussian Models to Groups of Observations
coef.st.lsm Least-squares means, contrasts and fixed-effect hypotheses
coef.st.predict Predict Random Effects and Combined Level Means
compare.gwas.fits Single-Marker Association Scans with Mixed Models
concat.population concat.population()
confint.st.lmm Gaussian Linear Mixed Models
confint.st.lmm.bootstrap Bootstrap Statistics from Gaussian Linear Mixed Models
confint.st.lmm.by Fits Independent Gaussian Models to Groups of Observations
confint.st.lmm.profile ML Likelihood Profiles for Gaussian Mixed Models
confint.st.lsm Least-squares means, contrasts and fixed-effect hypotheses
confint.st.predict Predict Random Effects and Combined Level Means
cooks.distance.st.lmm Exact Deletion Influence and Predictive Studentization
cooks.distance.st.lmm.influence Exact Deletion Influence and Predictive Studentization
copy.population copy.population()
corn Corn Yield for a One-Sample Analysis
cotton Datasets for Linear Mixed Models
cross cross()

-- D --

data.params Returns or inspect parameters associated with a data object
define.effects define.effects()
define.effects.df define.effects.df()
define.genome Defines a genome from a linkage map and optional chromosome lengths
define.map define.map()
deprecated Issue information for a deprecated function or object name
df.residual.st.lmm Gaussian Linear Mixed Models
dfbeta.st.lmm Exact Deletion Influence and Predictive Studentization
dfbeta.st.lmm.influence Exact Deletion Influence and Predictive Studentization
dfbetas.st.lmm Exact Deletion Influence and Predictive Studentization
dfbetas.st.lmm.influence Exact Deletion Influence and Predictive Studentization
dh dh()
divide.population divide.population()
drop1.st.lmm Likelihood Comparisons and Fixed-Term Deletion for Mixed Models

-- E --

effect.weight.set.all Sets effect weights for all effects
effmap.remove Removes a stored effect map
effmap.remove.all Removes all stored effect maps
emm_basis.st.lmm Gaussian Linear Mixed Models
evaluate.all.loci Evaluates all loci in a population
evaluate.allele evaluate.allele()
evaluate.allele.freq Calculates or return allele frequencies for selected loci
evaluate.allele.freq2 evaluate.allele.freq2()
evaluate.allele2 evaluate.allele2()
evaluate.genome evaluate.genome()
evaluate.genotype evaluate.genotype()
evaluate.hap.calc Calculates haplotype information for a pair of loci
evaluate.hap.calc.d Calculates a haplotype statistic for a pair of loci
evaluate.hap.free Releases or resets temporary haplotype-evaluation state
evaluate.hap.init Initializes haplotype evaluation for a population
evaluate.hap.return.genotype Returns genotype results from the current haplotype evaluation
evaluate.hap.return.table Returns the table produced by the current haplotype evaluation
evaluate.haplotype Evaluates haplotypes for selected loci in a population
evaluate.ld Evaluates linkage disequilibrium for a population and genomic position
evaluate.locus Evaluates selected loci in a population
evaluate.mdp Evaluates marker-assisted donor-parent information for populations
evaluate.population Evaluates genetic values for simulation populations
extend.marker.map Get a linkage map used by the simulation routines

-- F --

fertilizer Datasets for Linear Mixed Models
fertilizer.regression Fertilizer Dose and Yield for Linear Regression
fertilizer.replicated Replicated Fertilizer Doses for a Regression Lack-of-Fit Test
fitted.st.lmm Gaussian Linear Mixed Models
formula.st.lmm Gaussian Linear Mixed Models

-- G --

gd.allele.frequencies gd.allele.frequencies()
gd.allow.zero.frequencies gd.allow.zero.frequencies()
gd.correct.missing gd.correct.missing()
gd.data.parameters gd.data.parameters()
gd.distance.similarity Gd distance similarity
gd.dummy.populations Molecular marker data for several dummy populations
gd.genetic.distance gd.genetic.distance()
gd.list.irregular gd.list.irregular()
gd.list.missing gd.list.missing()
gd.maize.aflp 51 maize lines analyzed with 462 AFLP markers.
gd.maize.lines RFLP data from 50 maize lines
gd.maize.populations SSR data from seven maize populations
gd.mk.matr Constructs a matrix from genetic-distance results
gd.pcoa gd.pcoa()
gd.salad.aflp Jasminas 44 salads analyzed with 108 AFLP markers.
gd.similarity.coefficient gd.similarity.coefficient()
gd.splitdot Split or transform marker labels used by genetic-diversity routines
gd.status.zero.frequencies Returns status information concerning zero allele frequencies
generate.effect.file generate.effect.file()
generate.map.file generate.map.file()
generate.population generate.population()
genome.contribution genome.contribution()
genome.parameter.get Returns the current genome parameters
genome.parameter.set Sets genome parameters used by the simulation code
genome.segments Extracts or summarizes genome segments for populations
genotype.population genotype.population()
get.genome.par get.genome.par()
get.map get.map()
get.mdp Returns the current marker-assisted donor-parent information
get.population get.population()
get.population.gvalue Get genetic values from a simulation population
get.population.info Returns stored information for individuals in a population
get.population.pvalue Get phenotypic values from a simulation population
get.population.size get.population.size()
get.score get.score()
gs.build.esvs Builds estimation and validation subsets from a base data set
gs.build.tsps Builds training and prediction subsets from a base data set
gs.build.V Builds the genomic-selection variance or covariance matrix for a data set
gs.build.Z gs.build.Z()
gs.check.pvals Checks whether p-values are available for a genomic-selection data set
gs.compare.effects gs.compare.effects()
gs.cross.eval.es Evaluates Crosses by Expected Superior Progeny Value
gs.cross.eval.gd Evaluates Crosses by Genetic Distance
gs.cross.eval.gd.fct Evaluates crosses using genetic-distance function
gs.cross.eval.ma Evaluates Crosses by Maximum Marker-Effect Value
gs.cross.eval.mi Evaluates Crosses by Minimum Marker-Effect Value
gs.cross.eval.mu Evaluates Crosses by Expected Progeny Mean
gs.cross.eval.va Evaluates Crosses by Segregation Variance
gs.cross.info Information for Evaluated Crosses
gs.cross.info.gd Returns or write genetic-distance information for crosses
gs.cross.validation gs.cross.validation()
gs.esteff.lsq Estimates marker effects using the LSQ method
gs.esteff.rmla Estimates marker effects using the RMLA method
gs.esteff.rmlc gs.esteff.rmlc()
gs.esteff.rmlr Estimates marker effects using the RMLR method
gs.esteff.rmlv gs.esteff.rmlv()
gs.esteff.rr Estimates Marker Effects by Ridge Regression
gs.estimate.gv Estimates genomic values for an estimation or prediction data set
gs.get.im Returns the genomic-selection information matrix for a data set
gs.get.info.level Returns the genomic-selection information level for a data set
gs.get.V Returns the genomic-selection variance or covariance matrix for a data set
gs.get.y Returns the genomic-selection response vector for a data set
gs.get.Z Returns the genomic-selection marker or design matrix for a data set
gs.info Prints or handles a genomic-selection information message
gs.lambda.aov Estimates or sets genomic-selection lambda values using the aov method
gs.lambda.const Estimates or sets genomic-selection lambda values using the const method
gs.lambda.emstep Estimates or sets genomic-selection lambda values using the emstep method
gs.lambda.hsq Estimates or sets genomic-selection lambda values using the hsq method
gs.lambda.reg Estimates or sets genomic-selection lambda values using the reg method
gs.lambda.rmla Estimates or sets genomic-selection lambda values using the rmla method
gs.lambda.rmla.02 Estimates or sets genomic-selection lambda values using the rmla.02 method
gs.lambda.rmlc Estimates or sets genomic-selection lambda values using the rmlc method
gs.lambda.rmlr Estimates or sets genomic-selection lambda values using the rmlr method
gs.lambda.rmlv Estimates or sets genomic-selection lambda values using the rmlv method
gs.lambda.rrblup Estimates or sets genomic-selection lambda values using the rrblup method
gs.make.A Constructs a genomic relationship matrix from marker dosages
gs.mme.coeff Gs mme coeff
gs.mme.invcoeff Gs mme invcoeff
gs.mme.restcoeff Gs mme restcoeff
gs.mme.restrhs Gs mme restrhs
gs.mme.rhs Builds or returns the right-hand side for genomic-selection mixed-model equations
gs.mme.solve Gs mme solve
gs.mmet.coeff Builds or solves transformed mixed-model equations (coeff)
gs.mmet.coeff.3 Builds or solves transformed mixed-model equations (coeff.3)
gs.mmet.rhs Builds or solves transformed mixed-model equations (rhs)
gs.mmet.solve Builds or solves transformed mixed-model equations (solve)
gs.neg.effall Sets or process all marker effects using the negative-effect convention
gs.plot.effects gs.plot.effects()
gs.plot.model.fit Plots the Fit of a Genomic Prediction Model
gs.plot.validation gs.plot.validation()
gs.pos.effall Sets or process all marker effects using the positive-effect convention
gs.predict.genotypes gs.predict.genotypes()
gs.reset Reset genomic-selection data and settings
gs.restrict.marker.data.01 Restrict marker data using the package genomic-selection interface
gs.return.effects gs.return.effects()
gs.return.pvals Returns marker p-values for a genomic-selection data set
gs.set.all.info.levels Sets all info levels used by the genomic-selection routines
gs.set.allele.codes Sets allele codes used by the genomic-selection routines
gs.set.effects Sets effects used by the genomic-selection routines
gs.set.info.level Sets info level used by the genomic-selection routines
gs.set.lambda Sets lambda used by the genomic-selection routines
gs.set.num.threads Sets num threads used by the genomic-selection routines
gs.set.performance.data Sets performance data used by the genomic-selection routines
gs.single.marker.aov Performs single-marker analysis for a genomic-selection data set
gs.single.marker.reg Performs single-marker analysis for a genomic-selection data set
gs.start.timer Start or stop the genomic-selection timing helper
gs.stop.timer Start or stop the genomic-selection timing helper
gs.test.mp Tests marker or model parameters used by genomic-selection routines
gs.testeff.lsq Tests estimated marker effects using the indicated method
gs.testeff.rmlc Tests estimated marker effects using the indicated method
gs.testeff.rmlv Tests estimated marker effects using the indicated method
gs.vc.rrblup Estimates variance components for ridge-regression BLUP
gs.w.aov Calculates weights or statistics used by the genomic-selection ANOVA method
gs.write.pseff Writes Marker Effects for the Simulation Backend

-- H --

halfdiallel Maize Half Diallel for Shared Parental GCA and Cross SCA
hatvalues.st.lmm Leverage and Residual Diagnostics for Gaussian Mixed Models
homozygote homozygote()

-- I --

il.eval.library Constructs or evaluates introgression-line libraries
il.eval.lines Constructs or evaluates introgression-line libraries
il.ideal.library Constructs or evaluates introgression-line libraries
il.overlapping.library Constructs or evaluates introgression-line libraries
influence.st.lmm Exact Deletion Influence and Predictive Studentization
info Prints or handles an information message
info.cat Information-level-controlled status output
init.population init.population()
iris.manova Iris Measurements for MANOVA
iris.two Two Iris Species for Hotelling's Test

-- L --

lib00.map1 Linkage Map for Marker-Assisted Backcrossing Examples
lib00.map1a Reduced Linkage Map for Marker-Assisted Backcrossing Examples
lib00.map2 Linkage Map for Three-Stage Marker-Assisted Backcrossing
linkage.drag Calculates or summarize linkage drag
linkage.map.get Get a linkage map used by the simulation routines
linkage.map.load Loads a linkage map used by the simulation routines
linkage.map.save Saves a linkage map used by the simulation routines
list.effects list.effects()
list.populations list.populations
load.effmap load.effmap()
load.internal.effmap Loads an internal effect map
load.linkage.map load.linkage.map()
logLik.st.lmm Gaussian Linear Mixed Models

-- M --

mab.compare Runs and Compares a Series of Marker-Assisted Backcrossing Simulations
mab.df Helper used by the marker-assisted backcross simulation routines
mab.Examples mab.Examples
mab.Input.files Structure of input files for the optimize-mab routines.
mab.load.data Loads the data of a simulation
mab.save.inds Helper used by the marker-assisted backcross simulation routines
mab.simulate Simulates a marker-assisted backcrossing program
mab.tabulate Tabulate the results of a series of simulations
machines Machine and Operator Experiment
maize.aflp 51 maize lines analyzed with 462 AFLP markers.
maize.counties Datasets for Linear Mixed Models
maize.met Datasets for Linear Mixed Models
masbc.input Input Files for Marker-Assisted Backcrossing Examples
material.groups Datasets for Linear Mixed Models
Math.st.lmm.result Gaussian Linear Mixed Models
met.missing Datasets for Linear Mixed Models
met.seven.traits Datasets for Linear Mixed Models
met.unbalanced Datasets for Linear Mixed Models
model.frame.st.lmm Model Information and Updates for Gaussian Mixed Models
model.matrix.st.lmm Gaussian Linear Mixed Models
modelparm.st.lmm Gaussian Linear Mixed Models
mtcars Motor Trend Car Measurements for Regression

-- N --

nobs.st.lmm Gaussian Linear Mixed Models

-- O --

oats Datasets for Linear Mixed Models
Ops.st.lmm.result Gaussian Linear Mixed Models
optimize.population Optimize a population using the package selection routines

-- P --

pairs.st.lsm Least-squares means, contrasts and fixed-effect hypotheses
peas Datasets for Linear Mixed Models
ph.linkage.map.create Creates a linkage map for phenotype or simulation use
phenotype.population Generates phenotypic values for a simulation population
plabsim Performs the SelectionTools operation "plabsim"
plabsim.init Initializes the PLABSIM simulation state
plabsim.R.date Performs the SelectionTools operation "plabsim R date"
plabsim.R.version Performs the SelectionTools operation "plabsim R version"
plabsim.version Performs the SelectionTools operation "plabsim version"
plant Creates or process a plant object in the simulation framework
plot.st.gwas Single-Marker Association Scans with Mixed Models
plot.st.lmm Gaussian Linear Mixed Models
plot.st.lmm.influence Exact Deletion Influence and Predictive Studentization
plot.st.lmm.profile ML Likelihood Profiles for Gaussian Mixed Models
plot.st.lmm.selection.summary Gaussian Linear Mixed Models
population.append Appends a copy of a simulation population
population.concat Concatenate Simulation Populations
population.copy Copy a simulation population
population.cross cross()
population.divide Divide a simulation population
population.exist Checks whether a simulation population exists
population.individual.remove Individual remove a population or population data
population.info.get Returns stored information for an individual
population.info.set Sets stored information for an individual
population.list List a population or population data
population.marker.map Get a linkage map used by the simulation routines
population.matrix.load Matrix load a population or population data
population.matrix.save Matrix save a population or population data
population.name.swap Swap the names of two simulation populations
population.optimize Optimize storage for simulation populations
population.pedigree Returns stored information for an individual
population.plabsim.load Loads a population from PLABSIM-style data
population.plabsim.save Saves a population in PLABSIM-style format
population.remove Removes a population or population data
population.remove.all Removes all simulation populations
population.rename Rename a simulation population
population.resize Resize a population or population data
population.sample Sample a simulation population
population.size.get Returns the size and allocated capacity of a simulation population
population.sort Sort a simulation population by genetic or phenotypic value
population.swap.name Swap simulation population names
population.transfer Transfer a population or population data
predict.st.lmm Gaussian Linear Mixed Models
print.propsd Proportional Loss of Expected Sample Standard Deviation
print.st.cross.allocation Integer Progeny Allocation to Fixed Crosses
print.st.cross.plan Optimum Cross Selection with Integer Families
print.st.data.set.list List SelectionTools Marker Data Sets
print.st.gwas Single-Marker Association Scans with Mixed Models
print.st.gwas.summary Single-Marker Association Scans with Mixed Models
print.st.hybrid.data.set.list supported Hybrid Data Sets
print.st.lmm Gaussian Linear Mixed Models
print.st.lmm.allfit Compares Gaussian mixed-model optimizers and starting values
print.st.lmm.bootstrap Bootstrap Statistics from Gaussian Linear Mixed Models
print.st.lmm.by Fits Independent Gaussian Models to Groups of Observations
print.st.lmm.influence Exact Deletion Influence and Predictive Studentization
print.st.lmm.profile ML Likelihood Profiles for Gaussian Mixed Models
print.st.lmm.result Gaussian Linear Mixed Models
print.st.lmm.selection.summary Gaussian Linear Mixed Models
print.st.lsm Least-squares means, contrasts and fixed-effect hypotheses
print.st.lsm.mvcontrast Least-squares means, contrasts and fixed-effect hypotheses
print.st.ocs Optimum Parental Contributions
print.st.oma OMA: Optimum Mate Allocation
print.st.predict Predict Random Effects and Combined Level Means
print.st.switch.error Calculates Switch Error against Known Phase
print.summary.st.lmm.by Fits Independent Gaussian Models to Groups of Observations
print.summary.st.lsm Least-squares means, contrasts and fixed-effect hypotheses
print.summary.st.predict Predict Random Effects and Combined Level Means
profile.st.lmm ML Likelihood Profiles for Gaussian Mixed Models
propsd Proportional Loss of Expected Sample Standard Deviation

-- R --

rapeseed Datasets for Linear Mixed Models
rapeseed.blocks Datasets for Linear Mixed Models
rats Rats for Partial and Sequential Regression Tests
read.vcf Reads a Beagle-Oriented VCF into SelectionTools
recover_data.st.lmm Gaussian Linear Mixed Models
remove.all.populations remove.all.populations()
remove.effmaps remove.effmaps()
remove.evaluate.population Removes genetic and phenotypic evaluation values
remove.genotype.population Removes cached genotype information from a population
remove.map Removes the linkage map from the simulation
remove.population remove.population()
rename.population rename.population()
reset.all reset.all()
reset.mdp Reset marker-assisted donor-parent information
residuals.st.lmm Gaussian Linear Mixed Models
resize.population Resize a population
resources Returns or display resource information used by SelectionTools
return.population return.population()
rng.choose Chooses the simulation random-number generator state
rng.info Info the simulation random-number generator state
rng.init Initializes the random-number generators
rng.list List the simulation random-number generator state
rstandard.st.lmm Leverage and Residual Diagnostics for Gaussian Mixed Models
rstudent.st.lmm Exact Deletion Influence and Predictive Studentization

-- S --

sample.population sample.population()
save.linkage.map Saves a linkage map
sdev Calculates a standard-deviation quantity used by the simulation code
sel.target.define Defines a selection target
select.all.best Selects all individuals in the best value classes
select.all.best.intern Determines the best value classes in a sorted population
select.genotypes Selects specific allele combinations from populations
select.n.best Selects a fixed number of best individuals
select.n.best.segments Selects the best genome segments according to the requested criterion
set.co.freq Sets crossover-frequency information
set.eff.weight set.eff.weight()
set.genome.par set.genome.par()
set.info.level set.info.level()
set.mdp Sets marker-assisted donor-parent information
set.NoLociInit Sets the initial segment capacity used by the simulation
set.population.gvalue Sets genetic selection values for a simulation population
set.population.info Sets population information used by the simulation routines
show.phase Display a Compact Statistical Phasing Summary
simulate.st.lmm Simulates Gaussian linear mixed-model responses
single.cross Creates or evaluate a single cross
sm.start.timer Start or stop the simulation timing helper
sm.stop.timer Start or stop the simulation timing helper
soils Datasets for Linear Mixed Models
soybean Datasets for Linear Mixed Models
soybean.blocks Datasets for Linear Mixed Models
soybean.met Datasets for Linear Mixed Models
splitdt Split a data object according to the package-specific convention
ssd.mating ssd.mating()
st.calc.ld Calculates Linkage Disequilibrium from Marker Data
st.calc.ld.2 Calculates linkage disequilibrium from marker data
st.calc.q Calculates the package-specific Q statistic or quantity
st.calc.rf Calculates recombination fractions
st.calculate.enrichment Calculates Haplotype Enrichment and Selects Donor Genotypes
st.chrom.stats Returns chromosome statistics for a data set
st.compact.data.set Compacts a Marker Data Set and Repacks Allele Storage
st.copy.marker.data st.copy.marker.data()
st.cross.allocate Integer Progeny Allocation to Fixed Crosses
st.cross.plan Optimum Cross Selection with Integer Families
st.cross.plan.execute Executes Named Families from a Cross Plan
st.data.combine Combine Individuals from Marker Data Sets
st.data.set.list List SelectionTools Marker Data Sets
st.data.set.remove Removes a SelectionTools Marker Data Set
st.data.set.remove.all Removes All SelectionTools Marker Data Sets
st.datadir Constructs a path in the SelectionTools data directory
st.dataframe.to.STvcf Converts a VCF-Like Data Frame to Compact STvcf Format
st.dd Constructs a path in the SelectionTools data directory
st.def.hblocks Defines Haplotype Blocks
st.destroy.phase Randomizes the Stored Phase of a Marker Data Set
st.detect.core Detect a Haplotype Core Collection
st.diallel.cross Identify Unordered Diallel Crosses
st.generate.phenotypes Loads Performance Data from an R Data Set
st.genetic.distances Genetic Distances and Population Plots
st.genetic.distances.02 Calculates genetic distances from SelectionTools marker data
st.genetic.distances.fct Calculates genetic distances from SelectionTools marker data
st.get.A Constructs a genomic relationship matrix from a SelectionTools data set
st.get.hybrid.performance.data supported Hybrid Data Sets
st.get.info.level Returns the current SelectionTools information level
st.get.map Returns the linkage map for a SelectionTools data set
st.get.num.threads Reports the computational thread configuration
st.get.simpop Returns Simulation-Population Data in SelectionTools Format
st.get.simpop.perfdata Returns simulation-population data in SelectionTools format
st.get.simpop2 Returns Simulation-Population Marker Data without a Linkage Map
st.gwas Single-Marker Association Scans with Mixed Models
st.hybrid.data.set supported Hybrid Data Sets
st.hybrid.data.set.list supported Hybrid Data Sets
st.hybrid.data.set.remove supported Hybrid Data Sets
st.hybrid.data.set.remove.all supported Hybrid Data Sets
st.id Constructs a path in the SelectionTools input directory
st.indir Constructs a path in the SelectionTools input directory
st.info Prints or handles a SelectionTools information message
st.keep.intersection.markers Keeps the Marker Intersection of Two SelectionTools Data Sets
st.kernel.structure Verify Kronecker structure in a relationship kernel
st.LDheatmap Plots a Linkage Disequilibrium Heat Map
st.LDplot.ld Prepare linkage-disequilibrium information for plotting
st.LDplot.map Prepare linkage-disequilibrium information for plotting
st.lmm Gaussian Linear Mixed Models
st.lmm.allfit Compares Gaussian mixed-model optimizers and starting values
st.lmm.bootstrap Bootstrap Statistics from Gaussian Linear Mixed Models
st.lmm.by Fits Independent Gaussian Models to Groups of Observations
st.lmm.compare Likelihood Comparisons and Fixed-Term Deletion for Mixed Models
st.lmm.components Components and Random-Coefficient Covariance Diagnostics of a Fitted Model
st.lmm.diagnostics Leverage and Residual Diagnostics for Gaussian Mixed Models
st.lmm.model Model Information and Updates for Gaussian Mixed Models
st.lmm.objective Refits a Retained Gaussian Model and Evaluates Its Likelihood
st.lmm.pca Components and Random-Coefficient Covariance Diagnostics of a Fitted Model
st.lmm.profile ML Likelihood Profiles for Gaussian Mixed Models
st.lmm.refit Refits a Retained Gaussian Model and Evaluates Its Likelihood
st.lmm.simulate Simulates Gaussian linear mixed-model responses
st.lmm.singular Components and Random-Coefficient Covariance Diagnostics of a Fitted Model
st.load.performance.data Loads Performance Data from an R Data Set
st.load.vcf.data Loads Compact STvcf Marker Data with an Optional Linkage Map
st.lsm Least-squares means, contrasts and fixed-effect hypotheses
st.lsm-hypotheses Fixed-Effect Hypotheses Through st.lsm
st.lsm.cld Least-squares means, contrasts and fixed-effect hypotheses
st.lsm.mvcontrast Least-squares means, contrasts and fixed-effect hypotheses
st.lsm.test Least-squares means, contrasts and fixed-effect hypotheses
st.mark.alleles Identify markers by effect and replace selected marker alleles in marker data
st.marker.data.statistics Summarizes a SelectionTools Marker Data Set
st.marker.results Predict Random Effects and Combined Level Means
st.markerdata.to.STvcf Converts a SelectionTools Marker Data Set to STvcf
st.ocs Optimum Parental Contributions
st.od Constructs a path in the SelectionTools output directory
st.oma OMA: Optimum Mate Allocation
st.options Gaussian Linear Mixed Models
st.outdir Constructs a path in the SelectionTools output directory
st.phase Cohort and Reference-Assisted Phasing of Diploid Genotypes
st.plot.corr Plots correlation information from SelectionTools results
st.plot.corr.l Plots correlation information from SelectionTools results
st.plot.gene.diversity st.plot.gene.diversity()
st.plot.genetic.distances Genetic Distances and Population Plots
st.plot.ggt Plots Graphical Genotypes
st.plot.ggt.src Plots Graphical Genotypes from Map and Genotype Objects
st.plot.pcoa Genetic Distances and Population Plots
st.predict Predict Random Effects and Combined Level Means
st.read.map st.read.map()
st.read.map.df Loads a marker map from a data frame or matrix
st.read.marker.data st.read.marker.data()
st.read.marker.data.df Loads marker data from an R data frame or matrix
st.read.performance.data st.read.performance.data()
st.recode.hbc st.recode.hbc()
st.recode.hil Recode Defined Haplotype Blocks as Marker Loci
st.recode.ref Recode Marker Alleles Relative to a Reference Individual
st.recode.ref.2 Recode reference-marker information using the package-specific convention
st.repack.data.set Compacts a Marker Data Set and Repacks Allele Storage
st.reset Reset SelectionTools data and settings
st.restrict.marker.data st.restrict.marker.data()
st.return.performance.data Returns performance data from a SelectionTools data set
st.select.enrichment Calculates Haplotype Enrichment and Selects Donor Genotypes
st.select.phen Selects individuals using phenotypic information
st.set.hblocks Sets haplotype-block information
st.set.hybrid.performance.data supported Hybrid Data Sets
st.set.info.level Sets info level used by SelectionTools
st.set.matrix.ops Sets matrix ops used by SelectionTools
st.set.num.threads Sets the computational thread budget
st.set.openblas.threads Sets openblas threads used by SelectionTools
st.set.sim.ef Transfer Marker Effects to the Simulation Backend
st.set.sim.gp Sets sim gp used by SelectionTools
st.set.sim.mp Sets sim mp used by SelectionTools
st.set.sim.pp Transfer a Marker Population to the Simulation Backend
st.set.simpop Initializes the Simulation Backend from a SelectionTools Data Set
st.start.timer Start or stop the SelectionTools timing helper
st.stop.timer Start or stop the SelectionTools timing helper
st.STvcf.to.dataframe Converts Compact STvcf Data to a VCF-Like Data Frame
st.switch.error Calculates Switch Error against Known Phase
st.write.map Writes linkage-map information for a SelectionTools data set
st.write.marker.data st.write.marker.data()
sugar Datasets for Linear Mixed Models
summarize.gvalue summarize.gvalue()
summarize.gwas.fit Single-Marker Association Scans with Mixed Models
summarize.gwas.simulation Single-Marker Association Scans with Mixed Models
summary.st.lmm Gaussian Linear Mixed Models
summary.st.lmm.allfit Compares Gaussian mixed-model optimizers and starting values
summary.st.lmm.bootstrap Bootstrap Statistics from Gaussian Linear Mixed Models
summary.st.lmm.by Fits Independent Gaussian Models to Groups of Observations
summary.st.lmm.influence Exact Deletion Influence and Predictive Studentization
Summary.st.lmm.result Gaussian Linear Mixed Models
summary.st.lsm Least-squares means, contrasts and fixed-effect hypotheses
summary.st.predict Predict Random Effects and Combined Level Means
swap.population.name Swap population names

-- T --

t.st.lmm.result Gaussian Linear Mixed Models
talk.to.me Enables or controls SelectionTools console output
terms.st.lmm Model Information and Updates for Gaussian Mixed Models
tomato Tomato Plant Heights for Linear Hypotheses
tropmaize Constructed Tropical Maize Complete-Block Trial
tropmaize.lattice Tropical Maize Alpha-Lattice Trials in Five Environments

-- U --

update.st.lmm Model Information and Updates for Gaussian Mixed Models

-- V --

v-dentmaize-vcf STvcf Marker Data for Dent Maize Parents
v-dentmaize2-vcf Mapped Common-Marker Data for Dent Maize Parents
v-flintmaize-vcf STvcf Marker Data for Flint Maize Parents
v-flintmaize2-vcf Mapped Common-Marker Data for Flint Maize Parents
v-hybmaize-phe Phenotype Data for Maize Hybrids
v-hybmaize2-phe Hybrid Phenotypes for the Mapped Maize Parental Data
v-lmm-alfalfa Datasets for Linear Mixed Models
v-lmm-barley-lattice Datasets for Linear Mixed Models
v-lmm-barley-met Datasets for Linear Mixed Models
v-lmm-barley-seven-traits Datasets for Linear Mixed Models
v-lmm-chickens Datasets for Linear Mixed Models
v-lmm-climate Balanced Climate and Variety Experiment
v-lmm-corn Corn Yield for a One-Sample Analysis
v-lmm-cotton Datasets for Linear Mixed Models
v-lmm-data Datasets for Linear Mixed Models
v-lmm-fertilizer Datasets for Linear Mixed Models
v-lmm-fertilizer-regression Fertilizer Dose and Yield for Linear Regression
v-lmm-fertilizer-replicated Replicated Fertilizer Doses for a Regression Lack-of-Fit Test
v-lmm-halfdiallel Maize Half Diallel for Shared Parental GCA and Cross SCA
v-lmm-iris Iris Measurements for MANOVA
v-lmm-iris-two Two Iris Species for Hotelling's Test
v-lmm-machines Machine and Operator Experiment
v-lmm-maize-counties Datasets for Linear Mixed Models
v-lmm-maize-met Datasets for Linear Mixed Models
v-lmm-material-groups Datasets for Linear Mixed Models
v-lmm-met-missing Datasets for Linear Mixed Models
v-lmm-met-seven-traits Datasets for Linear Mixed Models
v-lmm-met-unbalanced Datasets for Linear Mixed Models
v-lmm-mtcars Motor Trend Car Measurements for Regression
v-lmm-oats Datasets for Linear Mixed Models
v-lmm-peas Datasets for Linear Mixed Models
v-lmm-rapeseed Datasets for Linear Mixed Models
v-lmm-rapeseed-blocks Datasets for Linear Mixed Models
v-lmm-rats Rats for Partial and Sequential Regression Tests
v-lmm-soils Datasets for Linear Mixed Models
v-lmm-soybean Datasets for Linear Mixed Models
v-lmm-soybean-blocks Datasets for Linear Mixed Models
v-lmm-soybean-met Datasets for Linear Mixed Models
v-lmm-sugar Datasets for Linear Mixed Models
v-lmm-tomato Tomato Plant Heights for Linear Hypotheses
v-lmm-tropmaize Constructed Tropical Maize Complete-Block Trial
v-lmm-tropmaize-lattice Tropical Maize Alpha-Lattice Trials in Five Environments
v-rapeseed Rapeseed Marker and Flowering-Time Data for the GWAS Vignette
v-tropmaize-map Genetic Map for Tropical Maize
v-tropmaize-phe Phenotypic Data for Tropical Maize
v-tropmaize-pop Genotype Data for Tropical Maize
v-tropmaize-vcf Compacts STvcf Genotype and Genetic-Map Data for Tropical Maize
v.dentmaize.vcf STvcf Marker Data for Dent Maize Parents
v.dentmaize2.vcf Mapped Common-Marker Data for Dent Maize Parents
v.flintmaize.vcf STvcf Marker Data for Flint Maize Parents
v.flintmaize2.vcf Mapped Common-Marker Data for Flint Maize Parents
v.hybmaize.phe Phenotype Data for Maize Hybrids
v.hybmaize2.phe Hybrid Phenotypes for the Mapped Maize Parental Data
v.rapeseed.map Rapeseed Marker and Flowering-Time Data for the GWAS Vignette
v.rapeseed.phe Rapeseed Marker and Flowering-Time Data for the GWAS Vignette
v.rapeseed.vcf Rapeseed Marker and Flowering-Time Data for the GWAS Vignette
v.tropmaize.map Genetic Map for Tropical Maize
v.tropmaize.phe Phenotypic Data for Tropical Maize
v.tropmaize.pop Genotype Data for Tropical Maize
v.tropmaize.vcf Compacts STvcf Genotype and Genetic-Map Data for Tropical Maize
vcov.st.lmm Gaussian Linear Mixed Models
vcov.st.lsm Least-squares means, contrasts and fixed-effect hypotheses
vcov.st.predict Predict Random Effects and Combined Level Means

-- W --

weights.st.lmm Model Information and Updates for Gaussian Mixed Models
write.vcf Writes SelectionTools Marker Data for Beagle
write.version.2 Writes version information in the package-specific format

-- misc --

.st_lmm_emm_DF Gaussian Linear Mixed Models
[.st.lmm.result Gaussian Linear Mixed Models
[[.st.lmm.result Gaussian Linear Mixed Models