Package | Description |
---|---|
pal.distance |
Classes for reading and generating distance matrices, including computation
of pairwise distances for sequence data (maximum-likelihood and observed
distances).
|
pal.eval |
Classes for evaluating evolutionary hypothesis (chi-square and likelihood
criteria) and estimating model parameters.
|
pal.statistics |
Classes with useful for statistics (normal distribution,
Gamma distribution, chi-square distribution, exponential distribution,
likelihood-ratio test, chi-square test, descriptive statistics, bootstrap estimators etc.)
|
pal.substmodel |
Classes describing substitution models, i.e.
|
pal.supgma | |
pal.tree |
Classes for providing the data structure of
trees, for constructing and modifying trees, and for parameterizing
trees (e.g., clock constraint).
|
pal.treesearch |
Modifier and Type | Method | Description |
---|---|---|
static DistanceMatrix |
DistanceTool.constructEvolutionaryDistances(Alignment a,
SubstitutionModel sm) |
Construct a distance matrix object such that the distance between sequence A, and sequence B, is the
evolutionary distance by a given substitution model.
|
static DistanceMatrixAccess |
DistanceMatrixAccess.Utils.createEvolutionary(Alignment a,
SubstitutionModel sm) |
|
static DistanceMatrixGenerator |
DistanceMatrixGenerator.Utils.createEvolutionary(Alignment a,
SubstitutionModel sm) |
|
static DistanceMatrixGenerator |
DistanceMatrixGenerator.Utils.createParametric(Tree baseTree,
SubstitutionModel sm,
int numberOfSites) |
Silly idea stuff
|
void |
AlignmentDistanceMatrix.recompute(SitePattern sp,
SubstitutionModel model) |
recompute maximum-likelihood distances under new site pattern
|
void |
AlignmentDistanceMatrix.recompute(SitePattern sp,
SubstitutionModel model,
AlgorithmCallback callback) |
recompute maximum-likelihood distances under new site pattern
|
void |
PairwiseDistance.updateModel(SubstitutionModel m) |
update model of substitution
|
void |
SequencePairLikelihood.updateModel(SubstitutionModel m) |
update model of substitution
|
Constructor | Description |
---|---|
AlignmentDistanceMatrix(SitePattern sp,
SubstitutionModel m) |
compute maximum-likelihood distances
|
AlignmentDistanceMatrix(SitePattern sp,
SubstitutionModel m,
AlgorithmCallback callback) |
compute maximum-likelihood distances
|
PairwiseDistance(SitePattern sp,
SubstitutionModel m) |
Constructor 2 (uses evolutionary model)
|
SequencePairLikelihood(SitePattern sp,
SubstitutionModel m) |
initialisation
|
Modifier and Type | Method | Description |
---|---|---|
SubstitutionModel |
LikelihoodValue.getModel() |
Returns the model of this likelihood value.
|
SubstitutionModel |
SiteDetails.getRelatedModel() |
Modifier and Type | Method | Description |
---|---|---|
protected abstract void |
LHCalculator.AbstractExternal.calculateCategoryPatternProbabilities(double distance,
SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftFlatConditionalProbabilities,
ConditionalProbabilityStore rightFlatConditionalProbabilities,
ConditionalProbabilityStore tempStore,
double[][] categoryPatternLogLikelihoodStore) |
|
protected abstract void |
LHCalculator.AbstractExternal.calculateCategoryPatternProbabilities(SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftConditionalProbabilities,
ConditionalProbabilityStore rightConditionalProbabilities,
double[][] categoryPatternLikelihoodStore) |
|
void |
LHCalculator.External.calculateExtended(double distance,
SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftConditionalProbabilities,
ConditionalProbabilityStore rightConditionalProbabilities,
ConditionalProbabilityStore resultStore) |
|
ConditionalProbabilityStore |
LHCalculator.Internal.calculateExtended(double distance,
SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftConditionalProbabilities,
ConditionalProbabilityStore rightConditionalProbabilities,
boolean modelChangedSinceLastCall) |
|
double |
LHCalculator.External.calculateLogLikelihood(double distance,
SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftFlatConditionalProbabilities,
ConditionalProbabilityStore rightFlatConditionalProbabilities,
ConditionalProbabilityStore tempStore) |
Calculate the likelihood given two sub trees (left, right) and their flat (unextend) likeihood probabilities
|
double |
LHCalculator.External.calculateLogLikelihood(SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftConditionalProbabilities,
ConditionalProbabilityStore rightConditionalProbabilities) |
Calculate the likelihood given two sub trees (left, right) and their extended likeihood probabilities
|
static double |
LikelihoodTool.calculateLogLikelihood(Tree tree,
Alignment alignment,
SubstitutionModel model) |
Calculate the log likelihood of a particular set of phylogenetic data
|
double |
LHCalculator.External.calculateLogLikelihoodSingle(SubstitutionModel model,
int[] patternWeights,
int numberOfPatterns,
ConditionalProbabilityStore conditionalProbabilityStore) |
Calculate the likelihood given the conditional probabilites at the root
|
ConditionalProbabilityStore |
LHCalculator.Internal.calculatePostExtendedFlat(double distance,
SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftConditionalProbabilities,
ConditionalProbabilityStore rightConditionalProbabilities,
boolean modelChangedSinceLastCall) |
|
void |
LHCalculator.External.calculateSingleExtendedDirect(double distance,
SubstitutionModel model,
int numberOfPatterns,
ConditionalProbabilityStore conditionalProbabilities) |
Extend the conditionals back in time by some distance, with some model
|
void |
LHCalculator.External.calculateSingleExtendedIndirect(double distance,
SubstitutionModel model,
int numberOfPatterns,
ConditionalProbabilityStore baseConditionalProbabilities,
ConditionalProbabilityStore resultConditionalProbabilities) |
Extend the conditionals back in time by some distance, with some model
|
SiteDetails |
LHCalculator.AbstractExternal.calculateSiteDetailsRooted(SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftConditionalProbabilitiesStore,
ConditionalProbabilityStore rightConditionalProbabilitiesStore) |
|
SiteDetails |
LHCalculator.External.calculateSiteDetailsRooted(SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftConditionalProbabilitiesStore,
ConditionalProbabilityStore rightConditionalProbabilitiesStore) |
Calculate the conditional probabilities of each pattern for each category
|
SiteDetails |
LHCalculator.AbstractExternal.calculateSiteDetailsUnrooted(double distance,
SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftFlatConditionalProbabilities,
ConditionalProbabilityStore rightFlatConditionalProbabilities,
ConditionalProbabilityStore tempStore) |
|
SiteDetails |
LHCalculator.External.calculateSiteDetailsUnrooted(double distance,
SubstitutionModel model,
PatternInfo centerPattern,
ConditionalProbabilityStore leftConditionalProbabilitiesStore,
ConditionalProbabilityStore rightConditionalProbabilitiesStore,
ConditionalProbabilityStore tempStore) |
Calculate the conditional probabilities of each pattern for each category
|
static SiteDetails |
SiteDetails.Utils.create(double[][] categoryPatternConditionalProbabilities,
boolean isLoggedConditionals,
SubstitutionModel model,
int numberOfPatterns,
int[] sitePatternMatchup,
int numberOfSites,
double[] siteLikelihoods) |
Create a Postriors object
|
static MolecularClockLikelihoodModel.Instance |
SimpleMolecularClockLikelihoodModel.createInstance(LHCalculator.Factory baseFactory,
SubstitutionModel model) |
|
static MolecularClockLikelihoodModel.Instance |
SimpleMolecularClockLikelihoodModel.createInstance(SubstitutionModel model) |
|
static UnconstrainedLikelihoodModel.Instance |
SimpleUnconstrainedLikelihoodModel.createInstance(LHCalculator.Factory base,
SubstitutionModel model) |
Create a SimpleUnconstrainedLikelihoodModel instance
|
static UnconstrainedLikelihoodModel.Instance |
SimpleUnconstrainedLikelihoodModel.createInstance(LHCalculator.Generator base,
SubstitutionModel model) |
Create a SimpleUnconstrainedLikelihoodModel instance
|
ConditionalProbabilityStore |
LHCalculator.Leaf.getExtendedConditionalProbabilities(double distance,
SubstitutionModel model,
boolean modelChanged) |
|
ConditionalProbabilityStore |
SimpleLeafCalculator.getExtendedConditionalProbabilities(double distance,
SubstitutionModel model,
boolean modelChanged) |
|
static Alignment |
LikelihoodTool.getMatchingDataType(Alignment alignment,
SubstitutionModel model) |
Creates a new alignment that has a compatible data type with a substution model (needed for likelihood stuff)
|
static double |
LikelihoodOptimiser.optimiseAlternate(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits) |
Optimise parameters to acheive maximum likelihood using an alternating stategy.
|
static double |
LikelihoodOptimiser.optimiseAlternate(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor) |
Optimise parameters to acheive maximum likelihood using an alternating stategy.
|
static Tree |
LikelihoodTool.optimiseClockConstrained(Tree tree,
Alignment alignment,
SubstitutionModel model,
boolean optimiseModel) |
Optimise the branches of a tree with regard to maximum likelihood, with a molecular clock assumption, that is, constrained such that all tips are contemporaneous, the tree is treated as rooted.
|
static double |
LikelihoodOptimiser.optimiseCombined(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits) |
Optimise parameters to acheive maximum likelihood using a combined stategy.
|
static double |
LikelihoodOptimiser.optimiseCombined(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor) |
Optimise parameters to acheive maximum likelihood using a combined stategy.
|
static double |
LikelihoodOptimiser.optimiseModel(Tree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor) |
Optimise model parameters only to acheive maximum likelihood using a combined stategy.
|
static Tree |
LikelihoodTool.optimiseMRDT(Tree tree,
Alignment alignment,
SubstitutionModel model,
TimeOrderCharacterData tocd,
boolean optimiseModel,
double[] rateStore) |
Optimise the branches of a tree with regard to maximum likelihood, with under an assumption of a molecular clock with serially sampled data and multiple mutation rate parameters, mu - one for each sampling interval.
|
static Tree |
LikelihoodTool.optimiseMRDT(Tree tree,
Alignment alignment,
SubstitutionModel model,
TimeOrderCharacterData tocd,
boolean optimiseModel,
double[] rateChangeTimes,
double[] rateStore) |
Optimise the branches of a tree with regard to maximum likelihood, with under an assumption of a molecular clock with serially sampled data and multiple mutation rate parameters, mu, over general time intervals.
|
static Tree |
LikelihoodTool.optimiseSRDT(Tree tree,
Alignment alignment,
SubstitutionModel model,
TimeOrderCharacterData tocd,
boolean optimiseModel,
double[] rateStore) |
Optimise the branches of a tree with regard to maximum likelihood, with under an assumption of a molecular clock with serially sampled data and a single mutation rate parameter.
|
static double |
LikelihoodOptimiser.optimiseTree(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits) |
Optimise tree branchlengths only to acheive maximum likelihood using a combined stategy.
|
static double |
LikelihoodOptimiser.optimiseTree(ParameterizedTree tree,
Alignment alignment,
SubstitutionModel model,
MultivariateMinimum minimiser,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor) |
Optimise tree branchlengths only to acheive maximum likelihood using a combined stategy.
|
static Tree |
LikelihoodTool.optimiseUnrooted(Tree tree,
Alignment alignment,
SubstitutionModel model,
boolean optimiseModel) |
Optimise the branches of a tree with regard to maximum likelihood, with no constraints on the branchlengths (as for an unrooted tree).
|
void |
LikelihoodValue.setModel(SubstitutionModel m) |
define model
(a site pattern must have been set before calling this method)
|
void |
GeneralLikelihoodCalculator.setup(Tree t,
SubstitutionModel model) |
Constructor | Description |
---|---|
GeneralLikelihoodCalculator(Alignment baseAlignment,
Tree tree,
SubstitutionModel model) |
Constructor taking site pattern, tree and a general substitution model.
|
InternalImpl(LHCalculator.Internal base,
SubstitutionModel model) |
|
LeafImpl(LHCalculator.Leaf base,
SubstitutionModel model) |
|
LikelihoodOptimiser(Tree tree,
Alignment alignment,
SubstitutionModel model) |
|
ModelParameters(SitePattern sp,
SubstitutionModel m) |
Constructor
|
Modifier and Type | Method | Description |
---|---|---|
static ReplicateLikelihoodEvaluator |
ReplicateLikelihoodEvaluator.Utils.createRELLEvaluator(SubstitutionModel model) |
Create a ReplicateLikelihoodEvaluator that based likelihood on original tree (does no optimisation)
|
static LikelihoodEvaluator |
LikelihoodEvaluator.Utils.createSimpleEvaluator(SubstitutionModel model) |
Create a simple evaluator that uses UnrootedTreeSearch
|
Modifier and Type | Class | Description |
---|---|---|
class |
GeneralRateDistributionSubstitutionModel |
|
class |
SingleClassSubstitutionModel |
|
static class |
YangCodonModel.SimpleNeutralSelection |
A Substitution Model which can be used to implment the Neutral Model (with out continuous rate stuff)
Codon model of [1] which uses the weighted sum of trwo base YangCodon models where
omega=0, omega=1 repectively
[1] Nielsen, R., Yang Z., 1998 Likelihood Models for Detecting Positively Selected Amino Acid Sites and Applications to the HIV-1 Envelope Gene. |
static class |
YangCodonModel.SimplePositiveSelection |
A Substitution Model which can be used to implment the Postitive Selection (with out continuous rate stuff)
Codon model of [1] which uses the weighted sum of a three base Codon model where
omega=0, omega=1 and omega=free
[1] Nielsen, R., Yang Z., 1998 Likelihood Models for Detecting Positively Selected Amino Acid Sites and Applications to the HIV-1 Envelope Gene. |
Modifier and Type | Field | Description |
---|---|---|
static SubstitutionModel |
F81.JC69_MODEL |
Modifier and Type | Method | Description |
---|---|---|
static SubstitutionModel |
SubstitutionTool.createF81Model(double[] baseFrequencies) |
Create an F81 model of substitution
|
static SubstitutionModel |
SubstitutionTool.createF84Model(double expectedTsTv,
double[] baseFrequencies) |
Create an F84 model of substitution
|
static SubstitutionModel |
SubstitutionTool.createGTRModel(double a,
double b,
double c,
double d,
double e,
double[] baseFrequencies) |
Create an GTR model of substitution
|
static SubstitutionModel |
SubstitutionTool.createJC69Model() |
Create a Jukes-cantor model of substitution
|
static SubstitutionModel |
SubstitutionTool.createM0YangCodonModel(double kappa,
double omega,
double[] baseFrequencies) |
Create an base Yang Codon model (M0) of substitution
|
static SubstitutionModel |
SubstitutionTool.createM1YangCodonModel(double kappa,
double p0,
double[] baseFrequencies) |
Create an neutral Yang Codon model (M1) of substitution
|
static SubstitutionModel |
SubstitutionTool.createM2YangCodonModel(double kappa,
double p0,
double p1,
double omega,
double[] baseFrequencies) |
Create an Positive Yang Codon model (M2) of substitution
|
static SubstitutionModel |
SubstitutionModel.Utils.createSubstitutionModel(NeoRateMatrix rm,
DataType dt,
double[] equilibriumFrequencies) |
|
static SubstitutionModel |
SubstitutionModel.Utils.createSubstitutionModel(RateMatrix rm) |
|
static SubstitutionModel |
SubstitutionModel.Utils.createSubstitutionModel(RateMatrix rm,
RateDistribution rd) |
|
static SubstitutionModel |
SubstitutionModel.Utils.createSubstitutionModel(RateMatrix rm,
RateDistribution rd,
boolean parameteriseDistribution) |
|
static SubstitutionModel |
SubstitutionTool.createTNModel(double kappa,
double r,
double[] baseFrequencies) |
Create an Tamura-Nei model of substitution
|
SubstitutionModel |
GeneralRateDistributionSubstitutionModel.getCopy() |
|
SubstitutionModel |
SingleClassSubstitutionModel.getCopy() |
|
SubstitutionModel |
YangCodonModel.SimpleNeutralSelection.getCopy() |
|
SubstitutionModel |
YangCodonModel.SimplePositiveSelection.getCopy() |
Modifier and Type | Method | Description |
---|---|---|
static double[][][] |
SubstitutionModel.Utils.generateTransitionProbabilityTables(SubstitutionModel model) |
Constructor | Description |
---|---|
SequenceSimulator(SubstitutionModel model,
int sequenceLength,
boolean stochasticDistribution) |
A constructor (with no provided random number generator - a fresh one is created)
|
SequenceSimulator(SubstitutionModel model,
int sequenceLength,
MersenneTwisterFast random,
boolean stochasticDistribution) |
A constructor (with no provided random number generator - a fresh one is created)
|
Modifier and Type | Method | Description |
---|---|---|
SUPGMABase.CISummary |
SUPGMABase.PopulationParameters.inferCI(AlgorithmCallback callback,
int numberOfReplicates,
SimulatedAlignment.Factory alignmentFactory,
SubstitutionModel evolutionaryModel,
LMSSolver solver) |
Constructor | Description |
---|---|
Factory(int sequenceLength,
SubstitutionModel model) |
|
SimulatedAlignment(int sites,
Tree t,
SubstitutionModel m) |
Inititalisation
|
Modifier and Type | Method | Description |
---|---|---|
UnrootedMLSearcher |
BranchAccess.attach(java.lang.String newSequence,
Alignment fullAlignment,
SubstitutionModel model) |
Create a new Tree Searcher with a new sub tree attached
|
UnrootedMLSearcher |
BranchAccess.attach(Node subTree,
Alignment fullAlignment,
SubstitutionModel model) |
Create a new Tree Searcher with a new sub tree attached
|
Tree |
TreeSearchTool.basicUnrootedTreeMLSearch(Alignment a,
SubstitutionModel sm,
boolean optimiseModel) |
Do a basic tree search using maximum likelihood on an unrooted tree space, without a given starting tree
|
Tree |
TreeSearchTool.basicUnrootedTreeMLSearch(Alignment a,
SubstitutionModel sm,
boolean optimiseModel,
AlgorithmCallback callback) |
Do a basic tree search using maximum likelihood on an unrooted tree space, without a given starting tree
|
Tree |
TreeSearchTool.basicUnrootedTreeMLSearch(Tree baseTree,
Alignment a,
SubstitutionModel sm,
boolean optimiseModel) |
Do a basic tree search using maximum likelihood on an unrooted tree space, with a given starting tree
|
Tree |
TreeSearchTool.basicUnrootedTreeMLSearch(Tree baseTree,
Alignment a,
SubstitutionModel sm,
boolean optimiseModel,
AlgorithmCallback callback) |
Do a basic tree search using maximum likelihood on an unrooted tree space, with a given starting tree
|
static double |
TreeSearchTool.calculateLogLikelihood(Tree tree,
Alignment alignment,
SubstitutionModel model) |
Calculate the log likelihood of a particular set of phylogenetic data
|
static Alignment |
TreeSearchTool.getMatchingDataType(Alignment alignment,
SubstitutionModel model) |
Creates a new alignment that has a compatible data type with a substution model (needed for likelihood stuff)
|
static Tree |
TreeSearchTool.optimiseClockConstrainedFixed(Tree tree,
Alignment alignment,
SubstitutionModel model,
boolean optimiseModel,
AlgorithmCallback callback) |
Optimise the branches of a tree with regard to maximum likelihood, with the contraints of a global molecular clock - that is, all the tips terminate at the same point.
|
static Tree |
TreeSearchTool.optimiseUnrootedFixed(Tree tree,
Alignment alignment,
SubstitutionModel model,
boolean optimiseModel) |
Optimise the branches of a tree with regard to maximum likelihood, with no constraints on the branchlengths (as for an unrooted tree).
|
static Tree |
TreeSearchTool.optimiseUnrootedFixed(Tree tree,
Alignment alignment,
SubstitutionModel model,
boolean optimiseModel,
AlgorithmCallback callback) |
Optimise the branches of a tree with regard to maximum likelihood, with no constraints on the branchlengths (as for an unrooted tree).
|
Constructor | Description |
---|---|
UnrootedMLSearcher(Alignment alignment,
SubstitutionModel model) |
Build an unconstrained optimiser based on a randomly generated tree.
|
UnrootedMLSearcher(Alignment alignment,
SubstitutionModel model,
LHCalculator.Factory calcFactory) |
|
UnrootedMLSearcher(Node root,
Alignment alignment,
SubstitutionModel model) |
|
UnrootedMLSearcher(Node root,
Alignment alignment,
SubstitutionModel model,
LHCalculator.Factory calcFactory) |
|
UnrootedMLSearcher(Node root,
SubstitutionModel model) |
Create a searcher based on a given tree, that has no alignment specified (useful as backbone tree for attaching new nodes)
|
UnrootedMLSearcher(Tree t,
Alignment alignment,
SubstitutionModel model) |