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3 The STAT module
3.1 List of AML functions from STAT: alphabetic order
Cluster -
Clustering -
Compare (distributions) -
Compare (vectors) -
Compare (sequences) -
Compare (Markovian models for sequences) -
Compare (Markovian models) -
ComparisonTest -
Compound -
ComputeCorrelation -
ComputePartialAutoCorrelation -
ComputeRankCorrelation -
ComputeSelfTransition -
ComputeStateSequences -
ComputeWhiteNoiseAutoCorrelation -
ContingencyTable -
Convolution -
Cumulate -
Difference -
Display -
Distribution -
Estimate (distributions) -
Estimate (renewal process) -
Estimate (Markovian models) -
Estimate ('top' parameters) -
ExtractData -
ExtractDistribution -
ExtractHistogram -
ExtracVectors -
Fit -
HiddenMarkov -
HiddenSemiMarkov -
Histogram -
IndexSelect -
LengthSelect -
Load -
Markov -
Merge -
MergeVariable -
Mixture -
ModelSelectionTest -
MovingAverage -
NbEventSelect -
Plot, NewPlot -
RecurrenceTimeSequences -
Regression -
RemoveApicalInternodes -
RemoveRun -
Renewal -
Reverse -
Save -
SegmentationExtract -
SelectIndividual -
SelectVariable -
SemiMarkov -
Sequences -
Shift -
Simulate (distributions) -
Simulate (renewal process) -
Simulate (Markovian models) -
Simulate ('topt' parameters) -
Symmetrize -
TimeEvents -
TimeScaling -
TimeSelect -
ToDistribution -
ToHistogram -
TopParameters -
Tops -
Transcode -
TransformPosition -
ValueSelect -
VariableScaling -
VarianceAnalysis -
VectorDistance -
Vectors
3.2 List of AML functions from STAT: by category
 Input/output functions
Compound: construction d'un objet de type COMPOUND
Convolution: CONVOLUTION constructor,
Distribution: DISTRIBUTION constructor,
HiddenMarkov: HIDDEN_MARKOV constructor,
HiddenSemiMarkov: HIDDEN_SEMI-MARKOV constructor,
Histogram: HISTOGRAM constructor,
Markov: MARKOV constructor,
Mixture: MIXTURE constructor,
Renewal: RENEWAL constructor,
SemiMarkov: SEMI-MARKOV constructor,
Sequences: SEQUENCES constructor,
TimeEvents: TIME_EVENTS constructor,
TopParameters: TOP_PARAMETERS constructor,
Tops: TOPS constructor,
VectorDistance: VECTOR_DISTANCE constructor,
Vectors: VECTORS, constructor,
Load: restoration of an object saved as a binary file,
Display: ASCII output,
Plot: graphical output,
Print: ASCII print,
Save: save in a file.
Functions of data manipulation:
Merge: merging of objects of the same 'data' type or merging of sample correlation functions,
Cluster: clustering of values,
Shift: shifting of values,
Transcode: transcoding of values,
SelectIndividual: selection of individuals,
ValueSelect: selection of individuals according to the values taken by a variable.
MergeVariable: merging of variables,
SelectVariable: selection of variables.
set of count data of type {time interval between two observation dates, number of events occurring between these two observation dates}:
NbEventSelect: selection of data item according to a number of events criterion,
TimeScaling: change of the time unit,
TimeSelect: selection of data item according to a length of the observation period criterion.
set of sequences:
Cumulate: sum of successive values along sequences,
Difference: first-order differencing of sequences,
IndexExtract: extraction of sub-sequences corresponding to a range of index parameters,
LengthSelect: selection of sequences according to a length criterion,
MovingAverage: extraction of trends or residuals using a symmetric smoothing filter,
RecurrenceTimeSequences: computation of recurrence time sequences for a given value,
RemoveRun: removal of the first or last run of a given value (for a given variable) in a sequence,
Reverse: reversing of sequences or 'tops',
SegmentationExtract: extraction of sub-sequences by segmentation,
VariableScaling: change of the unit of a variable.
set of 'tops':
RemoveApicalInternodes: removal of the apical internodes of the parent shoot of a 'top'.
dissimilarity matrix:
Symmetrize: symmetrization of a dissimilarity matrix.
Statistical functions:
Clustering: partition in k groups of a set of patterns from a dissimilarity matrix between patterns,
Compare: comparison of frequency distributions, vectors, sequences, Markovian models for sequences or Markovian models,
ComparisonTest: test of comparison of frequency distributions,
ComputeCorrelation: computation of sample autocorrelation or cross-correlation functions,
ComputePartialAutoCorrelation: computation of sample partial autocorrelation functions,
ComputeRankCorrelation: computation of a rank correlation matrix,
ComputeStateSequences: computation of the optimal state sequences corresponding to the observed sequences using a hidden Markov chain or a hidden semi-Markov chain,
ComputeWhiteNoiseAutoCorrelation: computation of the autocorrelation function induced on a white noise sequence by filtering,
ContingencyTable: computation of a contingency table,
Estimate: estimation of distributions, renewal processes, Markovian models or 'top' parametres from data sample,
Fit: fit of a frequency distribution by a theoretical distribution,
ModelSelectionTest: test for selecting the order of a Markov chain or an aggregation of states of a Markov chain,
Regression: simple (either linear or nonparametric) regression,
Simulate: generation of random samples from distributions, renewal processes, Markovian models or 'top' parametres,
VarianceAnalysis: one-way variance analysis.
Miscellaneous functions
ComputeSelfTransition: computation of the self-transition probabilities as a function of the index parameter from discrete sequences,
ExtractData: extraction of the 'data' part of an object of type 'model',
ExtractDistribution: extraction of a distribution from an object of type 'model',
ExtractHistogram: extraction of a frequency distribution from an object of type 'data',
ExtractVectors: extraction of vectors from global characteristics of sequences (length or counting characteristics),
ToDistribution: cast of an object of type HISTOGRAM into an object of type DISTRIBUTION
ToHistogram: cast of an object of type DISTRIBUTION into an object of type HISTOGRAM
TransformPosition: discretization of inter-position intervals.
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