| Package | Description |
|---|---|
| smile.association |
Frequent item set mining and association rule mining.
|
| smile.base.cart |
Classification and regression tree base package.
|
| smile.base.mlp |
Multilayer perceptron neural network base package.
|
| smile.base.rbf |
RBF network base package.
|
| smile.base.svm |
Support vector machine base package.
|
| smile.classification |
Classification algorithms.
|
| smile.clustering |
Clustering analysis.
|
| smile.clustering.linkage |
Cluster dissimilarity measures.
|
| smile.data |
Data and attribute encapsulation classes.
|
| smile.data.formula |
The formula interface symbolically specifies the predictors
and the response.
|
| smile.data.measure |
Level of measurement or scale of measure.
|
| smile.data.type |
Data types.
|
| smile.data.vector |
Immutable named vectors.
|
| smile.feature |
Feature generation, normalization and selection.
|
| smile.gap |
Genetic algorithm and programming.
|
| smile.glm |
Generalized linear models.
|
| smile.glm.model |
The error distribution models.
|
| smile.graph |
Graphs are mathematical structures used to model pairwise relations between
objects from a certain collection.
|
| smile.hash |
Hashing functions.
|
| smile.imputation |
Missing value imputation.
|
| smile.interpolation |
Interpolation is the process of constructing a function that takes on
specified values at specified points.
|
| smile.interpolation.variogram |
Variogram functions.
|
| smile.io |
Interfaces to read/write a Dataset.
|
| smile.manifold |
Manifold learning finds a low-dimensional basis for describing
high-dimensional data.
|
| smile.math |
Basic mathematical functions, complex, differentiable function interfaces,
random number generators, unconstrained optimization, and raw data type
(int and double) array lists, etc.
|
| smile.math.blas |
BLAS and LAPACK interfaces.
|
| smile.math.blas.mkl |
Intel MKL library.
|
| smile.math.blas.openblas |
OpenBLAS library.
|
| smile.math.distance |
Distance and metric measures.
|
| smile.math.kernel |
Mercer kernels.
|
| smile.math.matrix |
Matrix interface, dense and sparse (band or irregular) matrix encapsulation
classes, LU, QR, Cholesky, SVD and eigen decompositions, etc.
|
| smile.math.random |
High quality random number generators as a replacement of
the standard Random class of Java system.
|
| smile.math.rbf |
Radial basis functions.
|
| smile.math.special |
Special mathematical functions including beta, erf, and gamma.
|
| smile.mds |
Multidimensional scaling.
|
| smile.neighbor |
Nearest neighbor search.
|
| smile.neighbor.lsh |
LSH internal classes.
|
| smile.nlp |
Natural language processing.
|
| smile.nlp.collocation |
Collocation finding algorithms.
|
| smile.nlp.dictionary |
Common dictionaries such as stop words, punctuation, common English words, etc.
|
| smile.nlp.embedding |
Word embedding.
|
| smile.nlp.keyword |
Keyword extraction.
|
| smile.nlp.normalizer |
Text normalization.
|
| smile.nlp.pos |
Part-of-speech taggers.
|
| smile.nlp.relevance |
Term-document relevance ranking algorithms.
|
| smile.nlp.stemmer |
English word stemmer algorithms.
|
| smile.nlp.tokenizer |
Sentence splitter and word tokenizer.
|
| smile.plot.swing |
Mathematical and statistical plots.
|
| smile.projection |
Feature extraction.
|
| smile.projection.ica |
The contrast functions in FastICA.
|
| smile.regression |
Regression analysis.
|
| smile.sequence |
Learning algorithms for sequence data.
|
| smile.sort |
Sorting algorithms.
|
| smile.stat |
Probability distributions and statistical hypothesis tests.
|
| smile.stat.distribution |
Probability distributions.
|
| smile.stat.hypothesis |
Statistical hypothesis tests.
|
| smile.swing |
Enhanced and additional Swing components (FileChooser, FontChooser, Table,
Button, AlphaIcon, and Printer).
|
| smile.swing.table |
Enhancement to Swing JTable and cell components.
|
| smile.taxonomy |
A taxonomy is a tree of terms (concepts) where leaves
must be named but intermediary nodes can be anonymous.
|
| smile.timeseries |
Time series analysis.
|
| smile.util |
Utility functions.
|
| smile.validation |
Model validation and selection.
|
| smile.validation.metric |
Model validation metrics.
|
| smile.vq |
Vector quantization is a lossy compression technique used in speech
and image coding.
|
| smile.vq.hebb |
Hebbian theory is a neuroscientific theory claiming that an increase in
synaptic efficacy arises from a presynaptic cell's repeated and persistent
stimulation of a postsynaptic cell.
|
| smile.wavelet |
Discrete wavelet transform (DWT).
|