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Directories
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| Path | Synopsis |
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Package classification is a require()-able host module exposing supervised classifiers to .lsc code: a Naive Bayesian text classifier, k-nearest-neighbours, a perceptron, logistic regression, and a kernel SVM.
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Package classification is a require()-able host module exposing supervised classifiers to .lsc code: a Naive Bayesian text classifier, k-nearest-neighbours, a perceptron, logistic regression, and a kernel SVM. |
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Package clustering is a require()-able host module exposing unsupervised clustering algorithms (k-means, DBSCAN, agglomerative/hierarchical, and mean shift) to .lsc code.
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Package clustering is a require()-able host module exposing unsupervised clustering algorithms (k-means, DBSCAN, agglomerative/hierarchical, and mean shift) to .lsc code. |
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Package csv is a require()-able host module for reading and writing CSV, the lingua franca of tabular data.
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Package csv is a require()-able host module for reading and writing CSV, the lingua franca of tabular data. |
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Package dataframe is a require()-able host module providing a column- oriented, pandas-style tabular structure — the central abstraction for most data-science work in LuaScript.
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Package dataframe is a require()-able host module providing a column- oriented, pandas-style tabular structure — the central abstraction for most data-science work in LuaScript. |
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Package linalg is a require()-able host module providing the vector and matrix primitives that underpin most numeric data-science work: dot products, norms, matrix multiplication, transpose, and the small dense solvers (determinant, inverse, linear solve via Gaussian elimination with partial pivoting) needed for, e.g., closed-form linear regression.
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Package linalg is a require()-able host module providing the vector and matrix primitives that underpin most numeric data-science work: dot products, norms, matrix multiplication, transpose, and the small dense solvers (determinant, inverse, linear solve via Gaussian elimination with partial pivoting) needed for, e.g., closed-form linear regression. |
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luaml
Package luaml is the require()-able host module that exposes the ml neural- network engine (native/ml + native/ml/training) to LuaScript as `ml`.
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Package luaml is the require()-able host module that exposes the ml neural- network engine (native/ml + native/ml/training) to LuaScript as `ml`. |
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Package ndarray is a require()-able host module providing a dense, N-dimensional numeric array — the NumPy-style primitive that most data-science numerics build on.
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Package ndarray is a require()-able host module providing a dense, N-dimensional numeric array — the NumPy-style primitive that most data-science numerics build on. |
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Package plot is a require()-able host module that turns numeric data into standalone SVG charts — the "see your data" half of a data-science loop.
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Package plot is a require()-able host module that turns numeric data into standalone SVG charts — the "see your data" half of a data-science loop. |
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Package stats is a require()-able host module providing the descriptive and inferential statistics a data-science workflow reaches for first: central tendency (mean/median/mode), spread (variance/stddev/iqr/range), shape (skewness/kurtosis), relationships (covariance/correlation), and the per-element transforms (zscore/normalize/standardize) used to prepare features.
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Package stats is a require()-able host module providing the descriptive and inferential statistics a data-science workflow reaches for first: central tendency (mean/median/mode), spread (variance/stddev/iqr/range), shape (skewness/kurtosis), relationships (covariance/correlation), and the per-element transforms (zscore/normalize/standardize) used to prepare features. |
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