@hackage / hanalyze-models

hanalyze-models

The model layer of hanalyze. It owns the model zoo — everything from classical regression through machine learning, multivariate analysis, time series, survival/reliability, and causal inference. It is the largest of the six split layers (67 modules).

It depends on the three layers core (numerical foundation) / frame (dataframe interop) / bayes (MCMC), plus 12 external packages. -design (DoE) and -viz sit on top of this layer. It is the only layer that depends on megaparsec, since it owns the Formula DSL parser.

Main modules (67 in total)

Basic regression (Hanalyze.Model.*)

Formula DSL (Model.Formula.*)

Multivariate & dimensionality reduction

Machine learning

Gaussian processes & multi-output

Time series

Survival & reliability

Causal inference

Miscellaneous

Using it standalone

If you do not need the umbrella package, you can depend on this package directly. Since the FitResult result type lives in the core layer, you also need hanalyze-core explicitly:

build-depends: hanalyze-models, hanalyze-core, hmatrix

import qualified Numeric.LinearAlgebra as LA
import Hanalyze.Model.Core (FitResult (..))
import Hanalyze.Model.LM   (fitLM)

main :: IO ()
main = do
  let x = LA.fromLists [ [1, 1.0], [1, 2.0], [1, 3.0], [1, 4.0], [1, 5.0] ]
      y = LA.fromLists [ [2.1], [3.9], [6.2], [7.8], [10.1] ]
      fit = fitLM x y
  print (LA.toLists (coefficients fit))
  print (LA.toList (rSquared fit))
  -- [[5.000000000000132e-2],[1.9899999999999998]]
  -- [0.9973053289009771]

You must add the intercept column (1) yourself. To build a model matrix from a formula, use Model.Formula.

Normally you would just depend on the umbrella package hanalyze and get all of the above from a single import Hanalyze. Naming a layer directly is only worth it when you want to minimize dependencies.

Related docs

• Linear regression: docs/regression/01-lm.md / GLM: 02-glm.md
• Regularized regression: 04-regularized.md / usage-regularized-advanced.md
• PLS: usage-pls.md / Discriminant analysis (LDA/QDA): usage-discriminant.md
• Weibull / B_p life: usage-weibull.md / Accelerated life testing: usage-reliability.md
• Survival analysis: 10-survival.md / Multi-output: 05-multivariate.md
• Formula DSL: 11-formula-dsl.md
• Causal inference: docs/causal/ / Bayesian optimization: docs/optim/01-singleobj.md

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