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Implemented the base model parameter configuration objects: loss, penalty, penalty flavor, and constraint. Also includes the tuning objects for these model parameters;
infer
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Implements statistical inference procedures such as estimating the degrees of freedom.
metrics
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Computes metrics used for model selection and diagnostics such as information criteria.
opt
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Built in optimization library including FISTA, ADMM, and LLA algorithms. Comes with support for GLM loss functions, penalties and constraints.
cvxpy
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Support for cvxpy based solvers.
pen_max
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Computes the largest reasonable penalty value for combinations of loss + penalties.
solver
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Provides the solver wrapper objects that are used to compute the penalized GLM solutions.
tests
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Test cases for yaglm.
tune
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Handles tuning logistics e.g. cross-validation. Also has some tuning diagnostics.