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Fisher Scoring Solver

Source: R/solver_newton.R
fisher_scoring.Rd

Variant of Newton-Raphson that uses the expected Fisher information instead of the observed Fisher. Can be more stable for some problems.

Usage

fisher_scoring(
  line_search = TRUE,
  max_iter = 50L,
  tol = 1e-08,
  backtrack_ratio = 0.5,
  min_step = 1e-12,
  verbose = FALSE
)

Arguments

Use backtracking line search for stability

max_iter

Maximum number of iterations

tol

Convergence tolerance (on parameter change)

backtrack_ratio

Step size reduction factor for line search

min_step

Minimum step size before giving up

verbose

Logical; if TRUE and the cli package is installed, display progress during optimization. Default is FALSE.

Value

A solver function

Details

Fisher scoring is identical to Newton-Raphson when the expected and observed Fisher information are equal (e.g., exponential families). For other models, it may have different convergence properties.