# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# ------------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "rolloptim" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'rolloptim: Rolling Optimizations'
version: 1.0.0
abstract: Analytical computation of rolling optimization for time-series data. The
  'rolloptim' package solves constrained quadratic and linear programs in closed form
  by applying Lagrangian multipliers and the Karush-Kuhn-Tucker conditions (Kuhn and
  Tucker, 1951, <https://doi.org/10.1525/9780520411586-036>) to perform mean-variance
  portfolio optimization (Markowitz, 1952, <https://doi.org/10.1111/j.1540-6261.1952.tb01525.x>)
  over rolling windows. For each window, the analytical solution computes the optimal
  weights that minimize variance, maximize expected return, minimize residual sum
  of squares, or maximize quadratic utility, subject to a total-weight equality constraint
  and box bounds on each weight. Use cases include mean-variance portfolio optimization,
  expected-return maximization, and constrained regression. The package supports rolling
  optimizations with constraints via the total, lower, and upper arguments. The implementation
  accepts rolling moments computed via the 'roll' package and uses 'RcppArmadillo'
  for linear algebra, with parallelism across windows provided by 'RcppParallel'.
authors:
- family-names: Foster
  given-names: Jason
  email: jason.j.foster@gmail.com
repository: https://cran.r-universe.dev
repository-code: https://github.com/jasonjfoster/rolloptim
commit: 70c2a43936a62200b4af7e442d35d847d1b1a81e
url: https://github.com/jasonjfoster/rolloptim
date-released: '2026-07-11'
contact:
- family-names: Foster
  given-names: Jason
  email: jason.j.foster@gmail.com