A rolling version of the Latent Dirichlet Allocation, see Rieger et al. (2021) <doi:10.18653/v1/2021.findings-emnlp.201>. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.

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,
    <doi:10.1525/9780520411586-036>) to perform mean-variance portfolio
    optimization (Markowitz, 1952,
    <doi: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'.

Provides a unified framework for designing, simulating, and analyzing implementation rollout trials, including stepped wedge, sequential rollout, head-to-head, multi-condition, and rollout implementation optimization designs. The package enables users to flexibly specify rollout schedules, incorporate site-level and nested data structures, generate outcomes under rich hierarchical models, and evaluate analytic strategies through simulation-based power analysis. By separating data generation from model fitting, the tools support assessment of bias, Type I error, and robustness to model misspecification. The workflow integrates with standard mixed-effects modeling approaches and the tidyverse ecosystem, offering transparent and reproducible tools for implementation scientists and applied statisticians.