Computes exact observation weights for the Kalman filter and smoother, following Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>.
    The package provides tools for analyzing linear Gaussian state-space models,
    allowing users to quantify the contribution of individual observations to
    filtered and smoothed state estimates. These weights can be used for
    interpretation, decomposition, and diagnostic analysis in time series models,
    including applications such as dynamic factor models. See the README for examples.

A suite of routines for Weyl algebras.  Notation follows
   Coutinho (1995, ISBN 0-521-55119-6, "A Primer of Algebraic
   D-Modules").  Uses 'disordR' discipline
   (Hankin 2022 <doi:10.48550/arXiv.2210.03856>).  To cite
   the package in publications, use Hankin
   2022 <doi:10.48550/arXiv.2212.09230>.

Manage skills for large language model coding agents. Supports
    installing skills from 'GitHub' or local directories, tracking versions in
    a lock file, and keeping installations current. Installations can be scoped
    to a single project or shared globally across projects.

Provides a computationally efficient way of fitting
	     weighted linear fixed effects estimators for causal
	     inference with various weighting schemes. Weighted linear
	     fixed effects estimators can be used to estimate the
	     average treatment effects under different identification
	     strategies. This includes stratified randomized
	     experiments, matching and stratification for
	     observational studies, first differencing, and
	     difference-in-differences. The package implements methods
	     described in Imai and Kim (2017) "When should We Use
	     Linear Fixed Effects Regression Models for Causal
	     Inference with Longitudinal Data?", available at
	     <https://imai.fas.harvard.edu/research/FEmatch.html>.

Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes.