Conditional Forecasting and Scenario Analysis Using VAR Models R package cforecast version 0.1.1 Provides tools for interpretable conditional forecasting and scenario analysis in reduced-form vector autoregressive (VAR) models. Implements a Kalman smoothing framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions, and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications. The methodology is described in more detail in Caspi and Ginker (2026) <doi:10.13140/RG.2.2.25225.51040>. Software Depends: R (>= 3.5.0) Imports: BVAR, dplyr, FKF, KFAS, miscTools, tibble, vars, utils, methods, wex Tim Ginker <tim.ginker@gmail.com> Comprehensive R Archive Network (CRAN) Tim Ginker [aut, cre] (ORCID: <https://orcid.org/0000-0002-7138-5417>) GPL (>= 3) 2026-06-08 application/tgz https://CRAN.R-project.org/package=cforecast doi:10.32614/CRAN.package.cforecast