https://zhongjun-qu.r-universe.devRecent package updates in zhongjun-quR-universehttps://github.com/zhongjun-qu.pnghttps://zhongjun-qu.r-universe.devFri, 27 Mar 2026 11:59:57 GMTqu@bu.edu (Zhongjun Qu)Methods for detecting structural breaks and estimating break locations for linear multiple regression models under general linear restrictions on the coefficient vector. Restrictions can be within regimes, across regimes, or both, and are supported in two forms: an affine parameterization (Form A: delta = S*theta + s) and explicit linear constraints (Form B: R*delta = r). Provides break date estimation with confidence intervals, a restricted sup-F test for the null of no structural change, simulation of critical values by Monte Carlo, and a bootstrap restart procedure to reduce the risk of convergence to spurious local optima. Also implements a generalized regression tree (linear model tree) procedure where each leaf contains a linear regression model rather than a local average. Reference: Perron, P., and Qu, Z. (2006). 'Estimating Restricted Structural Change Models.' Journal of Econometrics, 134(2), 373-399. <doi:10.1016/j.jeconom.2005.06.030>.https://github.com/r-universe/zhongjun-qu/actions/runs/26498550559Fri, 27 Mar 2026 11:59:57 GMTrbreak1.0.7successhttps://zhongjun-qu.r-universe.devhttps://github.com/cran/rbreakintroduction.Rmdintroduction.htmlIntroduction to rbreak2026-03-27 11:59:572026-03-27 11:59:57qu@bu.edu (Zhongjun Qu)Implements a test for distinguishing between true long memory and spurious long memory. Reference: Qu, Z. (2011). "A Test Against Spurious Long Memory." Journal of Business & Economic Statistics, 29(3), 423–438. <doi:10.1198/jbes.2010.09153>.https://github.com/r-universe/zhongjun-qu/actions/runs/26707964904Mon, 01 Dec 2025 14:40:21 GMTSpuriousMemory1.0.0successhttps://zhongjun-qu.r-universe.devhttps://github.com/cran/SpuriousMemoryintroduction.Rmdintroduction.htmlIntroduction2025-12-01 14:40:212025-12-01 14:40:21qu@bu.edu (Zhongjun Qu)Methods for detecting structural breaks, determining the number of breaks, and estimating break locations in linear quantile regression, using one or multiple quantiles, based on Qu (2008) and Oka and Qu (2011). Applicable to both time series and repeated cross-sectional data. The main function is rq.break(). . References for detailed theoretical and empirical explanations: . (1) Qu, Z. (2008). "Testing for Structural Change in Regression Quantiles." Journal of Econometrics, 146(1), 170-184 <doi:10.1016/j.jeconom.2008.08.006> . (2) Oka, T., and Qu, Z. (2011). "Estimating Structural Changes in Regression Quantiles." Journal of Econometrics, 162(2), 248-267 <doi:10.1016/j.jeconom.2011.01.005>.https://github.com/r-universe/zhongjun-qu/actions/runs/26144581404Wed, 23 Apr 2025 18:44:55 GMTQR.break1.0.2successhttps://zhongjun-qu.r-universe.devhttps://github.com/cran/QR.break