NEWS

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spantest 1.3-0

• span_gl_a() / span_gl_ad(): the C++ sign-flip kernel now forms the restricted residuals via Ehat0s = Ehat1 + (XX %% premult) %% (H %*% Bhat1 - C), avoiding the per-simulation T x (K+1) x N restricted-least-squares matmul. This removes one of the three large per-simulation matrix products, cutting the GL kernel time by roughly 20% (about 1.2x at K = 100, N = 200, T = 250), with bit-identical p-values.
• span_gl_a() and span_gl_ad() now default to do_trace = FALSE (no console output unless requested).
• span_grs(), span_f2() and span_py() now return NA (like the other tests) instead of raising an error when a covariance/design matrix is singular (e.g. collinear benchmarks).
• span_simulate() validates its arguments (dimensions, correlation ranges, degrees of freedom, GARCH stationarity).
• span_as() is faster: the internal subseries t-test (f_ttest) no longer calls stats::t.test() once per column (whose input-checking dominated the runtime). The one-sample two-sided p-values are now computed directly and vectorised across columns, giving identical results (to floating-point) at about 2.5x lower cost for span_as().
• NEW: span_simulate() generates benchmark and test-asset returns for spanning size/power studies from a factor model with a controllable spanning violation (ncp). Innovations may be normal, Student-t, or skew-t, with iid, AR(1), GARCH(1,1), or AR-GARCH dynamics; the dgp argument selects the twelve processes of Ardia and Sessinou (2025). It is a fast, validated drop-in for ad-hoc fGarch::garchSim() loops (roughly 30-45x faster for the GARCH DGPs) and matches the reference processes in distribution, dynamics, and test size/power. The skew-t innovations use a base-R re-implementation of the Fernandez-Steel standardised skew-t (identical draws to fGarch::rsstd given the same RNG state), so the package has no external simulation dependency. Its GARCH(1,1) recursion runs in C++ (bit-for-bit identical to the R loop; floating-point contraction is disabled so the fused multiply-add does not alter the roundings), which about halves the data-generation time for the GARCH DGPs.
• span_gl_a() and span_gl_ad() now run their sign-flip Monte Carlo simulations in C++ (via Rcpp / RcppArmadillo). The random signs and the tie-breaking uniforms are still drawn in R, so the RNG stream -- and hence every p-value and decision -- is unchanged (verified identical to 1.2-1 across dimensions, totsim, and seeds); only the per-simulation SSR / F_max computation moved to a streaming C++ kernel that never forms the large T x (N*totsim) intermediates. About 3x faster than the 1.2-1 R implementation at large K and N, with much lower memory use, which also improves the parallel scaling of simulation studies. Installing from source now requires a C++ compiler (Rcpp and RcppArmadillo were added as build dependencies).

spantest 1.2-1

• span_as() is now vectorized across the test cross-section: benchmark-only QR decompositions are formed once and the swap regressions are obtained by Frisch-Waugh partialling, replacing the per-asset loop of full QR factorizations. Output is numerically identical to 1.2-0 (verified); span_as is roughly 12-15x faster for moderate-to-large test sets.
• span_gl_a() and span_gl_ad() streamlined. The sign-flip simulations are now assembled directly as T x (N*totsim) matrices (recycling the residuals and fitted values across simulations, gathering the sign columns) instead of building a T x N x totsim array and transposing it with aperm(), which removed the dominant cost at large N. In addition the balanced-MC restricted SSR is constant across sign-flips (it equals the raw restricted SSR, since squaring removes the flipped sign) so it is computed once; redundant array/matrix reshapes in the constrained-estimate step were collapsed to matrix products; and unused Sigma allocations were removed. Output is bit-for-bit identical to 1.2-0 (verified via identical()); GL is roughly 1.5-1.7x faster, most visibly for large test cross-sections (e.g. K = 100, N = 200).

spantest 1.2-0

• NEW: span_as(), the Ardia-Sessinou subseries-based Cauchy Combination Test (CCT) for high-dimensional spanning, is now available and exported. It returns combined p-values for the alpha (CCTa), variance/slope (CCTd), and joint (CCTad) nulls, robust to serial/cross-sectional dependence and conditional heteroskedasticity. Internal helper f_getpv() implements the per-asset computation.
• span_bj now implements the Britten-Jones (1999) tangency-spanning test correctly (regression of ones on raw returns); the previous version had no power against alpha alternatives
• span_py now returns NA instead of erroring when there is a single test asset (N = 1)
• f_prods no longer overwrites the caller's global RNG state (.Random.seed is saved and restored)
• span_gl_ad trace now prints the decision label instead of the numeric code
• tail p-values in span_grs and span_py use lower.tail = FALSE for better precision
• added @importFrom Rdpack reprompt to silence the R CMD check NOTE on unused Imports
• documentation: benchmark/test dimensions in span_km, span_gl_a, and span_gl_ad aligned with the rest of the package (R1 is T x K, R2 is T x N)
• added correctness (size/power) tests for span_bj and an N = 1 test for span_py
• added tests for span_as and f_getpv

spantest 1.1-3 (2025-09-09)

• seed is finally removed from GL functions

spantest 1.1-2

• DESCRIPTION modified according to CRAN guidelines
• seed is now an input for GL functions
• error fixed in the test file of span_bj

spantest 1.1-1

• Function span_km and span_bj were testing the same

spantest 1.1-0

• First release public version