modeHuntingApprox(X.raw, lower = -Inf, upper = Inf, d0 = 2, m0 = 10, fm = 2, crit.vals, min.int = FALSE)min.int = TRUE, the set of minimal intervals is output, otherwise all intervals with a test
statistic above the critical value are given.blocks for details how $\mathcal{I}_{app}$ is generated and modeHunting for
a proper introduction to the notation used here.
The function modeHuntingApprox computes $\mathcal{D}^\pm(\alpha)$ based on the two
test statistics $T_n^+({\bf{X}}, \mathcal{I}_{app})$ and $T_n({\bf{X}}, \mathcal{I}_{app})$. If min.int = TRUE, the set $\mathcal{D}^\pm(\alpha)$ is replaced by the set ${\bf{D}}^\pm(\alpha)$
of its minimal elements. An interval $J \in \mathcal{D}^\pm(\alpha)$ is called minimal if
$\mathcal{D}^\pm(\alpha)$ contains no proper subset of $J$. This minimization post-processing
step typically massively reduces the number of intervals. If we are mainly interested in locating the ranges
of increases and decreases of $f$ as precisely as possible, the intervals in
$\mathcal{D}^\pm(\alpha) \setminus \bf{D}^\pm(\alpha)$ do not contain relevant information.
Rufibach, K. and Walther, G. (2010). A general criterion for multiscale inference. J. Comput. Graph. Statist., 19, 175--190.
modeHunting, modeHuntingBlock, and cvModeApprox.## for examples type
help("mode hunting")
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