9#include <memory_resource>
10#include <nk/value/value.hpp>
33std::tuple<double, double, double, double, double, double>
35 std::pmr::memory_resource *mr =
nullptr);
49std::tuple<Value, Value, Value, Value>
51 std::pmr::memory_resource *mr =
nullptr);
72std::tuple<Value, Value, Value>
73friedman(
const Value &x,
int reps = 1, std::pmr::memory_resource *mr =
nullptr);
88 std::pmr::memory_resource *mr =
nullptr);
122 std::pmr::memory_resource *mr =
nullptr);
Core MATLAB-compatible multidimensional array and scalar value type.
McCorrection
Multiple-comparison correction methods for multcompare.
Value multcompare(const Value &stats, double alpha=0.05, McCorrection ctype=McCorrection::Bonferroni, std::pmr::memory_resource *mr=nullptr)
Pairwise post-hoc comparisons after one-way ANOVA (c = multcompare(stats [, alpha [,...
std::tuple< double, double, double, double, double, double > anova1(const Value &y, const Value &group, std::pmr::memory_resource *mr=nullptr)
One-way ANOVA ([p, F, df_b, df_w, ss_b, ss_w] = anova1(y, group)).
std::tuple< Value, Value, Value, Value > kruskalwallis(const Value &y, const Value &group, std::pmr::memory_resource *mr=nullptr)
Kruskal–Wallis non-parametric one-way ANOVA ([p, H, df, ssRanks] = kruskalwallis(y,...
Value dummyvar(const Value &group, std::pmr::memory_resource *mr=nullptr)
Convert categorical labels to indicator columns (X = dummyvar(group)).
std::tuple< Value, Value, Value > friedman(const Value &x, int reps=1, std::pmr::memory_resource *mr=nullptr)
Friedman non-parametric two-way ANOVA by ranks ([p, Q, df] = friedman(x, reps)).