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# Table 1 Overview of the evaluated tools

Tool |
t
| RAM | ts. | rep. | m/w | Description | Input | Output | lang. | Reference |
---|---|---|---|---|---|---|---|---|---|---|

χ
^{2}
| 6 s | 221 M | No | No | +/+ |
Pearson χ^{2} test for homogeneity (vectorized implementation)
| freq, cov, Ne | p | R | [14] |

E&R- χ^{2}
| 8 s | 306 M | Yes | No | +/+ |
χ^{2} test adapted to account for drift
| freq, cov, Ne | p | R | [12] |

CLEAR | 3000 s | 1100 M | Yes | Yes | +/+ | Discrete HMM of allele trajectories under a WF model | sync,Ne | s, Ne, h, LL | Python | [11] |

cmh | 216 s | 145 M | No | Yes | +/+ |
Test for homogeneity (similar to χ^{2}) accounting for stratified data
| sync | p | Perl/R | [13] |

E&R-cmh | 8 s | 560 M | Yes | Yes | +/+ | CMH test adapted to account for drift | freq, cov, Ne | p | R | [12] |

LLS | 1091 s (83 h) | 340 M | Yes | Yes | +/+ | Linear model with least square regression of logit-transformed allele frequencies | freq, cov, Ne | p, s, h | R | [14] |

LRT-1 | 31 s | 127 M | No | Yes | −/− | LRT of parallel selection | freq, cov, Ne | LRT, \(\hat \delta \) | Python | [15] |

LRT-2 | 31 s | 127 M | No | Yes | −/− | LRT of heterogeneous selection | freq, cov, Ne |
LRT, dx_{r}
| Python | [15] |

GLM | 220 s | 300 M | Yes | Yes | +/+ | Quasibinomial GLM with replicates and time as predictors | freq | p | R | [16] |

LM | 157 s | 300 M | Yes | Yes | +/+ | LM with replicates and time as predictors | freq | p | R | [16] |

BBGP | 37 h | 15 M | Yes | Yes | +/+ | A Bayesian model of allele trajectories following a Gaussian process | sync | BF | R | [17] |

FIT1 | 16 s | 220 M | Yes | No | −/− |
A t test with allele trajectories modeled as a Brownian process
| freq | p | R | [18] |

FIT2 | 68 s | 220 M | No | Yes | −/− |
A t test with allele frequencies differences between two time points
| freq | p | R | [18] |

WFABC | 42 h | 8 MB | Yes | No | +/+ | ABC of WF dynamics with selection | freq, Ne (h) | BF, s | C++ | [20] |

slattice | 41 h | 250 M | Yes | No | +/+ | HMM of allele trajectories under a WF model using an EM algorithm | freq, Ne (h) | s, LL | R | [19] |