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In the left panel is shown the estimates of the OU-based LIF model, and in the right panel is shown the Feller-based LIF model.
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Finally, Table S5 shows the estimates of N e using the temporal estimator of Wang and Whitlock (2003) in MLN e implemented across sampling intervals of 8 10 generations.
Table 3 and Fig. 4 shows the estimates of missing values by linear spline interpolation and linear regression estimation methods.
Table 2 and Fig. 3 shows the estimates of missing values by linear interpolation, linear spline interpolation and regression estimation methods.
Middle and bottom panels show the estimates of the three types of response kernels.
Table 1 shows the estimates of the coefficient δ, i.e., the public sector pay gap.
Figure 7a shows the estimates of the sample eigenvalue distribution for the first setting.
Table 6 shows the estimates of local mode 1, local mode 5, and inter-area mode.
Top panels show the estimates of μ (left) and σ (right) as box plots.
Right panel of Fig. 7 shows the estimates of the permanent component of total factor productivity growth.
Small circles show the estimates of their centres whereas the large circle is the particle with largest weight.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com