```r
m1 <- feglm(t1 ~ conditionC + t0SC | block + day, d4, family = "quasipoisson", cluster = "block")
data.table(tidy(m1))
term estimate std.error statistic p.value
1: conditionC -0.01096426 0.009785825 -1.120422 0.2625576
2: t0SC 0.51859189 0.001463858 354.263850 0.0000000
term estimate std.error statistic p.value cluster
1: conditionC 0.113352418 0.124716949 0.90887741 3.635075e-01 3
2: conditionC -0.002869913 0.060831963 -0.04717772 9.623737e-01 0
3: conditionC -0.104999644 0.210531221 -0.49873669 6.180683e-01 4
4: conditionC -0.013185893 0.010975843 -1.20135590 2.296389e-01 2
5: conditionC -0.353715625 0.275911330 -1.28199022 2.004754e-01 1
6: t0SC 0.520515327 0.001635772 318.20778650 0.000000e+00 2
7: t0SC 0.581546342 0.010773343 53.98011886 0.000000e+00 0
8: t0SC 0.596023188 0.023051877 25.85573327 5.038686e-130 3
9: t0SC 0.621746446 0.061200593 10.15915728 3.339866e-23 4
10: t0SC 0.513839531 0.056620932 9.07508070 3.087779e-18 1
```