Is it possible to enhance it for MICE-imputed pooled GEE-GLM coefficients?

[Generalized]

It partially works, but fails to recognize everything.
It doesn't recognize odds ratios, doesn't format the coefficients nicely like in the examples.

The model is the GEE estimated logistic regression (geepack::geeglm).
There exists a basic tidier:

> tidy(mod)
                                         term estimate std.error statistic p.value        b    df dfcom    fmi lambda  m    riv     ubar
1                                 (Intercept)  -0.2990    0.4300   -0.6953  0.4868 5.74e-03 17876   Inf 0.0327 0.0326 20 0.0337 0.178911
2                                      ArmHD    0.3809    0.6201    0.6142  0.5391 2.18e-02  5378   Inf 0.0598 0.0594 20 0.0632 0.361710
3                          Visit_nOrdMonth 12  -0.0829    0.3678   -0.2253  0.8218 2.10e-02   713   Inf 0.1656 0.1633 20 0.1951 0.113180
4                          Visit_nOrdMonth 20  -0.3074    0.3837   -0.8012  0.4237 3.61e-02   287   Inf 0.2623 0.2572 20 0.3462 0.109375
5                            CurrentSmokerYes  -0.7840    0.7094   -1.1052  0.2691 1.07e-02 38132   Inf 0.0224 0.0223 20 0.0228 0.491951
...

But the results:

> ggstats::ggcoef_table(mod, exponentiate = TRUE)
✖ Unable to identify the list of variables.

This is usually due to an error calling `stats::model.frame(x)`or `stats::model.matrix(x)`.
It could be the case if that type of model does not implement these methods.
Rarely, this error may occur if the model object was created within
a functional programming framework (e.g. using `lappy()`, `purrr::map()`, etc.).

Pooled model data:

mod <- structure(list(call = pool(object = m_longit), m = 20L, pooled = structure(list(
    term = structure(1:30, levels = c("(Intercept)", "ArmHD ", 
    "Visit_nOrdMonth 12", "Visit_nOrdMonth 20", "CurrentSmokerYes", 
    "BMI_centered", "GenderMale", "Age_centered", "ArmHD :Visit_nOrdMonth 12", 
    "ArmHD :Visit_nOrdMonth 20", "ArmHD :CurrentSmokerYes", 
    "ArmHD :BMI_centered", "ArmHD :GenderMale", 
    "ArmHD :Age_centered", "Visit_nOrdMonth 12:CurrentSmokerYes", 
    "Visit_nOrdMonth 20:CurrentSmokerYes", "Visit_nOrdMonth 12:BMI_centered", 
    "Visit_nOrdMonth 20:BMI_centered", "Visit_nOrdMonth 12:GenderMale", 
    "Visit_nOrdMonth 20:GenderMale", "Visit_nOrdMonth 12:Age_centered", 
    "Visit_nOrdMonth 20:Age_centered", "ArmHD :Visit_nOrdMonth 12:CurrentSmokerYes", 
    "ArmHD :Visit_nOrdMonth 20:CurrentSmokerYes", "ArmHD :Visit_nOrdMonth 12:BMI_centered", 
    "ArmHD :Visit_nOrdMonth 20:BMI_centered", "ArmHD :Visit_nOrdMonth 12:GenderMale", 
    "ArmHD :Visit_nOrdMonth 20:GenderMale", "ArmHD :Visit_nOrdMonth 12:Age_centered", 
    "ArmHD :Visit_nOrdMonth 20:Age_centered"), class = "factor"), 
    m = c(20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L), estimate = c(-0.29903187595548, 
    0.380872027401296, -0.0828676502765822, -0.307444671001533, 
    -0.783980495389177, 0.035212462157362, 0.250206944263597, 
    -0.017234017916225, 0.0873716935040566, 0.501669357945944, 
    0.629976239010189, 0.0103309021118499, -0.710440238180912, 
    0.0284496288428124, 0.187255608262129, 0.365859459306186, 
    -0.113801927460189, -0.0214189834022172, 0.639079773673284, 
    0.467942998794559, 0.0336432513729626, -0.0032003933188581, 
    -0.0390287655013661, -0.367468618749712, 0.110564691330047, 
    0.0195080672542059, -0.609947802591084, -0.514663582186983, 
    -0.0191275913422143, 0.0229444015254167), ubar = c(0.178910542612104, 
    0.361709639160786, 0.11318010075047, 0.109374942177015, 0.491950601812907, 
    0.00359752616836495, 0.37258391707241, 0.000779654085582352, 
    0.248436936335551, 0.253871527293263, 0.821531331040922, 
    0.00762841036178101, 0.695736347093203, 0.00144871817311475, 
    0.400419307876188, 0.582910240807128, 0.00341763960905729, 
    0.00448395840623119, 0.203718949538353, 0.322473550400124, 
    0.000569997779978852, 0.000608363225145927, 0.646926965308594, 
    0.899047011197042, 0.00617998444713391, 0.00874507037666418, 
    0.440344746613085, 0.615333409679517, 0.00154075485784863, 
    0.00123053826533071), b = c(0.00574228980863424, 0.0217701125420553, 
    0.0210353227321423, 0.0360599329922958, 0.0106970974728955, 
    0.000609255139901839, 0.0231763298949901, 2.57010039837727e-05, 
    0.0301639128892515, 0.036985935195778, 0.0370945204494253, 
    0.000944934906536697, 0.0306037473509527, 0.000249506763463724, 
    0.202339930508713, 0.094294780937389, 0.000974055403472398, 
    0.0026671211819612, 0.0692512364870469, 0.0402692080443424, 
    8.49167907626829e-05, 0.000182968384479355, 0.250361623612087, 
    0.123376260799054, 0.00190559100079968, 0.00279970449631357, 
    0.133737715221484, 0.0696958456346859, 0.000579398967755914, 
    0.000380137562633506), t = c(0.18493994691117, 0.384568257329944, 
    0.135267189619219, 0.147237871818926, 0.503182554159447, 
    0.00423724406526188, 0.39691906346215, 0.000806640139765313, 
    0.280109044869265, 0.29270675924883, 0.860480577512819, 0.00862059201364454, 
    0.727870281811703, 0.00171070027475166, 0.612876234910337, 
    0.681919760791386, 0.00444039778270331, 0.00728443564729045, 
    0.276432747849752, 0.364756218846684, 0.000659160410279669, 
    0.00080048002884925, 0.909806670101286, 1.02859208503605, 
    0.00818085499797357, 0.0116847600977934, 0.580769347595643, 
    0.688514047595937, 0.00214912377399234, 0.00162968270609589
    ), dfcom = c(Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, 
    Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, 
    Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf), df = c(17875.8312273481, 
    5377.74893584068, 712.625859410522, 287.318926486729, 38132.409398344, 
    833.572847238697, 5054.63256049474, 16975.9654941346, 1486.1201142506, 
    1079.36242884061, 9273.37078501162, 1434.31784421067, 9748.40946606314, 
    810.13651553986, 158.109284183625, 901.293506980286, 358.138852078407, 
    128.552737041216, 274.598980261076, 1413.95059720754, 1038.41293270817, 
    329.855627850284, 227.581899074746, 1197.83817922747, 317.623777540153, 
    300.185847842698, 324.993426903818, 1681.84833695141, 237.105935251678, 
    316.737544169371), riv = c(0.033700665209754, 0.0631960437167034, 
    0.195149931147748, 0.346175539737723, 0.0228314637793895, 
    0.177821610450628, 0.0653145379461191, 0.0346128554726992, 
    0.127485505983443, 0.152971986932216, 0.0474105429704621, 
    0.130064011348216, 0.0461869425864503, 0.180837174889335, 
    0.530586120237343, 0.169853800899371, 0.299258637726326, 
    0.624554687476034, 0.356931932332146, 0.131119803140739, 
    0.156426276439402, 0.315792927255325, 0.406351441336649, 
    0.144091546076687, 0.323766276105696, 0.336153923812171, 
    0.318896959853922, 0.118928432562332, 0.394851207539387, 
    0.324365728405779), lambda = c(0.0326019575530752, 0.0594396904410815, 
    0.163284895109635, 0.25715482826643, 0.0223218238662962, 
    0.150974993897925, 0.0613100972714056, 0.0334548863273935, 
    0.113070638431174, 0.132676239029223, 0.0452645271604824, 
    0.115094375223085, 0.04414788667909, 0.153143192588159, 0.346655515310087, 
    0.145192331527914, 0.230330304557391, 0.384446699326795, 
    0.263043358201978, 0.115920349707134, 0.135266968267993, 
    0.24000199477744, 0.288940181943737, 0.125944070272003, 0.244579637621653, 
    0.251583232905602, 0.241790655040438, 0.106287792052962, 
    0.283077654021558, 0.244921566187188), fmi = c(0.0327101746988321, 
    0.0597892924465919, 0.165623310061771, 0.262272270527071, 
    0.0223730979022412, 0.153004763352921, 0.0616812946217472, 
    0.0335687383891115, 0.114261851084983, 0.134278888452115, 
    0.045470369616539, 0.116325704296666, 0.0443439305672371, 
    0.155226131408954, 0.354766090538579, 0.147082885118919, 
    0.234592763115993, 0.393804973651112, 0.26835286359789, 0.11716821205609, 
    0.1369276602079, 0.244568527181479, 0.295107706982863, 0.127399813343547, 
    0.249291829388427, 0.256520249408316, 0.24641397540461, 0.10734867354401, 
    0.289049371007454, 0.249644680176034)), class = "data.frame", row.names = c(NA, 
-30L)), glanced = NULL), class = c("mipo", "data.frame"))

[Generalized]

Ah sorry, wrong repository. I meant ggstats.

[larmarange]

In fact, both gtsummary and ggstats relies on broom.helpers.

It would help if you post a reproducible example on https://github.com/larmarange/broom.helpers/issues

[larmarange]

If mod is a mira object, use tidy_fun = function(x, ...) {mice::pool(x) %>% mice::tidy(...)}