## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----lm-robust, eval=FALSE----------------------------------------------------
# DAGassist(
#     dag = dag_model,
#     formula = estimatr::lm_robust(
#         ancvs_change_1116 ~ western_belt +
#             unemploy_broad + black_popshare + col_popshare +
#             indasian_popshare + other_popshare + share_formal +
#             share_informal + share_traditional + afrikaans_popshare +
#             english_popshare + isindebele_popshare + isixhosa_popshare +
#             sepedi_popshare + sesotho_popshare + setswana_popshare +
#             signlanguage_popshare + siswati_popshare + tshivenda_popshare +
#             xitsonga_popshare +
#             latitude + longitude + latitudelongitude,
#         data     = df,
#         clusters = cat_b)
# )

## ----feglm, eval=FALSE--------------------------------------------------------
# DAGassist(
#     dag = dag_model2,
#     formula = fixest::feglm(
#         exc_eq ~ cat4_prevalence_best + nonvprotgovt + v2x_gencs +
#             WINGOImputed_fill + UNintervention + majpowintervention + UNwomen +
#             religious + ideolleft + lgt + substantive + v2x_gencl +
#             Krause_wpm_neg + eqgen_binary,
#         data = df %>% filter(interstateonly==0, inter_intrastate==0),
#         family = binomial("logit"),
#         cluster = ~cowcode
#     )
# )

## ----feols, eval=FALSE--------------------------------------------------------
# 
# DAGassist(
#   dag = dag_model,
#   formula = fixest::feols(
#     psoe ~ time * treated2 + log_pop + no_education + growthunemployment+
#       men_50_rs + immigration_rs + ebal_w | province,
#     data = df,
#     cluster = ~municipality)
# )

## ----felm, eval=FALSE---------------------------------------------------------
# DAGassist(dag=dag_model,
#           formula = lfe::felm(
#               share_female ~ lag_democracy_stock_poly_95 + lag_v2xcl_rol +
#                   lag_v2xcl_prpty + lag_v2x_rule + lag_v2x_jucon +
#                   lag_v2xlg_legcon + lag_v2x_corr + lag_v2clstown +
#                   lag_v2xcs_ccsi + lag_v2xps_party | year + country_isocode | 0 |
#                   country_isocode,
#               data=df_cross)
# )

## ----glmer, eval=FALSE--------------------------------------------------------
# 
# DAGassist(
#     dag = dag_model,
#     formula = lme4::glmer(
#         dsu_file ~ specific + lnearnings + distortion + progress + duration +
#             eu + japan + mexico + korea + nonoecd +
#             lntotdirx + lnprod + lnpolcon + active301 +
#             (1 | isic3_4dig),
#         data   = df,
#         family = binomial(link = "logit"),
#         control = glmerControl(optimizer = "bobyqa"))
# )

## ----lmerTest, eval=FALSE-----------------------------------------------------
# 
# DAGassist(
#     dag = dag_model,
#     formula = lmerTest::lmer(
#         ekloges_PERCENTAGE_May_2012_GD ~ ekloges_PERCENTAGE_2009_GD +
#             N_AntifaEvent_notzero2_Oc09_May12 + avg_age + log_population  +
#             prop_ksenoi + (1|code_perif),
#         data=dta))
# 

## ----glm-nb, eval=FALSE-------------------------------------------------------
# DAGassist(
#   dag = dag_model,
#   formula = MASS::glm.nb(
#     protestcount ~ civilcas_percapita + sigact_percapita + log_pop + shiapop_perc
#       kurdpop_perc + urban + totaloilvolx + lognearestcity + feb2011 + nov2010 +
#       laggedprotestcount + illiterate + incomequintilelowest +
#       incomequintilehighest + urate + powercontinuous,
#     data = iraqevents_timeseries_small,
#     control = glm.control(maxit = 50),
#     link = "log")
# )

## ----lm, eval=FALSE-----------------------------------------------------------
# DAGassist(
#   dag = dag_model,
#   formula = stats::lm(
#     restraint ~ nonvio2 + prime_frat + prime_future + prime_RU + prime_UN +
#       oppose92coup2 + civwar + islamist + supp_sharia + active + conscript +
#       soldier + junoff + senoff + branch2 + train_west + train_russia +
#       train_china + young + sex + edu + urban_area + employed + student +
#       arabic + econ1 + corr1 + dem + supp_opp_parties + continue + protested +
#       preboutef + post_exp + mon + factor(fgov),
#     data=data)
# )