ttest_rho: Compute bias adjusted t-statistic for parameter rho
In adzemski/netprobitFE: Network probit with fixed effects

Compute bias adjusted t-statistic for parameter rho

ttest_rho(fit_ML, fit_ttest_theta, gammaS = fit_ML$gammaS_hat,
  gammaR = fit_ML$gammaR_hat, theta = fit_ML$theta_hat,
  rho = fit_ML$rho_hat, rho0 = 0, var_meat_estimator = "theorem",
  trimming_bound = 0, force_robust = FALSE)

`fit_ML`	returned object from ML estimation
`fit_ttest_theta`	fitted ttest_theta object
`gammaS`	value used for gammaS (default: estimated value)
`gammaR`	value used for gammaR (default: estimated value)
`theta`	value used for theta (default: estimated value)
`rho`	value used for rho (default: estimated value)
`rho0`	value of theta under null hypothesis (default: zero/test significance)
`var_meat_estimator`	method to compute "meat" of the variance sandwich * "theorem" implement exactly as in theorem (put hats on everything) * "clustering" clustering based on first-order expansion (will not depend on rho_hat! )
`trimming_bound`	specify trimming of probabilities
`force_robust`	don't use speedglm (for robustness in sparse networks)