Similar to NLMIXED procedure in SAS, optim() in R provides the functionality to estimate a model by specifying the log likelihood function explicitly. Below is a demo showing how to estimate a Poisson model by optim() and its comparison with glm() result.

> df <- read.csv('credit_count.csv')
> # ESTIMATE A POISSON MODEL WITH GLM()
> mdl <- glm(MAJORDRG ~ AGE + ACADMOS + ADEPCNT + MINORDRG, family = poisson, data = df)
> summary(mdl)

Call:
glm(formula = MAJORDRG ~ AGE + ACADMOS + ADEPCNT + MINORDRG, 
    family = poisson, data = df)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-9.9940  -0.8907  -0.8079  -0.7633  11.6866  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.3813012  0.0450281 -30.676  < 2e-16 ***
AGE          0.0056126  0.0013616   4.122 3.76e-05 ***
ACADMOS      0.0013437  0.0001975   6.803 1.03e-11 ***
ADEPCNT      0.0803056  0.0093378   8.600  < 2e-16 ***
MINORDRG     0.4499422  0.0068969  65.238  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 24954  on 13443  degrees of freedom
Residual deviance: 22026  on 13439  degrees of freedom
AIC: 28520

Number of Fisher Scoring iterations: 6

> # ESTIMATE A POISSON MODEL WITH OPTIM()
> log.like <- function(par) {
+   xb <- par[1] + par[2] * df$AGE + par[3] * df$ACADMOS + par[4] * df$ADEPCNT + par[5] * df$MINORDRG
+   mu <- exp(xb)
+   ll <- sum(log(exp(-mu) * (mu ^ df$MAJORDRG) / factorial(df$MAJORDRG)))
+   return(-ll)
+ }
> result <- optim(c(0, 0, 0, 0, 0), log.like, hessian = TRUE, method = "BFGS")
> stder <- sqrt(diag(solve(result$hessian)))
> estimate <- data.frame(beta = result$par, stder = stder, z_values = result$par / stder)
> print(estimate)
          beta        stder   z_values
1 -1.380911081 0.0450398804 -30.659741
2  0.005656423 0.0013611828   4.155520
3  0.001298029 0.0001956315   6.635072
4  0.080171673 0.0093427325   8.581180
5  0.450468859 0.0068922289  65.358952