R version 4.5.0 beta (2025-03-27 r88065 ucrt) -- "How About a Twenty-Six"
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> require(glarma)
Loading required package: glarma
> ### Boat Race:
> data(OxBoatRace)
> 
> y1 <- OxBoatRace$Camwin
> n1 <- rep(1, length(OxBoatRace$Year))
> Y <- cbind(y1, n1 - y1)
> X <- cbind(OxBoatRace$Intercept, OxBoatRace$Diff)
> colnames(X) <- c("Intercept", "Weight Diff")
> 
> oxcamglm <- glm(Y ~ Diff + I(Diff^2),
+                 data = OxBoatRace,
+                 family = binomial(link = "logit"), x = TRUE)
> 
> X <- oxcamglm$x
> 
> glarmamod <- glarma(Y, X, thetaLags = c(1, 2), type = "Bin", method = "NR",
+                     residuals = "Pearson", maxit = 100, grad = 1e-6)
> 
> summary(glarmamod)

Call: glarma(y = Y, X = X, type = "Bin", method = "NR", residuals = "Pearson", 
    thetaLags = c(1, 2), maxit = 100, grad = 1e-06)

Pearson Residuals:
    Min       1Q   Median       3Q      Max  
-1.8865  -0.7968   0.4395   0.8436   2.9658  

GLARMA Coefficients:
        Estimate Std.Error z-ratio Pr(>|z|)    
theta_1   0.3396    0.1709   1.987 0.046975 *  
theta_2   0.5552    0.1459   3.804 0.000142 ***

Linear Model Coefficients:
             Estimate Std.Error z-ratio Pr(>|z|)   
(Intercept)  0.349954  0.267249   1.309  0.19038   
Diff         0.114755  0.038238   3.001  0.00269 **
I(Diff^2)   -0.011333  0.004916  -2.305  0.02114 * 

    Null deviance: 216.16  on 155  degrees of freedom
Residual deviance: 148.53  on 151  degrees of freedom
AIC: 193.0553 

Number of Newton Raphson iterations: 5

LRT and Wald Test:
Alternative hypothesis: model is a GLARMA process
Null hypothesis: model is a GLM with the same regression structure
          Statistic  p-value    
LR Test       15.56 0.000417 ***
Wald Test     17.51 0.000158 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> 
> par(mfrow=c(3,2))
> plot(glarmamod)
> ### model adequate - test using randomized PIT?
> rt <- normRandPIT(glarmamod)$rt
> par(mfrow = c(2,2))
> hist(rt, main = "Histogram of Randomized Residuals",
+      xlab = expression(r[t]))
> qqnorm(rt, main = "Q-Q Plot of  Randomized Residuals" )
> abline(0, 1, lty = 2)
> acf(rt, main = "ACF of Randomized Residuals")
> pacf(rt, main = "PACF of Randomized Residuals")
> 
> proc.time()
   user  system elapsed 
   0.56    0.03    0.57