Author: Jared Huling
Date: 2026-05-04

# Quick Usage Guide to the ‘fastglm’ Package

#### Jared Huling

#### 2026-05-04

The fastglm package is intended to be a fast and stable alternative to the `glm()` and `glm2::glm2()` functions for fitting generalized linear models. fastglm is compatible with `R`’s `family` objects (see `?family`). The package can be installed via

    pak::pak("fastglm")
    # pak::pak("jaredhuling/fastglm") #development version

and loaded via:

    library(fastglm)

## Example

The main package function is `fastglm()`. Currently, `fastglm()` does not allow for formula-based data input and is restricted to matrices. We use the following example to demonstrate the usage of `fastglm()`:

    data(esoph)
    x <- model.matrix(~ agegp + unclass(tobgp) + unclass(alcgp),
                      data = esoph)

    y <- cbind(esoph$ncases, esoph$ncontrols)

    gfit1 <- fastglm(x = x, y = y, family = binomial(link = "cloglog"))

    summary(gfit1)
    #>
    #> Call:
    #> fastglm.default(x = x, y = y, family = binomial(link = "cloglog"))
    #>
    #> Coefficients:
    #>                Estimate Std. Error z value Pr(>|z|)
    #> (Intercept)    -4.29199    0.29777 -14.414  < 2e-16 ***
    #> agegp.L         3.30677    0.63454   5.211 1.88e-07 ***
    #> agegp.Q        -1.35525    0.57310  -2.365    0.018 *
    #> agegp.C         0.20296    0.43333   0.468    0.640
    #> agegp^4         0.15056    0.29318   0.514    0.608
    #> agegp^5        -0.23425    0.17855  -1.312    0.190
    #> unclass(tobgp)  0.33276    0.07285   4.568 4.93e-06 ***
    #> unclass(alcgp)  0.80384    0.07538  10.664  < 2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> (Dispersion parameter for binomial family taken to be 1)
    #>
    #>     Null deviance: 61.609  on 87  degrees of freedom
    #> Residual deviance: 96.950  on 80  degrees of freedom
    #> AIC: 228
    #>
    #> Number of Fisher Scoring iterations: 6

Alternatively, to use the built-in function of `glm()` for processing formulas and data frames, we can use `glm()` with `method = fastglm_fit` to call the fastglm fitting functionality:

    gfit2 <- glm(cbind(ncases, ncontrols) ~ agegp + unclass(tobgp) +
                     unclass(alcgp), data = esoph,
                 family = binomial(link = "cloglog"),
                 method = fastglm_fit)

    summary(gfit2, refit = FALSE)
    #>
    #> Call:
    #> glm(formula = cbind(ncases, ncontrols) ~ agegp + unclass(tobgp) +
    #>     unclass(alcgp), family = binomial(link = "cloglog"), data = esoph,
    #>     method = fastglm_fit)
    #>
    #> Coefficients:
    #>                Estimate Std. Error z value Pr(>|z|)
    #> (Intercept)    -4.29199    0.29777 -14.414  < 2e-16 ***
    #> agegp.L         3.30677    0.63454   5.211 1.88e-07 ***
    #> agegp.Q        -1.35525    0.57310  -2.365    0.018 *
    #> agegp.C         0.20296    0.43333   0.468    0.640
    #> agegp^4         0.15056    0.29318   0.514    0.608
    #> agegp^5        -0.23425    0.17855  -1.312    0.190
    #> unclass(tobgp)  0.33276    0.07285   4.568 4.93e-06 ***
    #> unclass(alcgp)  0.80384    0.07538  10.664  < 2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> (Dispersion parameter for binomial family taken to be 1)
    #>
    #>     Null deviance: 367.95  on 87  degrees of freedom
    #> Residual deviance:  96.95  on 80  degrees of freedom
    #> AIC: 1099.6
    #>
    #> Number of Fisher Scoring iterations: 6

Setting `refit = FALSE` in `summary()` uses the standard errors computed by the internal fitting functions of fastglm. The default, `refit = TRUE`, re-fits the model using `glm()` with `method = "glm.fit"` and one IRLS iteration with the coefficients computed previously as the starting values.

## Computational stability

fastglm does not compromise computational stability for speed. In fact, for many situations where `glm()` and even `glm2::glm2()` do not converge, `fastglm()` does converge.

As an example, consider the following data scenario, where the response distribution is (mildly) misspecified, but the link function is quite badly misspecified. In such scenarios, the standard IRLS algorithm tends to have convergence issues. The glm2 package was designed to handle such cases, however, it still can have convergence issues. fastglm uses a similar step-halving technique as `glm2::glm2()`, but it starts at better initialized values and thus tends to have better convergence properties in practice.

    set.seed(1)
    x <- matrix(rnorm(10000 * 100), ncol = 100)
    y <- exp(0.25 * x[,1] - 0.25 * x[,3] + 0.5 * x[,4] - 0.5 * x[,5] + rnorm(10000)) + 0.1

    # fastglm
    system.time(gfit1 <- glm(y ~ x, family = Gamma(link = "sqrt"),
                             method = fastglm_fit))
    #>    user  system elapsed
    #>   0.275   0.005   0.281

    # glm
    system.time(gfit2 <- glm(y ~ x, family = Gamma(link = "sqrt")))
    #>    user  system elapsed
    #>   1.425   0.027   1.458

    # glm2
    system.time(gfit3 <- glm(y ~ x, family = Gamma(link = "sqrt"),
                             method = glm2::glm.fit2))
    #>    user  system elapsed
    #>   1.012   0.023   1.040

    ## Note that fastglm() returns estimates with the
    ## largest likelihood
    logLik(gfit1)
    #> 'log Lik.' -16046.66 (df=102)
    logLik(gfit2)
    #> 'log Lik.' -16704.05 (df=102)
    logLik(gfit3)
    #> 'log Lik.' -16046.66 (df=102)

    coef(gfit1)[1:5]
    #>   (Intercept)            x1            x2            x3            x4
    #>  1.426850e+00  1.242357e-01 -7.475901e-05 -1.254684e-01  2.361046e-01
    coef(gfit2)[1:5]
    #>   (Intercept)            x1            x2            x3            x4
    #>  1.431168e+00  1.251936e-01 -6.896739e-05 -1.281857e-01  2.366473e-01
    coef(gfit3)[1:5]
    #>   (Intercept)            x1            x2            x3            x4
    #>  1.426864e+00  1.242616e-01 -9.860241e-05 -1.254873e-01  2.361301e-01

    ## check convergence of fastglm
    gfit1$converged
    #> [1] TRUE
    ## number of IRLS iterations
    gfit1$iter
    #> [1] 16

    ## now check convergence for glm()
    gfit2$converged
    #> [1] FALSE
    gfit2$iter
    #> [1] 25

    ## check convergence for glm2()
    gfit3$converged
    #> [1] TRUE
    gfit3$iter
    #> [1] 19

    ## increasing number of IRLS iterations for glm() does not help that much
    system.time(gfit2 <- glm(y ~ x, family = Gamma(link = "sqrt"),
                             control = list(maxit = 100)))
    #>    user  system elapsed
    #>   5.468   0.129   5.678

    gfit2$converged
    #> [1] FALSE
    gfit2$iter
    #> [1] 100

    logLik(gfit1)
    #> 'log Lik.' -16046.66 (df=102)
    logLik(gfit2)
    #> 'log Lik.' -16068.27 (df=102)