Improved score tests are modifications of the score test such that the null distribution of the modified test statistic is better approximated by the chi-squared distribution. The literature includes theoretical and empirical evidence favoring the improved test over its unmodified version. However, the developed methodology seems to have been overlooked by data analysts in practice, possibly because of the difficulties associated with the computation of the modified test. In this article, we describe the mdscore package to compute improved score tests in generalized linear models, given a fitted model by the glm() function in R. The package is suitable for applied statistics and simulation experiments. Examples based on real and simulated data are discussed.

We address the issue of performing hypothesis testing in accelerated failure time models for non-censored and censored samples. The performances of the likelihood ratio test and a recently proposed test, the gradient test, are compared through simulation. The gradient test features the same asymptotic properties as the classical large sample tests, namely, the likelihood ratio, Wald and score tests. Additionally, it is as simple to compute as the likelihood ratio test. Unlike the score and Wald tests, the gradient test does require the computation of the information matrix, neither observed nor expected. Our study suggests that the gradient test is more reliable than the other classical tests when the sample is of small or moderate size.