checkRd: (-1) robfmtest.Rd:24: Lost braces; missing escapes or markup?
    24 | an optional data frame containing the variables in the model. If not found in data, the variables are taken from \code{environment(formula)}, typically the environment from which {robfmtest} is called.
       |                                                                                                                                                                                    ^
checkRd: (-1) robfmtest.Rd:37: Lost braces; missing escapes or markup?
    37 | Since the classical tests including {resettest}, {raintest} and {harvtest} implemented in {lmtest} are not resistant to outliers and can become misleading even in the presence of one outlier, we provide a test which is resistant to outliers. The price to pay for robustness is a small loss of power, when the model holds exactly.
       |                                     ^
checkRd: (-1) robfmtest.Rd:37: Lost braces; missing escapes or markup?
    37 | Since the classical tests including {resettest}, {raintest} and {harvtest} implemented in {lmtest} are not resistant to outliers and can become misleading even in the presence of one outlier, we provide a test which is resistant to outliers. The price to pay for robustness is a small loss of power, when the model holds exactly.
       |                                                  ^
checkRd: (-1) robfmtest.Rd:37: Lost braces; missing escapes or markup?
    37 | Since the classical tests including {resettest}, {raintest} and {harvtest} implemented in {lmtest} are not resistant to outliers and can become misleading even in the presence of one outlier, we provide a test which is resistant to outliers. The price to pay for robustness is a small loss of power, when the model holds exactly.
       |                                                                 ^
checkRd: (-1) robfmtest.Rd:37: Lost braces; missing escapes or markup?
    37 | Since the classical tests including {resettest}, {raintest} and {harvtest} implemented in {lmtest} are not resistant to outliers and can become misleading even in the presence of one outlier, we provide a test which is resistant to outliers. The price to pay for robustness is a small loss of power, when the model holds exactly.
       |                                                                                           ^