checkRd: (-1) pampe.Rd:54: Lost braces; missing escapes or markup?
    54 | The way they propose to estimate the outcome of the treated unit under no treatment, Y^0_{1t}, is to use the following modeling strategy: use R^2 (or likelihood values) in order to select the best OLS estimator for Y^0_{1t} using j out of the J units in the donor pool, denoted by M(j)* for j=1, ..., J; then choose M(m)* from M(1)*, ..., M(J)* in terms of a model selection criterion, like AICc, AIC or BIC. Note that the method calculates OLS models of up to J+1 parameters; so that if the length of the pre-treatment period t=1, 2, ..., T'-1 is not of a much higher order than that, the regressions M(J-1)*, M(J)* can not be calculated because there are not enough degrees of freedom.
       |                                                                                          ^
checkRd: (-1) pampe.Rd:54: Lost braces; missing escapes or markup?
    54 | The way they propose to estimate the outcome of the treated unit under no treatment, Y^0_{1t}, is to use the following modeling strategy: use R^2 (or likelihood values) in order to select the best OLS estimator for Y^0_{1t} using j out of the J units in the donor pool, denoted by M(j)* for j=1, ..., J; then choose M(m)* from M(1)*, ..., M(J)* in terms of a model selection criterion, like AICc, AIC or BIC. Note that the method calculates OLS models of up to J+1 parameters; so that if the length of the pre-treatment period t=1, 2, ..., T'-1 is not of a much higher order than that, the regressions M(J-1)*, M(J)* can not be calculated because there are not enough degrees of freedom.
       |                                                                                                                                                                                                                            ^
checkRd: (-1) pampe.Rd:56: Lost braces; missing escapes or markup?
    56 | To avoid this problem, the pampe package proposes the following slight modification to the previously outlined modeling strategy: use R^2 in order to select the best OLS estimator for Y^0_{1t} using j out of the J units in the donor pool, denoted by M(j)* for j=1, ..., T_0-4; then choose M(m)* from M(1)*, ..., M(T_0-4)* in terms of a model selection criterion (in our case AICc). Note that the key difference is that while we allowed models up to M(J)*, this is now modified to allow models up to M(T_0-4)*, with T_0-4<J, which allows for at least 3 degrees of freedom. This is implemented through the default value of nvmax, which is equal to J, or if not possible, to J-4. The user can of course override this default.
       |                                                                                                                                                                                             ^