- using R version 4.4.2 (2024-10-31 ucrt)
- using platform: x86_64-w64-mingw32
- R was compiled by
gcc.exe (GCC) 13.3.0
GNU Fortran (GCC) 13.3.0
- running under: Windows Server 2022 x64 (build 20348)
- using session charset: UTF-8
- checking for file 'pampe/DESCRIPTION' ... OK
- checking extension type ... Package
- this is package 'pampe' version '1.1.2'
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See the install log for details.
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- checking Rd files ... [1s] NOTE
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.
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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.
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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.
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- DONE
Status: 1 NOTE