* using log directory 'd:/Rcompile/CRANpkg/local/4.4/pampe.Rcheck'
* 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'
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'pampe' can be installed ... OK
See 'https://www.r-project.org/nosvn/R.check/r-release-windows-x86_64/pampe-00install.html' for details.
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... [0s] OK
* checking whether the package can be loaded with stated dependencies ... [0s] OK
* checking whether the package can be unloaded cleanly ... [0s] OK
* checking whether the namespace can be loaded with stated dependencies ... [0s] OK
* checking whether the namespace can be unloaded cleanly ... [0s] OK
* checking loading without being on the library search path ... [0s] OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [3s] OK
* 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.
       |                                                                                          ^
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.
       |                                                                                                                                                                                             ^
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... [0s] OK
* checking data for ASCII and uncompressed saves ... OK
* checking examples ... [2s] OK
* checking PDF version of manual ... [20s] OK
* checking HTML version of manual ... [1s] OK
* DONE
Status: 1 NOTE