Last updated on 2024-12-25 00:49:30 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.9.1 | 10.90 | 96.27 | 107.17 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.9.1 | 7.56 | 70.15 | 77.71 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.9.1 | 171.56 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 1.9.1 | 168.69 | NOTE | |||
| r-devel-windows-x86_64 | 1.9.1 | 12.00 | 111.00 | 123.00 | NOTE | |
| r-patched-linux-x86_64 | 1.9.1 | 11.70 | 91.83 | 103.53 | NOTE | |
| r-release-linux-x86_64 | 1.9.1 | 9.56 | 91.88 | 101.44 | NOTE | |
| r-release-macos-arm64 | 1.9.1 | 49.00 | NOTE | |||
| r-release-macos-x86_64 | 1.9.1 | 80.00 | NOTE | |||
| r-release-windows-x86_64 | 1.9.1 | 12.00 | 111.00 | 123.00 | NOTE | |
| r-oldrel-macos-arm64 | 1.9.1 | 51.00 | OK | |||
| r-oldrel-macos-x86_64 | 1.9.1 | 126.00 | OK | |||
| r-oldrel-windows-x86_64 | 1.9.1 | 16.00 | 143.00 | 159.00 | OK |
Version: 1.9.1
Check: Rd files
Result: NOTE
checkRd: (-1) ivmodel.Rd:37: Lost braces
37 | and produces statistics for \eqn{\beta}. In particular, \code{ivmodel} computes the OLS, TSLS, k-class, limited information maximum likelihood (LIML), and Fuller-k (Fuller 1977) estimates of \eqn{\beta} using \code{KClass}, \code{LIML}, and code{Fuller}. Also, \code{ivmodel} computes confidence intervals and hypothesis tests of the type \eqn{H_0: \beta = \beta_0} versus \eqn{H_0: \beta \neq \beta_0} for the said estimators as well as two weak-IV confidence intervals, Anderson and Rubin (Anderson and Rubin 1949) confidence interval (Anderson and Rubin 1949) and the conditional likelihood ratio confidence interval (Moreira 2003). Finally, the code also conducts a sensitivity analysis if \eqn{Z} is one-dimensional (i.e. there is only one instrument) using the method in Jiang et al. (2015).
| ^
checkRd: (-1) ivmodelFormula.Rd:42: Lost braces
42 | and produces statistics for \eqn{\beta}. In particular, \code{ivmodel} computes the OLS, TSLS, k-class, limited information maximum likelihood (LIML), and Fuller-k (Fuller 1977) estimates of \eqn{\beta} using \code{KClass}, \code{LIML}, and code{Fuller}. Also, \code{ivmodel} computes confidence intervals and hypothesis tests of the type \eqn{H_0: \beta = \beta_0} versus \eqn{H_0: \beta \neq \beta_0} for the said estimators as well as two weak-IV confidence intervals, Anderson and Rubin (Anderson and Rubin 1949) confidence interval (Anderson and Rubin 1949) and the conditional likelihood ratio confidence interval (Moreira 2003). Finally, the code also conducts a sensitivity analysis if \eqn{Z} is one-dimensional (i.e. there is only one instrument) using the method in Jiang et al. (2015).
| ^
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64