Last updated on 2024-02-28 00:56:23 CET.

Flavor | Version | T_{install} | T_{check} | T_{total} | Status | Flags |
---|---|---|---|---|---|---|

r-devel-linux-x86_64-debian-clang | 1.9.1 | 13.18 | 126.22 | 139.40 | NOTE | |

r-devel-linux-x86_64-debian-gcc | 1.9.1 | 10.83 | 94.38 | 105.21 | NOTE | |

r-devel-linux-x86_64-fedora-clang | 1.9.1 | 171.89 | NOTE | |||

r-devel-linux-x86_64-fedora-gcc | 1.9.1 | 166.86 | NOTE | |||

r-devel-windows-x86_64 | 1.9.1 | 12.00 | 114.00 | 126.00 | OK | |

r-patched-linux-x86_64 | 1.9.1 | 11.77 | 122.86 | 134.63 | OK | |

r-release-linux-x86_64 | 1.9.1 | 12.95 | 122.61 | 135.56 | OK | |

r-release-macos-arm64 | 1.9.1 | 51.00 | OK | |||

r-release-macos-x86_64 | 1.9.1 | 126.00 | OK | |||

r-release-windows-x86_64 | 1.9.1 | 16.00 | 143.00 | 159.00 | OK | |

r-oldrel-macos-arm64 | 1.9.1 | 52.00 | OK | |||

r-oldrel-windows-x86_64 | 1.9.1 | 16.00 | 150.00 | 166.00 | 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