./math/R-lmtest, Testing linear regression models

[ CVSweb ] [ Homepage ] [ RSS ] [ Required by ] [ Add to tracker ]


Branch: CURRENT, Version: 0.9.40, Package name: R-lmtest-0.9.40, Maintainer: minskim

A collection of tests, data sets, and examples for diagnostic checking
in linear regression models. Furthermore, some generic tools for
inference in parametric models are provided.


Required to run:
[lang/g95] [math/R] [math/R-zoo]

Required to build:
[pkgtools/cwrappers]

Master sites: (Expand)


Version history: (Expand)


CVS history: (Expand)


   2022-05-29 11:48:06 by Wen Heping | Files touched by this commit (2) | Package updated
Log message:
Update to 0.9.40
Remove duplicate line

Upstream changes:
Changes in Version 0.9-40

  * bptest() now optionally also accepts a "weights" argument and processes
    weighted "lm" objects correctly (suggested by Robert Gulotty).

  * The degrees of freedom (df) in the default coeftest() method can now also
    be a vector of the same length as the coefficients.

Changes in Version 0.9-39

  o Small improvements in handling of "df" attribute in coeftest() methods.

  o In case update() is called within the default waldtest() method, it tries
    harder to evaluate this in the calling environment (reported by Vincent
    Arel-Bundock). This is still not guaranteed to find the right data but
    should be more robust than the previous implementation. To avoid any such
    issues it is recommended to call waldtest(object1, object2) and fitted
    model objects (and not just update formulas).

Changes in Version 0.9-38

  o coeftest.default() now includes attributes for "nobs" and \ 
"logLik" provided
    that nobs() and logLik() extractors are available. This is in order to
    facilitate model summaries a la broom::glance for "coeftest" objects
    (suggested by Grant McDermott). The attributes can be extracted again
    from the coeftest object with nobs() and logLik(), respectively.

  o Optionally, the entire original model object x is saved as an attribute
    "object" in coeftest(x, save = TRUE). Like nobs/logLik above, this \ 
should
    also facilitate model summaries.

  o waldtest.default() is stricter about detecting non-nestedness of models,
    e.g., when under the alternative variables are added but the intercept
    dropped.

  o waldtest.lm() now uses . ~ 0 as the default reference model if waldtest(object)
    tests an object without intercept (suggested by Kevin Tappe).
   2021-10-26 12:56:13 by Nia Alarie | Files touched by this commit (458)
Log message:
math: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
SHA512 hashes
   2021-10-07 16:28:36 by Nia Alarie | Files touched by this commit (458)
Log message:
math: Remove SHA1 hashes for distfiles
   2019-08-08 21:53:58 by Brook Milligan | Files touched by this commit (189) | Package updated
Log message:
Update all R packages to canonical form.

The canonical form [1] of an R package Makefile includes the
following:

- The first stanza includes R_PKGNAME, R_PKGVER, PKGREVISION (as
  needed), and CATEGORIES.

- HOMEPAGE is not present but defined in math/R/Makefile.extension to
  refer to the CRAN web page describing the package.  Other relevant
  web pages are often linked from there via the URL field.

This updates all current R packages to this form, which will make
regular updates _much_ easier, especially using pkgtools/R2pkg.

[1] http://mail-index.netbsd.org/tech-pkg/2019/08/02/msg021711.html
   2018-07-28 16:40:53 by Brook Milligan | Files touched by this commit (126)
Log message:
Remove MASTER_SITES= from individual R package Makefiles.

Each R package should include ../../math/R/Makefile.extension, which also
defines MASTER_SITES.  Consequently, it is redundant for the individual
packages to do the same.  Package-specific definitions also prevent
redefining MASTER_SITES in a single common place.
   2018-03-21 16:26:08 by Min Sik Kim | Files touched by this commit (3)
Log message:
math/R-lmtest: Import version 0.9.35

A collection of tests, data sets, and examples for diagnostic checking
in linear regression models. Furthermore, some generic tools for
inference in parametric models are provided.