head	1.6;
access;
symbols
	pkgsrc-2026Q1:1.6.0.12
	pkgsrc-2026Q1-base:1.6
	pkgsrc-2025Q4:1.6.0.10
	pkgsrc-2025Q4-base:1.6
	pkgsrc-2025Q3:1.6.0.8
	pkgsrc-2025Q3-base:1.6
	pkgsrc-2025Q2:1.6.0.6
	pkgsrc-2025Q2-base:1.6
	pkgsrc-2025Q1:1.6.0.4
	pkgsrc-2025Q1-base:1.6
	pkgsrc-2024Q4:1.6.0.2
	pkgsrc-2024Q4-base:1.6
	pkgsrc-2024Q3:1.5.0.12
	pkgsrc-2024Q3-base:1.5
	pkgsrc-2024Q2:1.5.0.10
	pkgsrc-2024Q2-base:1.5
	pkgsrc-2024Q1:1.5.0.8
	pkgsrc-2024Q1-base:1.5
	pkgsrc-2023Q4:1.5.0.6
	pkgsrc-2023Q4-base:1.5
	pkgsrc-2023Q3:1.5.0.4
	pkgsrc-2023Q3-base:1.5
	pkgsrc-2023Q2:1.5.0.2
	pkgsrc-2023Q2-base:1.5
	pkgsrc-2023Q1:1.4.0.12
	pkgsrc-2023Q1-base:1.4
	pkgsrc-2022Q4:1.4.0.10
	pkgsrc-2022Q4-base:1.4
	pkgsrc-2022Q3:1.4.0.8
	pkgsrc-2022Q3-base:1.4
	pkgsrc-2022Q2:1.4.0.6
	pkgsrc-2022Q2-base:1.4
	pkgsrc-2022Q1:1.4.0.4
	pkgsrc-2022Q1-base:1.4
	pkgsrc-2021Q4:1.4.0.2
	pkgsrc-2021Q4-base:1.4
	pkgsrc-2021Q3:1.2.0.2
	pkgsrc-2021Q3-base:1.2
	pkgsrc-2021Q2:1.1.0.16
	pkgsrc-2021Q2-base:1.1
	pkgsrc-2021Q1:1.1.0.14
	pkgsrc-2021Q1-base:1.1
	pkgsrc-2020Q4:1.1.0.12
	pkgsrc-2020Q4-base:1.1
	pkgsrc-2020Q3:1.1.0.10
	pkgsrc-2020Q3-base:1.1
	pkgsrc-2020Q2:1.1.0.8
	pkgsrc-2020Q2-base:1.1
	pkgsrc-2020Q1:1.1.0.4
	pkgsrc-2020Q1-base:1.1
	pkgsrc-2019Q4:1.1.0.6
	pkgsrc-2019Q4-base:1.1
	pkgsrc-2019Q3:1.1.0.2
	pkgsrc-2019Q3-base:1.1;
locks; strict;
comment	@# @;


1.6
date	2024.11.24.05.23.25;	author mef;	state Exp;
branches;
next	1.5;
commitid	eA9ENZrFp3LjhRyF;

1.5
date	2023.06.02.13.42.34;	author mef;	state Exp;
branches;
next	1.4;
commitid	LQdYWQyfCA9FqnrE;

1.4
date	2021.10.26.10.55.33;	author nia;	state Exp;
branches;
next	1.3;
commitid	vzl6zVlmjiF3hjeD;

1.3
date	2021.10.07.14.27.55;	author nia;	state Exp;
branches;
next	1.2;
commitid	wLkpKfebF6VS3TbD;

1.2
date	2021.09.20.00.49.49;	author mef;	state Exp;
branches;
next	1.1;
commitid	ItU15Ni03Jm85D9D;

1.1
date	2019.07.31.13.30.20;	author brook;	state Exp;
branches;
next	;
commitid	rYYUYABWiqhvqcxB;


desc
@@


1.6
log
@(math/R-survey) Updated 4.2.0 to 4.4.2

4.4-2	Invalid read in C++ code, found by Brian Ripley, fixed by Ben Schneider

	Updated small-area vignette (Peter Gao)

4.4-1   CRAN

4.4	Fixes to calibration for PPS sampling

	A PPS variance matrix can now be specified as phase two of a two-phase design.
	This includes poisson_sampling() as a model for non-response (for Pam Shaw, Jasper Yang)

	svysmoothUnit() and svysmoothArea() as an interface to the SUMMER package for small-area
	estimation (Peter Gao, Jon Wakefield, Richard Li)

4.3	Added Ben Schneider's C++ code for multistage variances. It is currently controlled
	by options(survey.use_rcpp), which defaults to TRUE

	error in scaling Pearson residuals for svyrepglm led to confint.svrepglm with
	likelihood profiling not finding the ends of the interval (Stephanie Zimmer)

	svyolr with rank deficiency and subsetting of cases in raked designs was overwriting
	the 'keep' variable (Justin Wishart)

	print.svyciprop() was printing fewer digits for the upper CI limit than the
	lower limit.

       	C++ code issues fixed

	degrees of freedom for score F-test in loglinear models fixed (Thomas Loughin)

	correct the scale in the F-distributed score test (Keiran Shao)

	allow user to specify degf= in svrepdesign to avoid needing to compute it
	(for Ben Schneider)

	warn if svychisq() is used with a single variable (for Isabelle Michaud)

	fix svyglm(rescale=FALSE) for replicate weights
@
text
@$NetBSD: distinfo,v 1.5 2023/06/02 13:42:34 mef Exp $

BLAKE2s (R/survey_4.4-2.tar.gz) = 62fae78306d03f9a1c0d4d05a556f63f6760bd6369631d612984dbb43db7e83c
SHA512 (R/survey_4.4-2.tar.gz) = e9648cb482e2b5a8edefd609c5faa0305a43a5a9d91dcd0dd27e8275252486126320cc60ed2f899c016fa2aced8682bb75649a3386a936f0981256a5d8f41746
Size (R/survey_4.4-2.tar.gz) = 2341313 bytes
@


1.5
log
@(math/R-survey) Updated 4.1.1 to 4.2.1

Following lines from inst/NEWS

4.2	Handling of influence functions has CHANGED. A function that supplies influence
	functions must supply one for every observation it was given in its input:
	use 0 for observations removed by subsetting. (for Guilherme Jacob)

	regTermTest(method="LRT") required the two models to use the same observations
	(of course), but didn't check, so IT WAS WRONG.
	It now subsets properly. (for Keiran Shao)

	This will probably be the *last* interpreted-R-only version of survey. Future
	versions will likely incorporate C++ code for faster variance computation and
	for small-area estimation.

	deffs in svyglm.svyrep.design (Ben Schneider)

	svynls() allows for prior (eg precision) weights (for Gary Nelson)

	improved names in svyquantile.svyrep.design (Ben Schneider)

	trimWeights() could get into an infinite recursion (Ingmar Sturm)

	data(myco) from Rao, Scott, and Skinner (originally from Clayton & Hills)

	as.svrepdesign now throws an error with post-stratified/raked/calibrated
	designs -- create replicates *first*, then calibrate (for Lauren Kennedy)

	trimWeights now works with replicate-weights designs

	score tests for svyglm (with Keiran Shao)

	svyby(), and thus svyboxplot(), didn't handle the new quantile functions
	correctly when standard error/ci weren't requested (Stephanie Zimmer, Raymond Pan)

	svycontrast() threw an error on the output of svyby() with return.replicates=TRUE
	(Alena Stern)

	Fix regTermTest for svycoxph for the methods lookup changes in R 4.0
	(and consequential change to marginpred)

	We don't provide model.matrix.svycoxph any more;use the inherited
	survival:::model.matrix.coxph

	Recent survival::coxph() switches to robust variances with non-integer weights
	so check for model$naive.var before model$var

	Disable the pass-through from predict.svycoxph to predict.coxph for type="expected"
	(Bryan Shepherd)

	svyby( svyvar) would throw an error on domains with only one observation
	(for Dirk Schumacher)

	svyvar() computes sample size 'n' in the same way for na.rm=TRUE as for a domain
	(for Raymond Pan)

	xdesign() for crossed designs (which aren't strictly surveys, but are basically similar)

	removed redundant loop in saddlepoint approximation to pchisqsum (Qiaolan Deng)

	fix in summary(svyivreg) for variances (Chandler McClellan)

	svyolr() didn't run for subsets of raked/calibrated designs (Antony Damico)

	add predict.svyolr (for Vincent Arel-Bundock)

	add anova.svycoxph (for Bryan Shepherd)

	add stringsToFactors=TRUE to svyby to ensure factors have same levels in domains
	(for various people including Stephanie Zimmer)

	more complicated svyby example in ?svycontrast

	svyranktest() gave an error for multiple groups with replicate weights (Kasuki Yoshida)

	Additional chisquared test for tables with zeros (CrossValidated question #571328)

	svydesign() has a 'calibrate.formula' option to tell R that your weights have
	already been calibrated/raked/post-stratified (for Tobias Schoch)

	svycontrast() didn't work on svrepglm objects without replicates (Thomas Loughlin)

	error in svycoxph with rescale=FALSE (Jing Zhang)

	error in vcov.svyrep.design with mse=TRUE (shows up in svyVGAM)

	svyquantile() didn't pay attention to interval_type for replicate weights (David Jorquera Petersen)

	svyquantile() qrules 5 to 9 now return the single data value when there's
	just one (David Jorquera Petersen)

	regTermTest() on svyolr() objects created in a function now finds the
	design object more reliably. (for Pedro Baldoni)

	regTermTest() will now test against null models in svycoxph()

	Fixes to svyquantile: https://github.com/bschneidr/r-forge-survey-mirror/pull/7
	(Ben Schneider)

	anova.svyloglin was broken by change to anova.glm(test=NULL)
	(Brian Ripley)

	One-sample svyttest() on logical variable now tests for P(TRUE==0) not P(FALSE==0)
	(Stephanie Zimmer)

	rename svykm.fit to svykm_fit because CRAN tests
@
text
@d1 1
a1 1
$NetBSD: distinfo,v 1.4 2021/10/26 10:55:33 nia Exp $
d3 3
a5 3
BLAKE2s (R/survey_4.2-1.tar.gz) = ee84dd9b95c6aa482d49ae97498811de6de272aa0fc9cd8b88dae73ba0086a7b
SHA512 (R/survey_4.2-1.tar.gz) = bb60ebf12aa027fac64596c6770c3cf98aa9a04ca3a87fc9d54e5b1aa583f27f447708039298bd139b324938ed18c560ad6f8cbd8315c0eeb44a0ef3ed79679e
Size (R/survey_4.2-1.tar.gz) = 2153349 bytes
@


1.4
log
@math: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
SHA512 hashes
@
text
@d1 1
a1 1
$NetBSD: distinfo,v 1.3 2021/10/07 14:27:55 nia Exp $
d3 3
a5 3
BLAKE2s (R/survey_4.1-1.tar.gz) = 67c945c16f21b7af4bbce91f009ac99f41d8683f431b5f7fe92a248c9492f874
SHA512 (R/survey_4.1-1.tar.gz) = 54264ef11b075f3a22e3ff1818879f57ab3be4df80d17d3a5810c552a2fbcb7a7de98f13429c4dc7200e9f58dfb3071f197276c21e462594e46c121933c5776b
Size (R/survey_4.1-1.tar.gz) = 1736232 bytes
@


1.3
log
@math: Remove SHA1 hashes for distfiles
@
text
@d1 1
a1 1
$NetBSD: distinfo,v 1.2 2021/09/20 00:49:49 mef Exp $
d3 1
a3 1
RMD160 (R/survey_4.1-1.tar.gz) = 57e81198c8637e65046004052c28ef4d84c3ae1e
@


1.2
log
@(math/R-survey) Updated 3.36 to 4.1.1

4.1-1	CRAN

4.1	svyquantile() has been COMPLETELY REWRITTEN. The old version is available
	as oldsvyquantile() (for David Eduardo Jorquera Petersen)

	svycontrast()'s improvements for statistics with replicates are now also there with
	svyby(), for domain comparisons (Robert Baskin)

	svyttest() now gives an error message if the binary group variable isn't binary
	(for StackOverflow 60930323)

	confint.svyglm Wald-type intervals now correctly label the columns (eg 2.5%, 97.5%)
	(for Molly Petersen)

	svyolr() using linearisation had the wrong standard errors for intercepts
	other than the first, if extracted using vcov (it was correct in summary() output)

	svyglm() gave deffs that were too large by a factor of nrow(design). (Adrianne Bradford)

	svycoxph() now warns if you try to use frailty or other penalised terms, because they
	just come from calling coxph and I have no reason to believe they work correctly
	in complex samples (for Claudia Rivera)

	coef.svyglm() now has a complete= argument to match coef.default(). (for Thomas Leeper)

	summary.svyglm() now gives NA p-values and a warning, rather than Inf standard errors,
	when the residual df are zero or negative (for Dan Simpson and Lauren Kennedy)

	In the multigroup case, svyranktest() now documents which elements of the 'htest'
	object have which parts of the result, because it's a bit weird (for Justin Allen)

	svycontrast() gets a new argument add=TRUE to keep the old coefficients as well

	twophase() can now take strata= arguments that are character, not just factor
	or numeric. (for Pam Shaw)

	add reference to Chen & Lumley on tail probabilities for quadratic forms.

	add reference to Breslow et al for calibrate()

	add svyqqplot and svyqqmath for quantile-quantile plots

	SE.svyby would grab confidence interval limits instead of SEs if vartype=c("ci","se").

	svylogrank(method="small") was wrong (though method="score" and method="large" are ok),
	because of problems in obtaining the at-risk matrix from coxph.detail. (for Zhiwen Yao)

	added as.svrepdesign.svyimputationList and withReplicates.svyimputationList
	(for Ángel Rodríguez Laso)

	logLik.svyglm used to return the deviance and now divides it by -2

	svybys() to make multiple tables by separate variables rather than a joint table
	(for Hannah Evans)

	added predictat= option to svypredmeans for Steven Johnston.

	Fixed bug in postStratify.svyrep.design, was reweighting all reps the same (Steven Johnston)

	Fix date for Thomas & Rao (1987) (Neil Diamond)

	Add svygofchisq() for one-sample chisquared goodness of fit (for Natalie Gallagher)

	confint.svyglm(method="Wald") now uses t distribution with design df by default.
	(for Ehsan Karim)

	confint.svyglm() checks for zero/negative degrees of freedom


	confint.svyglm() checks for zero/negative degrees of freedom

	mrb bootstrap now doesn't throw an error when there's a single PSU in a stratum
	(Steve White)

	oldsvyquantile() bug with producing replicate-weight confidence intervals for
	multiple quantiles (Ben Schneider)

	regTermTest(,method="LRT") didn't work if the survey design object and model were
	defined in a function (for Keiran Shao)

	svyglm() has clearer error message when the subset= argument contains NAs (for Pam Shaw)
	and when the weights contain NAs (for Paige Johnson)

	regTermTest was dropping the first term for coxph() models (Adam Elder)

	svydesign() is much faster for very large datasets with character ids or strata.

	svyglm() now works with na.action=na.exclude (for Terry Therneau)

	extractAIC.svylm does the design-based AIC for the two-parameter Gaussian model, so
	estimating the variance parameter as well as the regression parameters.
	(for Benmei Liu and Barry Graubard)

	svydesign(, pps=poisson_sampling()) for Poisson sampling, and ppscov() for
	specifying PPS design with weighted or unweighted covariance of sampling indicators
	(for Claudia Rivera Rodriguez)


4.0	Some (and eventually nearly all) functions now return influence functions when
	called with a survey.design2 object and the influence=TRUE option.  These allow
	svyby() to estimate covariances between domains, which could previously only be
	done for replicate-weight designs, and so allow svycontrast() to do domain contrasts
	 - svymean, svytotal, svyratio, svymle, svyglm, svykappa

	Nonlinear least squares with svynls() now available

	Document that predict.svyglm() doesn't use a rescaled residual mean square
	to estimate standard errors, and so disagrees with some textbooks. (for Trent Buskirk)

3.38	When given a statistic including replicates, svycontrast() now transforms the replicates
	and calculates the variance, rather than calculating the variance then using the
	delta method.  Allows geometric means to exactly match SAS/SUDAAN (for Robert Baskin)

	vcov.svyrep.design to simplify computing variances from replicates (for William Pelham)

	svykm() no longer throws an error with single-observation domains (for Guy Cafri)

	Documentation for svyglm() specifies that it has always returned
	model-robust standard errors. (for various people wanting to fit relative risk
	regression models).

3.37	RODBC database connections are no longer supported.
	Use the DBI-compatible 'odbc' package

	set scale<-1 if it is still NULL after processing, inside svrepdesign()
        [https://stats.stackexchange.com/questions/409463]

       	Added withPV for replicate-weight designs [for Tomasz Żółtak]

      	svyquantile for replicate-weight designs now uses a supplied alpha to get
       	confidence intervals and estimates SE by dividing confidence interval length
       	by twice abs(qnorm(alpha/2)). [For Klaus Ignacio Lehmann Melendez]

       	All the svyquantile methods now take account of design degrees of freedom and
       	use t distributions for confidence intervals. Specify df=Inf to get a Normal.
       	[For Klaus Ignacio Lehmann Melendez]

       	svyivreg() for 2-stage least-squares (requires the AER package)

       	warn when rho= is used with type="BRR" in svrepdesign [for Tomasz Żółtak]

	Add "ACS" and "successive-difference" to type= in svrepdesign(),
	for the American Community Survey weights

	Add "JK2" to type= in svrepdesign

	Warn when scale, rscales are supplied unnecessarily to svyrepdesign

        More explanation of 'symbolically nested' in anova.svyglm

        Link to blog post about design df with replicate weights.

        Chase 'Encyclopedia of Design Theory' link again.
@
text
@d1 1
a1 1
$NetBSD: distinfo,v 1.1 2019/07/31 13:30:20 brook Exp $
a2 1
SHA1 (R/survey_4.1-1.tar.gz) = 99ff92ff4e0711bf9fea79d356dd63f75df474aa
@


1.1
log
@R-survey: initial commit.

Summary statistics, two-sample tests, rank tests, generalised linear
models, cumulative link models, Cox models, loglinear models, and
general maximum pseudolikelihood estimation for multistage stratified,
cluster-sampled, unequally weighted survey samples. Variances by
Taylor series linearisation or replicate weights. Post-stratification,
calibration, and raking. Two-phase subsampling designs. Graphics. PPS
sampling without replacement. Principal components, factor analysis.
@
text
@d1 1
a1 1
$NetBSD$
d3 4
a6 4
SHA1 (R/survey_3.36.tar.gz) = 9505042993c610a311b6b26f9b56bbd004e358c3
RMD160 (R/survey_3.36.tar.gz) = f5268b49a748f2a379130d938468d68174d52b20
SHA512 (R/survey_3.36.tar.gz) = c383068281004b855638eb0835969b35d7fad2bd4bace0f75f4bedf1925121231e5373662f2d26cdbd4c4543564109b3de37e7a6edde9a560bdc241e5314c008
Size (R/survey_3.36.tar.gz) = 1550706 bytes
@