Log message:
(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

Log message:
math: Replace RMD160 checksums with BLAKE2s checksums

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Log message:
(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.

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Update all R packages to canonical form.

The canonical form [1] of an R package Makefile includes the
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This updates all current R packages to this form, which will make
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[1] http://mail-index.netbsd.org/tech-pkg/2019/08/02/msg021711.html