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.