function wish list following the 1.0 release

[Alcampopiano]

one group

• onesampb 1-alpha percentile boot CI for any estimator
• trimpb percentile boot CI for trimmed mean
• trimcibt bootstrap-t CI for trimmed mean
• mestci CI for M-measure of location based on huber's psi using percentile boot method (might be redundant with onesampb)
• momci CI for modified one-step M-estimator (might be redundant with onesampb)

two groups

• yuen yuen-welch method to compare trimmed means (no bootstrap)
• yuenbt bootstrapped-t CI for ut1 - ut2
• yhbt seems to be similar to yuenbt but modified for when trimming is <20 (maybe not needed)
• pb2gen percentile bootstrap CI for difference between any estimators
• m2ci convenience function func for comparing M-estimators based on huber's psi
• comvar2 bootstrapped comparison of variances
• permg permutation bootstrap test, any measure of location of scale
• t1way non-bootstrap method (but robust) for J indep groups (could be used for J>2 too)
• t1wayv2 same as t1way but explanatory es is returned for all pairs of groups

two dependent groups

• ydbt bootstrap-t CI for ut1 - ut2
• loc2dif difference between estimators using all combinations of difference scores
• l2drmci significance test for loc2dif using percentile bootstrap
• bootdpci percentile bootstrap method any estimator; can set options for using difference scores or measures of location based on the marginal distributions
• pcorb comparing variance of dep groups by extending some correlation-related method (i.e., pcorb(col1 - col1, col1 - col2) )
• pcorhc4 similar to pcorb; need more information on usage
• dfried some distance based test for J dependant groups (also used for more than 2 dep groups)

one-way for independent groups

• t1way non-bootstrap method (but robust) for J indep groups (could be in two indep group section too)
• t1wayv2 same as t1way but explanatory es is returned as well
• box1way another J=> 2 method based on trimmed means
• t1waybt test hyp of equal trimmed means using bootstrap t method (related to btrim which returns explanatory effect size and allows one to structure data a bit differently; btrim may not be needed)
• b1way percentile boot method for comparing J groups; seeing how deeply nest 0 is (1st method)
• other methods, especially ones using percentile bootstrap, under "methods based on MCP and linear contrasts" may be applicable here too

one-way methods based on multiple comparisons and linear contrasts

• lincon test linear contrasts with t means
• linconb test linear contrasts using bootstrap-t method
• tmcppb rom/hoch/ben-type methods using percentile bootstrap and trimmed means
• pbdepth percentile boot method for comparing J groups; seeing how deeply nest 0 is (2nd method)

two-way designs based on trimmed means

• t2way (no bootstrapping)

three-way designs based on trimmed means

• t3way (no bootstrapping)

two- and three-way multiple comparisons using contrasts (I believe for independent groups)

• mcp2atm all pairwise comparisons for each factor and interactions
• mcp3atm all pairwise comparisons for each factor and interactions
• bbtrim use bootstrap-t method for comparisons using contrasts
• bbbtrim use bootstrap-t method for comparisons using contrasts
• bbmcppb two-way percentile boot and trimmed mean tests
• bbbmcppb three-way percentile boot and trimmed mean tests

one-way dependant groups

• dfried some distance based test for J dependant groups
• rmanova trimmed means, no bootstrapping, for J groups
• rmmcp mcp for dep groups with trimmed means and Rom's method for FWE (might be able to extend to higher-level designs; 2 & 3-way)
• rmanovab bootstrap-t method for comparing measure associated with marginal distributions
• pairepb bootstrap-t method for all multi-comparisons
• bptd CI for all linear contrasts (very similar to pairdbp; but you can specify certain contrasts)
• bd1way percentile boot for J dep groups
• ddep another percentile boot method for J dep groups
• rmdzero percentile boot method for J group based on diff scores
• rmmcppb multiple comparisons for J dep groups using percentile boot method
• lindepbt boot-t method for mcp among J dep groups

within-within (two-way) dependent groups

• wwtrim non-bootstrap for trimmed means
• wwtrimbt same as wwtrim but bootstrap-t used
• wwmcp multi comps for main effects and interactions with linear contrasts (no boot)
• wwmcppb like wwmcp but percentile boot is used
• wwmcpbt like wwmcpppb but uses bootstrap-t method instead

mixed designs

• bwtrim no bootstrapping
• tsplitbt bootstrap-t for mixed design
• bwtrimbt same as tsplitbt but reports p values
• sppba test for factorA using percentile boot
• sppbb test for factorB using percentile boot
• sppbi test for interaction using percentile boot
• bwmcp all main effects and interactions for bw design bootstrap-t tests
• bwamcp same for factorA
• bwbmcp same for factorB
• bwimcp for interaction (non-bootstrap)
• spmcpa FA; same but with percentile boostrap
• spmcpb FB; same but with percentile boostrap
• spmcpi interaction; same but with percentile boostrap
• bwmcppb only for trimmed means? ; all main effects and interactions with percentile bootstrap method

three-way designs with one or more dependent groups

• bbwtrim no boot ominbus for main effect and interactions
• bwwtrim same as above two are within
• wwwtrim same as above all within
• bbwtrimbt no boot ominbus for main effect and interactions (bootstrap-t)
• bwwtrimbt same as above two are within (bootstrap-t)
• wwwtrimbt same as above all within (bootstrap-t)

three-way methods using multiple comparisons

• rm3mcp no bootstrap all contrasts
• bbwmcp bootstrap-t all comparisons with trimmed means
• bwwmcp bootstrap-t for the corresponding design
• bbwmcppb using percentile boot
• bwwmcppb using percentile boot
• wwwmcppb using percentile boot

effect sizes

• akp.effect delta (using trimmed mean and winsorized variance)
• yuenv2 compare two trimmed means and return explanatory effect size (xi2)
• ees.ci CI for two groups using percentile bootstrap method computes |xi|
• esmcp explanatory effect size returned for all pairs of J groups (can be used for dep groups)
• ESmainMCP a two-way method for getting explanatory effect size for FA and then FB
• esImcp two-way explanatory effect for all interactions

correlations and test of independence

• pbcor percentage bend correlation
• pball for a set of variables
• wincor winsorized correlation
• winall for a set of variables
• corb test for zero correlation using bootstrapping
• twopcor get CI of rho1 - rho2 (CI for difference of correlations) using percentile boot
• twocor test that two cors are equal (returns a p value and CI)

robust regression

• lsfitci CIs for reg parameters using percentile bootstrap method
• hc4wtest tests hypo that all slope parameters are zero using wild bootstrap method

utilities

• con1way create linear contrasts
• con2way
• con3way