[ ] onesampb 1-alpha percentile boot CI for any estimator
[ ] trimpb percentile boot CI for trimmed mean
[x] 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
[x] yuen yuen-welch method to compare trimmed means (no bootstrap)
[x] yuenbt bootstrapped-t CI for ut1 - ut2
[ ] yhbt seems to be similar to yuenbt but modified for when trimming is <20 (maybe not needed)
[x] 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
[x] ydbt bootstrap-t CI for ut1 - ut2
[x] loc2dif difference between estimators using all combinations of difference scores
[x] l2drmci significance test for loc2dif using percentile bootstrap
[x] 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
[x] lincon test linear contrasts with t means
[x] linconb test linear contrasts using bootstrap-t method
[x] 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
[x] 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
[x] rmmcppb multiple comparisons for J dep groups using percentile boot method
[x] 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)
[x] wwmcppb like wwmcp but percentile boot is used
[x] 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
[x] bwmcp all main effects and interactions for bw design bootstrap-t tests
[x] bwamcp same for factorA
[x] bwbmcp same for factorB
[x] bwimcp for interaction (non-bootstrap)
[x] spmcpa FA; same but with percentile boostrap
[x] spmcpb FB; same but with percentile boostrap
[x] spmcpi interaction; same but with percentile boostrap
[x] 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
[x] pbcor percentage bend correlation
[x] pball for a set of variables
[x] wincor winsorized correlation
[x] winall for a set of variables
[x] 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
one group
onesampb1-alpha percentile boot CI for any estimatortrimpbpercentile boot CI for trimmed meantrimcibtbootstrap-t CI for trimmed meanmestciCI for M-measure of location based on huber's psi using percentile boot method (might be redundant withonesampb)momciCI for modified one-step M-estimator (might be redundant withonesampb)two groups
yuenyuen-welch method to compare trimmed means (no bootstrap)yuenbtbootstrapped-t CI for ut1 - ut2yhbtseems to be similar to yuenbt but modified for when trimming is <20 (maybe not needed)pb2genpercentile bootstrap CI for difference between any estimatorsm2ciconvenience function func for comparing M-estimators based on huber's psicomvar2bootstrapped comparison of variancespermgpermutation bootstrap test, any measure of location of scalet1waynon-bootstrap method (but robust) for J indep groups (could be used for J>2 too)t1wayv2same as t1way but explanatory es is returned for all pairs of groupstwo dependent groups
ydbtbootstrap-t CI for ut1 - ut2loc2difdifference between estimators using all combinations of difference scoresl2drmcisignificance test forloc2difusing percentile bootstrapbootdpcipercentile bootstrap method any estimator; can set options for using difference scores or measures of location based on the marginal distributionspcorbcomparing variance of dep groups by extending some correlation-related method (i.e., pcorb(col1 - col1, col1 - col2) )pcorhc4similar topcorb; need more information on usagedfriedsome distance based test for J dependant groups (also used for more than 2 dep groups)one-way for independent groups
t1waynon-bootstrap method (but robust) for J indep groups (could be in two indep group section too)t1wayv2same ast1waybut explanatory es is returned as wellbox1wayanother J=> 2 method based on trimmed meanst1waybttest hyp of equal trimmed means using bootstrap t method (related tobtrimwhich returns explanatory effect size and allows one to structure data a bit differently;btrimmay not be needed)b1waypercentile boot method for comparing J groups; seeing how deeply nest 0 is (1st method)one-way methods based on multiple comparisons and linear contrasts
lincontest linear contrasts with t meanslinconbtest linear contrasts using bootstrap-t methodtmcppbrom/hoch/ben-type methods using percentile bootstrap and trimmed meanspbdepthpercentile 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)
mcp2atmall pairwise comparisons for each factor and interactionsmcp3atmall pairwise comparisons for each factor and interactionsbbtrimuse bootstrap-t method for comparisons using contrastsbbbtrimuse bootstrap-t method for comparisons using contrastsbbmcppbtwo-way percentile boot and trimmed mean testsbbbmcppbthree-way percentile boot and trimmed mean testsone-way dependant groups
dfriedsome distance based test for J dependant groupsrmanovatrimmed means, no bootstrapping, for J groupsrmmcpmcp for dep groups with trimmed means and Rom's method for FWE (might be able to extend to higher-level designs; 2 & 3-way)rmanovabbootstrap-t method for comparing measure associated with marginal distributionspairepbbootstrap-t method for all multi-comparisonsbptdCI for all linear contrasts (very similar to pairdbp; but you can specify certain contrasts)bd1waypercentile boot for J dep groupsddepanother percentile boot method for J dep groupsrmdzeropercentile boot method for J group based on diff scoresrmmcppbmultiple comparisons for J dep groups using percentile boot methodlindepbtboot-t method for mcp among J dep groupswithin-within (two-way) dependent groups
wwtrimnon-bootstrap for trimmed meanswwtrimbtsame as wwtrim but bootstrap-t usedwwmcpmulti comps for main effects and interactions with linear contrasts (no boot)wwmcppblike wwmcp but percentile boot is usedwwmcpbtlike wwmcpppb but uses bootstrap-t method insteadmixed designs
bwtrimno bootstrappingtsplitbtbootstrap-t for mixed designbwtrimbtsame as tsplitbt but reports p valuessppbatest for factorA using percentile bootsppbbtest for factorB using percentile bootsppbitest for interaction using percentile bootbwmcpall main effects and interactions for bw design bootstrap-t testsbwamcpsame for factorAbwbmcpsame for factorBbwimcpfor interaction (non-bootstrap)spmcpaFA; same but with percentile boostrapspmcpbFB; same but with percentile boostrapspmcpiinteraction; same but with percentile boostrapbwmcppbonly for trimmed means? ; all main effects and interactions with percentile bootstrap methodthree-way designs with one or more dependent groups
bbwtrimno boot ominbus for main effect and interactionsbwwtrimsame as above two are withinwwwtrimsame as above all withinbbwtrimbtno boot ominbus for main effect and interactions (bootstrap-t)bwwtrimbtsame as above two are within (bootstrap-t)wwwtrimbtsame as above all within (bootstrap-t)three-way methods using multiple comparisons
rm3mcpno bootstrap all contrastsbbwmcpbootstrap-t all comparisons with trimmed meansbwwmcpbootstrap-t for the corresponding designbbwmcppbusing percentile bootbwwmcppbusing percentile bootwwwmcppbusing percentile booteffect sizes
akp.effectdelta (using trimmed mean and winsorized variance)yuenv2compare two trimmed means and return explanatory effect size (xi2)ees.ciCI for two groups using percentile bootstrap method computes |xi|esmcpexplanatory effect size returned for all pairs of J groups (can be used for dep groups)ESmainMCPa two-way method for getting explanatory effect size for FA and then FBesImcptwo-way explanatory effect for all interactionscorrelations and test of independence
pbcorpercentage bend correlationpballfor a set of variableswincorwinsorized correlationwinallfor a set of variablescorbtest for zero correlation using bootstrappingtwopcorget CI of rho1 - rho2 (CI for difference of correlations) using percentile boottwocortest that two cors are equal (returns a p value and CI)robust regression
lsfitciCIs for reg parameters using percentile bootstrap methodhc4wtesttests hypo that all slope parameters are zero using wild bootstrap methodutilities
con1waycreate linear contrastscon2waycon3way