Other statistical methods
The Procedure Library covers many other areas of statistics, including analysis of repeated
measurements or of circular data, exact tests, meta analysis, sample re-use, survival analysis,
Bayesian methods, and the assessment of species diversity and abundance:
assesses order of ante-dependence for repeated measures
data
calculates overall tests based on a specified order of ante-dependence
produces an analysis of variance for repeated
measurements
fits frequency distributions to accumulated counts
plots profiles and differences of profiles for repeated
measurements
fits models to longitudinal data by generalized estimating
equations
calculates orthogonal polynomial time-contrasts for
repeated measurements
calculates summary statistics and tests of circular data
plots circular data
does circular regression of mean direction for an angular
response
plots rose diagrams of circular data like wind speeds
does Fisher's exact test for 2×2 tables
does random permutation and exact tests for regression or
generalized-linear-model analyses
does random permutation and exact tests for analysis of
variance
combines estimates from individual trials
produces bootstrapped estimates, standard errors and
distributions
produces Jackknife estimates and standard errors
calculates the Kaplan-Meier estimate of the survivor
function
calculates the life-table estimate of the survivor function
fits the proportional hazards model to survival data as a GLM
modifies a proportional hazards model fitted by RPHFIT
prints output for a proportional hazards model fitted by
RPHFIT
saves information from a proportional hazards model fitted by
RPHFIT
compares groups of right-censored survival data by nonparametric
tests
models survival times of exponential, Weibull or extreme-value
distributions
imports MCMC output in CODA format produced by WinBUGS
or OpenBUGS.
produces plots for output and diagnostics from MCMC
simulations.
runs WinBUGS from GenStat in batch mode using
scripts.
performs Bayesian computing using the Differential Evolution Markov
Chain algorithm
produces rank/abundance, ABC and k-dominance
plots
plots species accumulation curves for samples or
individuals
performs an analysis of similarities (ANOSIM)
calculates measures of diversity with jackknife or bootstrap
estimates
fits models to species abundance data
generates relative abundance of species for niche-based
models
calculates individual or sample-based rarefaction
plots the Lorenz curve and calculates the Gini and asymmetry
coefficients