We are experienced users of the following packages:

General applications:
Genstat, Minitab, R, SAS,
SPSS, Stata.

Power studies:
G*Power, nQuery, PASS.

Bayesian approach:
WinBUGS, OpenBUGS, JAGS.

Spreadsheets:
Excel, Calc, Sheets.

Linear Mixed Models for interval responses with clusters, i.e. random effects. Normal and lognormal distributions.

GLMM for counts and categorical responses with clusters. Poisson, binomial and multinomial distributions.

Time-to-event responses: Cox model, stratification, frailty. Counting process for time-varying covariates; piecewise Cox model. Weibull Proportional Hazards and Accelerated Failure Time model.

Data Reduction techniques: Principal Components, Multi-Dimensional Scaling, Discriminant Analysis, Clustering, PLS, Correspondence Analysis.

Designs for assay optimisation: Response Surface Methods. Central Composite, Box-Behnken, Fractional Factorials.

Sample Size for superiority, non-inferiority and equivalence studies in cross-over and longitudinal designs.

Models for Repeated Measures. Summary statistics approach, applications of Mixed Models: marginal (covariance pattern) and subject-specific (random coefficients) models.

Bayesian approaches: model fitting, MCMC diagnostics, posterior distributions, Credible and HPD Intervals. Model selection, model checking, predictive hierarchical models.

Causal approach for observational studies: Average Treatment Effects. Propensity Score Matching and doubly robust matching methods for continuous, binary, counts and time-to-event outcomes.