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. Negative binomial, Poisson, binomial and multinomial distributions. From subject-specific effects to population-averaged effects.

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.

Evaluation of policy impact: 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.