Svensson’s Method (svenssonm)
Obtain parameters of Svensson’s Method, including percentage agreement, systematic change and individual change. Also, the contingency table can be generated. Svensson’s Method is a rank-invariant nonparametric method for the analysis of ordered scales which measures the level of change both from systematic and individual aspects. For the details, please refer to Svensson E. Analysis of systematic and random differences between paired ordinal categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.

Utility Functions for ‘INLA’ (INLAutils)
A number of utility functions for ‘INLA’ <http://www.r-inla.org>. Additional diagnostic plots and support for ‘ggplot2’. Step wise regression with ‘INLA’. Species distribution models and other helper functions.

Fitting Multistate Models (multistate)
Medical researchers are often interested in investigating the relationship between explicative variables and multiple times-to-event. Time-inhomogeneous Markov models consist of modelling the probabilities of transitions according to the chronological times (times since the baseline of the study). Semi-Markov (SM) models consist of modelling the probabilities of transitions according to the times spent in states. In this package, we propose functions implementing such 3-state and 4-state multivariable and multistate models. The user can introduce multiple covariates to estimate conditional (subject-specific) effects. We also propose to adjust for possible confounding factors by using the Inverse Probability Weighting (IPW). When a state is patient death, the user can consider to take into account the mortality of the general population (relative survival approach). Finally, in the particular situation of one initial transient state and two competing and absorbing states, this package allows for estimating mixture models.

Greedy Expected Posterior Loss (GreedyEPL)
Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2016) ‘Optimal Bayesian estimators for latent variable cluster models’ <arXiv:1607.02325>.

Sensitivity Analysis for Stratified Observational Studies (senstrat)
Sensitivity analysis in unmatched observational studies, with or without strata. The main functions are sen2sample() and senstrat(). See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545-555 <doi:10.1111/1467-9868.00249> .

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