In the framework of IP214, NCCR LIVES members develop computerized tools as packages for the R environment for statistical computing and graphics.

  • TraMineRextras: TraMineR Extension (maintained by Gilbert Ritschard)
    Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration.

  • WeightedCluster: Clustering of Weighted Data (maintained by Matthias Studer)
    Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions, and plotting clusters of state sequences.

  • march: Markov Chains (maintained by André Berchtold)
    Computation of various Markovian models for categorical data including homogeneous Markov chains of any order, MTD models, Hidden Markov models (HMM), and Double Chain Markov Models (DCMM).

  • PST: Probabilistic Suffix Trees and Variable Length Markov Chains (maintained by Alexis Gabadinho)
    Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST.

  • vcrpart: Tree-Based Varying Coefficient Regression for Generalized Linear and Ordinal Mixed Models (maintained by Reto Bürgin)
    Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients, and partitioning of time-varying variables in longitudinal regression.

  • Spacom: Spatially Weighted Context Data for Multilevel Modelling (maintained by Till Junge)
    Spacom provides tools to construct and exploit spatially weighted context data. Spatial weights are derived by a Kernel function from a user-defined matrix of distances between contextual units. Spatial weights can then be applied either to precise contextual measures or to aggregate estimates based on micro-level survey data, to compute spatially weighted context data. Available aggregation functions include indicators of central tendency, dispersion, or inter-group variability, and take into account survey design weights. The package further allows combining the resulting spatially weighted context data with individual-level predictor and outcome variables, for the purposes of multilevel modelling.