means of indel (insert or delete) and substitution operations with possibly costs of these elementary operations depending on the states involved. SA is so intimately linked with OM that it is sometimes named optimal matching analysis (OMA). There are roughly three categories of dissimilarity measures: Optimal matching and other edit distances Examples: OM, OMloc (localized OM), OMslen (spell-length sensitive OM), OMspell (OM of spell sequences), OMstran (OM of sequences of transitions), TWED (time-warp edit distance), HAM (Hamming and generalized Hamming), DHD (Dynamic Hamming). Strategies for setting the substitution and indel costs Constant costs (all substitution costs identical and single indel cost) Theory-based costs Feature-based costs Data-driven costs: based on transition probabilities or state frequencies Measures based on the count of common attributes Examples: LCS (derived from length of longest common subsequence), LCP (from length of longest common prefix), NMS (number of matching subsequences), and NMSMST and SVRspell two variants of NMS. Distances between within-sequence state distributions Examples: CHI2 and EUCLID defined as the average of respectively the Chi-squared and Euclidean distance between state distributions in successive sliding windows. Dissimilarity-based analysis Pairwise dissimilarities between sequences give access to a series of techniques to discover holistic structuring characteristics of the sequence data. In particular, dissimilarities between sequences can serve as input to cluster algorithms and multidimensional scaling, but also allow to identify medoids or other representative sequences, define neighborhoods, measure the discrepancy of a set of sequences, proceed to ANOVA-like analyses, and grow regression trees. Cluster analysis Descriptive: identification of main sequence patterns. Clusters as dependent or independent variables in regression analysis: study of relationships with other variables of interest. Multidimensional scaling (principal coordinates): numerical representation of sequences. Discrepancy (ANOVA-like) analysis Sequence of ANOVA-like analyses Regression trees Representative sequences Multiple domains (multichannel analysis) Dyadic and polyadic sequence data Other methods of analysis Although dissimilarity-based methods play a central role in social SA, essentially because of their ability to preserve the holistic perspective, several other approaches also prove useful for analyzing sequence data. Non dissimilarity-based clustering Latent class analysis (LCA), Markov model mixture and hidden Markov model mixture Mixtures of exponential-distance models Sequence networks Representing a single sequence as a network Meta network of sequences Sequence network measures Life history graph Probabilistic approaches Markovian and other transition distribution models. See also Markov model. Probabilistic Suffix Tree (PST) also known as variable-order Markov model or variable-length Markov model. Event sequences Event structure models Rendering