Description: Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.

Title: Analysis of MEDITS-Like Survey Data
Author: Walter Zupa, Loredana Casciaro, Isabella Bitetto, Maria Teresa Spedicato
Description: Set of functions working with survey data in the format of the MEDITS project <https://www.sibm.it/SITO%20MEDITS/principaleprogramme.htm>. In this version, functions use TA, TB and TC tables respectively containing haul, catch and aggregated biological data.

Title: Mixtures of Exponential-Distance Models with Covariates
person("Thomas Brendan", "Murphy", email = "brendan.murphy@ucd.ie", role = "ctb"),
person("Raffaella", "Piccarreta", email = "raffaella.piccarreta@unibocconi.it ", role = "ctb"),
Description: Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2019) <arXiv:1908.07963>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics. Sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
Imports: cluster, matrixStats, nnet, seriation, stringdist, TraMineR,

person("Thomas Brendan", "Murphy", email = "brendan.murphy@ucd.ie", role = "ctb", comment = c(ORCID = "0000-0002-5668-7046")),
Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>),

Imports: cluster, matrixStats (>= 0.53.1), nnet, seriation, stringdist,
Suggests: klaR (>= 0.6-13), knitr, rmarkdown, viridisLite (>= 0.2.0)

Description: Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics. Sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
Imports: cluster, matrixStats (>= 0.53.1), nnet (>= 7.3-0), seriation,
stringdist, TraMineR (>= 1.6), WeightedCluster
Suggests: klaR (>= 0.6-13), knitr, rmarkdown, viridisLite (>= 0.4.0)