An implementation of several machine learning algorithms for
multivariate time series. The package includes functions allowing the
execution of clustering, classification or outlier detection methods,
among others. It also incorporates a collection of multivariate time
series datasets which can be used to analyse the performance of new
proposed algorithms. Some of these datasets are stored in GitHub data
packages 'ueadata1' to 'ueadata8'. To access these data packages, run
'install.packages(c('ueadata1', 'ueadata2', 'ueadata3', 'ueadata4', 'ueadata5', 'ueadata6', 'ueadata7', 'ueadata8'), repos='<https://anloor7.github.io/drat/>')'.
The installation takes a couple of minutes but we strongly encourage the
users to do it if they want to have available all datasets of mlmts.
Practitioners from a broad variety of fields could
benefit from the general framework provided by 'mlmts'.
quantspec, waveslim, Rfast, TSclust, forecast, tseries, TSA,
tsfeatures, tseriesChaos, freqdom, e1071, dtw, base, psych,
complexplus, MTS, Matrix, ggplot2, multiwave, MASS, fda.usc,
TSdist, evolqg, geigen, DescTools, pracma, pspline, Rdpack,
stats, ClusterR, AID, caret, ranger, igraph, randomForest
ueadata1, ueadata2, ueadata3, ueadata4, ueadata5, ueadata6,
ueadata7, ueadata8, testthat (>= 3.0.0)

Estimation of the multinomial logit model with choice specific variables

Estimation of the multinomial logit model with alternative and/or individual specific variables

Estimation of the multinomial logit model with choice
specific variables

Estimation of the multinomial logit model

R (>= 1.8.0), Formula, statmod, lmtest, maxLik, zoo

R (>= 2.10), Formula, statmod, lmtest, maxLik, zoo

R (>= 2.10), Formula, statmod, lmtest, maxLik, zoo, MASS

knitr, car, nnet, lattice, AER, ggplot2, texreg
Maximum Likelihood estimation of random utility discrete choice models (logit and probit).

knitr, car, nnet, lattice, AER, ggplot2, texreg, rmarkdown

Maximum Likelihood estimation of random utility discrete
choice models, as described in Kenneth Train (2012) Discrete Choice
Methods with Simulations <doi:10.1017/CBO9780511805271>.