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Mango Solutions Training Datasets
Datasets to be used primarily in conjunction with Mango Solutions training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9). Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with training materials, whilst retaining compatibility with the book.
Inference and Estimation of Hidden Markov Models and Hidden Semi-Markov Models
Provides flexible maximum likelihood estimation and inference for Hidden
Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs), as well as the
underlying systems in which they operate. The package supports a wide range
of observation and dwell-time distributions, offering a flexible modelling
framework suitable for diverse practical data. Efficient implementations of
the forward-backward and Viterbi algorithms are provided via 'Rcpp' for enhanced
computational performance. Additional functionality includes model simulation,
residual analysis, non-initialised estimation, local and global decoding,
calculation of diverse information criteria, computation of confidence intervals
using parametric bootstrap methods, numerical covariance matrix estimation, and
comprehensive visualisation functions for interpreting the data-generating
processes inferred from the models. Methods follow standard approaches described
by Guédon (2003)
Segmentation and Classification of Accelerometer Data
Segmentation and classification procedures for data from the 'Activinsights GENEActiv' < https://activinsights.com/technology/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.
Full Factorial Breeding Analysis
We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples.
Estimate the Cause of Recurrent Vivax Malaria using Genetic Data
Plot malaria parasite genetic data on two or more episodes.
Compute per-person posterior probabilities that each
Plasmodium vivax (Pv) recurrence is a recrudescence, relapse,
or reinfection (3Rs) using per-person P. vivax genetic data on two or
more episodes and a statistical model described in
Taylor, Foo and White (2022)
Karl Broman's R Code
Miscellaneous R functions, including functions related to graphics (mostly for base graphics), permutation tests, running mean/median, and general utilities.