HMMs with Ordered Hidden States and Emission Densities

Inference using a class of Hidden Markov models (HMMs) called 'oHMMed'(ordered HMM with emission densities ): The 'oHMMed' algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. These are modelled as discrete hidden states; the observed data points are then realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are then inferred. Relevant for application to genomic sequences, time series, or any other sequence data with serial autocorrelation.

Create Scatter Plots with Marginal Density or Box Plots

Allows you to make clean, good-looking scatter plots with the option to easily add marginal density or box plots on the axes. It is also available as a module for 'jamovi' (see < https://www.jamovi.org> for more information). 'Scatr' is based on the 'cowplot' package by Claus O. Wilke and the 'ggplot2' package by Hadley Wickham.

A Sky Illuminance Location Tracker

Calculate geolocations by light using template matching. The routine uses a calibration free optimization of a sky illuminance model to determine locations robustly using a template matching approach, as described by Ekstrom (2004) < https://nipr.repo.nii.ac.jp/records/2496>, and behaviourly informed constraints (step-selection).

Decision Tree Analysis for Probabilistic Subgroup Identification with Multiple Treatments

In the situation when multiple alternative treatments or interventions available, different population groups may respond differently to different treatments. This package implements a method that discovers the population subgroups in which a certain treatment has a better effect than the other alternative treatments. This is done by first estimating the treatment effect for a given treatment and its uncertainty by computing random forests, and the resulting model is summarized by a decision tree in which the probabilities that the given treatment is best for a given subgroup is shown in the corresponding terminal node of the tree.

Analysis of Single-Cell Viral Growth Curves

Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) ), and the package name stands for "SIngle CEll Growth Analysis in R".