Bivariate Segmentation/Clustering Methods and Tools (segclust2d)
Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages ‘moveHMM’ and ‘marcher’. The segmentation method is a bivariate extension of Lavielle’s method available in ‘adehabitatLT’ (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method (formerly available in ‘cghseg’ package) to the bivariate case. The full description of the method is not published yet.

Inverse Probability Weighting Estimation of Average Treatment Effect with Outcome Misclassification (ipwErrorY)
An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighting (IPW) estimation of average treatment effect (ATE) with misclassified outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method.

Transformation Models (tram)
Formula-based user-interfaces to specific transformation models implemented in package ‘mlt’. Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.

cpt-city’ Colour Gradients (cptcity)
Incorporates colour gradients from the ‘cpt-city’ web archive available at <http://…/>.

Distance-Based k-Medoids (kmed)
A simple and fast distance-based k-medoids clustering algorithm from Park and Jun (2009) <doi:10.1016/j.eswa.2008.01.039>. Calculate distances for mixed variable data such as Gower (1971) <doi:10.2307/2528823>, Wishart (2003) <doi:10.1007/978-3-642-55721-7_23>, Podani (1999) <doi:10.2307/1224438>, Huang (1997) <http://….1.94.9984&rep=rep1&type=pdf>, and Harikumar and PV (2015) <doi:10.1016/j.procs.2015.10.077>. Cluster validation applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages.