Outlier Detection Using Invariant Coordinate Selection

Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.

Assessing Pst Statistics

Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation).

Experiment-Selector CV-TMLE for Integration of Observational and RCT Data

The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) .

Interpolation and Extrapolation for Species Diversity

Provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation curves for species diversity (Hill numbers) based on individual-based abundance data or sampling-unit- based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution) for an introduction of the R package.

Naive Bayes Transmission Analysis

Estimates the relative transmission probabilities between cases in an infectious disease outbreak or cluster using naive Bayes. Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (2020) .

Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) ; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) .

Species-Richness Prediction and Diversity Estimation with R

Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.

Sustainable Transport Planning

Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) , but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) . The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as < https://cyclestreets.net/> (Desjardins et al. 2021) ; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) and the 'OD jittering' method described in Lovelace et al. (2022) . Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) , and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) .

Small Helpers and Tricks for Epidemics Analysis

A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions.

A Statistical Methodology to Select Covariates in High-Dimensional Data under Dependence

Two steps variable selection procedure in a context of high-dimensional dependent data but few observations. First step is dedicated to eliminate dependence between variables (clustering of variables, followed by factor analysis inside each cluster). Second step is a variable selection using by aggregation of adapted methods. Bastien B., Chakir H., Gegout-Petit A., Muller-Gueudin A., Shi Y. A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles associated with outcome of a non-small-cell lung cancer treatment. 2018. < https://hal.archives-ouvertes.fr/hal-01939694>.