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Time Series Analysis Tools
A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013)
A DNA Reference Library Manager
Reference database manager offering a set of functions to import, organize, clean, filter, audit and export reference genetic data. Provide functions to download sequence data from NCBI GenBank < https://www.ncbi.nlm.nih.gov/genbank/>. Designed as an environment for semi-automatic and assisted construction of reference databases and to improve standardization and repeatability in barcoding and metabarcoding studies.
Mini Charts for Interactive Maps
Add and modify small charts on an interactive map created with package 'leaflet'. These charts can be used to represent at same time multiple variables on a single map.
Inference Using Simulation
Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated, as first described in Rousset et al. (2017)
Easy Web Scraping
The goal of 'ralger' is to facilitate web scraping in R.
Cramer-von Mises Goodness-of-Fit Tests
It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
Airborne LiDAR Filtering Method Based on Cloth Simulation
Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground points filtering algorithm which is based on cloth simulation. It tries to simulate the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface < https://www.mdpi.com/2072-4292/8/6/501/htm>.
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Exploratory Data Analysis for the 'spatstat' Family
Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Computes the Variance-Covariance Matrix of Multidimensional Parameters Using M-Estimation
Provides a flexible framework for estimating the variance-covariance matrix of estimated parameters. Estimation relies on unbiased estimating functions to compute the empirical sandwich variance. (i.e., M-estimation in the vein of Tsiatis et al. (2019)