Infer the genetic composition of individuals in terms of haplotype dosages for a haploblock, based on bi-allelic marker dosages, for any ploidy level. Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data. Submitted to BMC Bioinformatics (2021).

Download and generate summaries for the rodent, plant, ant, and weather data from the Portal Project. Portal is a long-term (and ongoing) experimental monitoring site in the Chihuahuan desert. The raw data files can be found at <https://github.com/weecology/portaldata>.

This package provides several measures ((dis)similarity, distance/metric, correlation, entropy) for comparing two partitions of the same set of objects. The different measures can be assigned to three different classes: Pair comparison (containing the famous Jaccard and Rand indices), set based, and information theory based. Many of the implemented measures can be found in Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006) <doi:10.1007/s00357-006-0017-z> and Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>. Partitions are represented by vectors of class labels which allow a straightforward integration with existing clustering algorithms (e.g. kmeans()). The package is mostly based on the S4 object system.

This package provides a collection of R Markdown templates for creating simple and easy to personalize single page websites.

This package provides functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons.

Like similar profiling tools, the proffer package automatically detects sources of slowness in R code. The distinguishing feature of proffer is its utilization of pprof', which supplies interactive visualizations that are efficient and easy to interpret. Behind the scenes, the profile package converts native Rprof() data to a protocol buffer that pprof understands. For the documentation of proffer', visit <https://r-prof.github.io/proffer/>. To learn about the implementations and methodologies of pprof', profile', and protocol buffers, visit <https://github.com/google/pprof>. <https://protobuf.dev>, and <https://github.com/r-prof/profile>, respectively.

It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.

This package provides path_chain class and functions, which facilitates loading and saving directory structure in YAML configuration files via config package. The file structure you created during exploration can be transformed into legible section in the config file, and then easily loaded for further usage.

R interface to PRIMME <https://www.cs.wm.edu/~andreas/software/>, a C library for computing a few eigenvalues and their corresponding eigenvectors of a real symmetric or complex Hermitian matrix, or generalized Hermitian eigenproblem. It can also compute singular values and vectors of a square or rectangular matrix. PRIMME finds largest, smallest, or interior singular/eigenvalues and can use preconditioning to accelerate convergence. General description of the methods are provided in the papers Stathopoulos (2010, <doi:10.1145/1731022.1731031>) and Wu (2017, <doi:10.1137/16M1082214>). See citation("PRIMME") for details.

This package provides tools for exchanging pedigree data between the pedsuite packages and the Familias software for forensic kinship computations (Egeland et al. (2000) <doi:10.1016/s0379-0738(00)00147-x>). These functions were split out from the forrel package to streamline maintenance and provide a lightweight alternative for packages otherwise independent of forrel'.

Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>.

This package provides tools to process legacy format summary redistricting data files produced by the United States Census Bureau pursuant to P.L. 94-171. These files are generally available earlier but are difficult to work with as-is.

Includes functions implementing the conditionally optimal matching algorithm, which can be used to generate matched samples in designs with multiple groups. The algorithm is described in Nattino, Song and Lu (2022) <doi:10.1016/j.csda.2021.107364>.

This package implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.

Pupillometric data collected using SR Research Eyelink eye trackers requires significant preprocessing. This package contains functions for preparing pupil dilation data for visualization and statistical analysis. Specifically, it provides a pipeline of functions which aid in data validation, the removal of blinks/artifacts, downsampling, and baselining, among others. Additionally, plotting functions for creating grand average and conditional average plots are provided. See the vignette for samples of the functionality. The package is designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer.

This package provides functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R. The package also provides helper functions to manipulate data and execute common procedures such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database from the downloaded images. All functionalities are parallelized for fast and efficient analyses.

It enables sparklyr to integrate with Spark Connect', and Databricks Connect by providing a wrapper over the PySpark python library.

Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019) <https://hal.archives-ouvertes.fr/hal-02002275>. It can be used with univariate and multivariate data.

Given a SpatialPolygonsDataFrame and a set of populations for each polygon, compute a population density estimate based on Tobler's pycnophylactic interpolation algorithm. The result is a SpatialGridDataFrame. Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.

Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.

Replace the standard print method for functions with one that performs syntax highlighting, using ANSI colors, if the terminal supports them.

Computes optimal changepoint models using the Poisson likelihood for non-negative count data, subject to the PeakSeg constraint: the first change must be up, second change down, third change up, etc. For more info about the models and algorithms, read "Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection" <https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.

An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015). Renton, M, Diggle, A, Manalil, S and Powles, S (2011) <doi:10.1016/j.jtbi.2011.05.010> Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.).

Kappa, ICC, reliability coefficient, parallel analysis, multi-traits multi-methods, spherical representation of a correlation matrix.