Homogeneity tests of the coefficients in panel data. Currently, only the Hsiao test for determining coefficient homogeneity between the panel data individuals is implemented, as described in Hsiao (2022), "Analysis of Panel Data" (<doi:10.1017/9781009057745>).

This package provides functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as vcf files or rsync files generated by the the PoPoolation software) and perform conversion to alternative format (as used in the BayPass and SelEstim software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in TreeMix', BayPass and SelEstim format, see the Package vignette for details).

This package produces power spectral density estimates through iterative refinement of the optimal number of sine-tapers at each frequency. This optimization procedure is based on the method of Riedel and Sidorenko (1995), which minimizes the Mean Square Error (sum of variance and bias) at each frequency, but modified for computational stability. The same procedure can now be used to calculate the cross spectrum (multivariate analyses).

Pharmacokinetics is the study of drug absorption, distribution, metabolism, and excretion. The pharmacokinetics model explains that how the drug concentration change as the drug moves through the different compartments of the body. For pharmacokinetic modeling and analysis, it is essential to understand the basic pharmacokinetic parameters. All parameters are considered, but only some of parameters are used in the model. Therefore, we need to convert the estimated parameters to the other parameters after fitting the specific pharmacokinetic model. This package is developed to help this converting work. For more detailed explanation of pharmacokinetic parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001"; "Benet and Zia-Amirhosseini" (1995) <DOI: 10.1177/019262339502300203>; "Mould and Upton" (2012) <DOI: 10.1038/psp.2012.4>; "Mould and Upton" (2013) <DOI: 10.1038/psp.2013.14>.

This package provides tools for loading and processing passive acoustic data. Read in data that has been processed in Pamguard (<https://www.pamguard.org/>), apply a suite processing functions, and export data for reports or external modeling tools. Parameter calculations implement methods by Oswald et al (2007) <doi:10.1121/1.2743157>, Griffiths et al (2020) <doi:10.1121/10.0001229> and Baumann-Pickering et al (2010) <doi:10.1121/1.3479549>.

Engineered features and "helper" functions ancillary to the public.ctn0094data package, extending this package for ease of use (see <https://CRAN.R-project.org/package=public.ctn0094data>). This public.ctn0094data package contains harmonized datasets from some of the National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN) projects. Specifically, the CTN-0094 project is to harmonize and de-identify clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for opioid use disorder. This current version is built from public.ctn0094data v. 1.0.6.

Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...

Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) <doi:10.1002/ecs2.4328> in Ecosphere. These can be used to estimate the parameters of a stochastic state space model (i.e. a model where a time series is observed with error). The goal of this package is to estimate the variability around a deterministic process, both in terms of observation error - i.e. variability due to imperfect observations that does not influence system state - and in terms of process noise - i.e. stochastic variation in the actual state of the process. Unlike classical methods for estimating variability, this package does not necessarily assume that the deterministic state is fixed (i.e. a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated (e.g. stochastic fluctuations during predator-prey dynamics).

Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis, <https://our.oakland.edu/items/c8942577-2562-4d2f-8677-cb8ec0bf6234>.

This package provides a method of clustering functional data using subregion information of the curves. It is intended to supplement the fda and fda.usc packages in functional data object clustering. It also facilitates the printing and plotting of the results in a tree format and limits the partitioning candidates into a specific set of subregions.

The pedsuite is a collection of packages for pedigree analysis, covering applications in forensic genetics, medical genetics and more. A detailed presentation of the pedsuite is given in the book Pedigree Analysis in R (Vigeland, 2021, ISBN: 9780128244302).

Implementations of several methods for principal component analysis using the L1 norm. The package depends on COIN-OR Clp version >= 1.17.4. The methods implemented are PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>, L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>, L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>, L1-PCAhp (Visentin, Prestwich and Armagan 2016) <DOI:10.1007/978-3-319-46227-1_37>, wPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>, awPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>, PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and SharpEl1-PCA (Brooks and Dula, submitted).

Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like paleotree and APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, paleobuddy can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in RPANDA': Morlon H. et al (2016) <doi:10.1111/2041-210X.12526>.

This package provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

This package provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Curated color palettes drawn from India's natural beauty - Himalayan snow, Thar dunes, Kerala backwaters, Andaman reefs, Spiti's cold desert, Kashmir's autumn chinar, and more. Provides discrete and continuous palettes with first-class ggplot2 integration through scale_color_prakriti() and scale_fill_prakriti(), plus base graphics helpers for displaying palettes.

It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) <doi:10.1093/biomet/ass032>. The functions in this package facilitate bootstrapping for propensity score analysis (PSA). By default, bootstrapping using two classification tree methods (using rpart and ctree functions), two matching methods (using Matching and MatchIt packages), and stratification with logistic regression. A framework is described for users to implement additional propensity score methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results.

This package provides tools to match plant species names against the official threatened species list of Peru (Supreme Decree 043-2006-AG, 2006). Implements a hierarchical matching pipeline with exact, fuzzy, and suffix matching algorithms to handle naming variations and taxonomic changes. Supports both the original 2006 nomenclature and updated taxonomic names, allowing users to check protection status regardless of nomenclatural changes since the decree's publication. Threat categories follow International Union for Conservation of Nature standards (Critically Endangered, Endangered, Vulnerable, Near Threatened).

Intended for larger-than-memory tabular data, prt objects provide an interface to read row and/or column subsets into memory as data.table objects. Data queries, constructed as R expressions, are evaluated using the non-standard evaluation framework provided by rlang and file-backing is powered by the fast and efficient fst package.

Computes Owen's T function or the bivariate normal integral using one of the following methods: the modified Euler's arctangent series, tetrachoric series, or Vasicek's series. For the methodology, see Komelj, J. (2023) <doi:10.4236/ajcm.2023.134026>, or the revised postprint version with corrected contents and improved typography at <doi:10.48550/arXiv.2312.00011>. Also see the alternative approach by Vasicek, O. A. (1998) <doi:10.21314/JCF.1998.015>.

This package provides a framework for defining pipelines of functions for applying data transformations, model estimation and inverse-transformations, resulting in predicted value generation (or model-scoring) functions that automatically apply the entire pipeline of functions required to go from input to predicted output.

Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.

Makes it easier to create a Quarto website for an R package. Converts R documentation ('Rd') files into Quarto Markdown documents individually. Supports configuration via the _quarto.yml file and groups functions using roxygen2 family tags.

Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO.