Post-selection inference in linear regression models, constructing simultaneous confidence intervals across a user-specified universe of models. Implements the methodology described in Kuchibhotla, Kolassa, and Kuffner (2022) "Post-Selection Inference" <doi:10.1146/annurev-statistics-100421-044639> to ensure valid inference after model selection, with applications in high-dimensional settings like Lasso selection.

The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, <doi:10.1007/s13171-021-00255-2>).

Priority-ElasticNet extends the Priority-LASSO method (Klau et al. (2018) <doi:10.1186/s12859-018-2344-6>) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets.

Estimate coefficient of variation percent (CV%) for any arbitrary distribution, including some built-in estimates for commonly-used transformations in pharmacometrics. Methods are described in various sources, but applied here as summarized in: Prybylski, (2024) <doi:10.1007/s40262-023-01343-2>.

Numerical derivatives through finite-difference approximations can be calculated using the pnd package with parallel capabilities and optimal step-size selection to improve accuracy. These functions facilitate efficient computation of derivatives, gradients, Jacobians, and Hessians, allowing for more evaluations to reduce the mathematical and machine errors. Designed for compatibility with the numDeriv package, which has not received updates in several years, it introduces advanced features such as computing derivatives of arbitrary order, improving the accuracy of Hessian approximations by avoiding repeated differencing, and parallelising slow functions on Windows, Mac, and Linux.

Parametric bootstrap (PB) has been used for three-way ANOVA model with unequal group variances.

The merits of TIMESAT and phenopix are adopted. Besides, a simple and growing season dividing method and a practical snow elimination method based on Whittaker were proposed. 7 curve fitting methods and 4 phenology extraction methods were provided. Parameters boundary are considered for every curve fitting methods according to their ecological meaning. And optimx is used to select best optimization method for different curve fitting methods. Reference: Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>; Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. <doi:10.1029/2020JG005636>; Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13â 24; Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982â 2013). Agric. For. Meteorol. 248, 408â 417. <doi:10.1016/j.agrformet.2017.10.026>.

This package implements kernel-based classification Support Vector Machines with reliable estimated probabilities of class membership. Theoretical support for the functions in this package can be found in Duarte Silva (2025) <doi:10.1016/j.cor.2025.107203>.

Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.

Estimates corrected Procrustean correlation between matrices for removing overfitting effect. Coissac Eric and Gonindard-Melodelima Christelle (2019) <doi:10.1101/842070>.

This package provides classes to pre-process microarray gene expression data as part of the OOMPA collection of packages described at <http://silicovore.com/OOMPA/index.html>.

This package provides functions to simplify the PatentsView API (<https://search.patentsview.org/docs/docs/Search%20API/SearchAPIReference/#api-query-language>) query language, send GET and POST requests to the API's twenty seven endpoints, and parse the data that comes back.

Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses.

Convenient structures for creating, sourcing, reading, writing and manipulating ordinal preference data. Methods for writing to/from PrefLib formats. See Nicholas Mattei and Toby Walsh "PrefLib: A Library of Preference Data" (2013) <doi:10.1007/978-3-642-41575-3_20>.

An integrative toolbox of word embedding research that provides: (1) a collection of pre-trained static word vectors in the .RData compressed format <https://psychbruce.github.io/WordVector_RData.pdf>; (2) a group of functions to process, analyze, and visualize word vectors; (3) a range of tests to examine conceptual associations, including the Word Embedding Association Test <doi:10.1126/science.aal4230> and the Relative Norm Distance <doi:10.1073/pnas.1720347115>, with permutation test of significance; and (4) a set of training methods to locally train (static) word vectors from text corpora, including Word2Vec <doi:10.48550/arXiv.1301.3781>, GloVe <doi:10.3115/v1/D14-1162>, and FastText <doi:10.48550/arXiv.1607.04606>.

Manipulation and analysis of phylogenetically simulated data sets and phylogenetically based analyses using GLS.

Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.

Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.

Package for corpus analysis using the Corpus Workbench ('CWB', <https://cwb.sourceforge.io>) as an efficient back end for indexing and querying large corpora. The package offers functionality to flexibly create subcorpora and to carry out basic statistical operations (count, co-occurrences etc.). The original full text of documents can be reconstructed and inspected at any time. Beyond that, the package is intended to serve as an interface to packages implementing advanced statistical procedures. Respective data structures (document-term matrices, term-co-occurrence matrices etc.) can be created based on the indexed corpora.

This package provides functions to make board game graphics with the ggplot2', grid', rayrender', rayvertex', and rgl packages. Specializes in game diagrams, animations, and "Print & Play" layouts for the piecepack <https://www.ludism.org/ppwiki> but can make graphics for other board game systems. Includes configurations for several public domain game systems such as checkers, (double-18) dominoes, go, piecepack', playing cards, etc.

This package provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. It is built upon ggplot2 and other plotting packages, and is designed to be easy to use and to work seamlessly with ggplot2 objects. It is particularly useful for creating complex plots with multiple layers, facets, and annotations. It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees. The package is designed to be flexible and customizable, and to work well with the ggplot2 ecosystem. The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.

Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.

Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Mrode, R. A. (2005) <ISBN:9780851989969, 0851989969>; Nilforooshan, M.A. (2022) <doi:10.37496/rbz5120210131>.

This package provides functions for working with primary event censored distributions and Stan implementations for use in Bayesian modeling. Primary event censored distributions are useful for modeling delayed reporting scenarios in epidemiology and other fields (Charniga et al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for arbitrary delay distributions, a range of common primary distributions, and allows for truncation and secondary event censoring to be accounted for (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of common distributions also have analytical solutions implemented, allowing for faster computation. In addition, it provides multiple methods for fitting primary event censored distributions to data via optional dependencies.