Understanding the dynamics of potentially heterogeneous variables is important in statistical applications. This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis. The methods are developed by Okui and Yanagi (2019) <doi:10.1016/j.jeconom.2019.04.036> and Okui and Yanagi (2020) <doi:10.1093/ectj/utz019>.

An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.

Prepares data for statistical analysis (e.g., analysis of variance ;ANOVA) by enabling the user to easily and quickly merge (using the file_merge() function) raw data files into one merged table and then aggregate the merged table (using the prep() function) into a finalized table while keeping track and summarizing every step of the preparation. The finalized table contains several possibilities for dependent measures of the dependent variable. Most suitable when measuring variables in an interval or ratio scale (e.g., reaction-times) and/or discrete values such as accuracy. Main functions included are file_merge() and prep(). The file_merge() function vertically merges individual data files (in a long format) in which each line is a single observation to one single dataset. The prep() function aggregates the single dataset according to any combination of grouping variables (i.e., between-subjects and within-subjects independent variables, respectively), and returns a data frame with a number of dependent measures for further analysis for each cell according to the combination of provided grouping variables. Dependent measures for each cell include among others means before and after rejecting all values according to a flexible standard deviation criteria, number of rejected values according to the flexible standard deviation criteria, proportions of rejected values according to the flexible standard deviation criteria, number of values before rejection, means after rejecting values according to procedures described in Van Selst & Jolicoeur (1994; suitable when measuring reaction-times), standard deviations, medians, means according to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data frame prep() returns can also be exported as a txt file to be used for statistical analysis in other statistical programs.

Construct parser combinator functions, higher order functions that parse input. Construction of such parsers is transparent and easy. Their main application is the parsing of structured text files like those generated by laboratory instruments. Based on a paper by Hutton (1992) <doi:10.1017/S0956796800000411>.

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.

Store and retrieve data from options() using syntax derived from the here package. potions makes it straightforward to update and retrieve options, either in the workspace or during package development, without overwriting global options.

This program contains a function to find the peaks and troughs of a data set. It filters the set of peaks to remove noise based on the expected height and expected slope of a peak. Peaks that are too short (caused by random noise), or too shallow (part of the background data) are filtered out.

This package contains functions to calculate power and sample size for various study designs used in bioequivalence studies. Use known.designs() to see the designs supported. Power and sample size can be obtained based on different methods, amongst them prominently the TOST procedure (two one-sided t-tests). See README and NEWS for further information.

The Penn World Table 8.x provides information on relative levels of income, output, inputs, and productivity for 167 countries between 1950 and 2011.

To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.

Implementation of class "polyMatrix" for storing a matrix of polynomials and implements basic matrix operations; including a determinant and characteristic polynomial. It is based on the package polynom and uses a lot of its methods to implement matrix operations. This package includes 3 methods of triangularization of polynomial matrices: Extended Euclidean algorithm which is most classical but numerically unstable; Sylvester algorithm based on LQ decomposition; Interpolation algorithm is based on LQ decomposition and Newton interpolation. Both methods are described in D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization, IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) <doi:10.1109/9.751344> and in Salah Labhalla, Henri Lombardi & Roger Marlin, Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux, Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) <doi:10.1016/0304-3975(95)00090-9>.

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

This package provides a set of raw datasets used to create SDTM domains in pharmaversesdtm package.

An innovative tool-set that incorporates graph community detection methods into systematic conservation planning. It is designed to enhance spatial prioritization by focusing on the protection of areas with high ecological connectivity. Unlike traditional approaches that prioritize individual planning units, priorCON focuses on clusters of features that exhibit strong ecological linkages. The priorCON package is built upon the prioritizr package <doi:10.32614/CRAN.package.prioritizr>, using commercial and open-source exact algorithm solvers that ensure optimal solutions to prioritization problems.

Plot malaria parasite genetic data on two or more episodes. Compute per-person posterior probabilities that each Plasmodium vivax (Pv) recurrence is a recrudescence, relapse, or reinfection (3Rs) using per-person P. vivax genetic data on two or more episodes and a statistical model described in Taylor, Foo and White (2022) <doi:10.1101/2022.11.23.22282669>. Plot per-recurrence posterior probabilities.

This package implements L1 and L2 penalized conditional logistic regression with penalty factors allowing for integration of multiple data sources. Implements stability selection for variable selection.

This package provides functions to perform the peer performance analysis of funds returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.

This package implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.

Determine minimal protein set explaining peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants. Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions.

This package provides a set of palettes imported from Gimp distributed under GPL3 (<https://www.gimp.org/about/COPYING>), and Inkscape distributed under GPL2 (<https://inkscape.org/about/license/>).

Integrated species distribution modeling is a rising field in quantitative ecology thanks to significant rises in the quantity of data available, increases in computational speed and the proven benefits of using such models. Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. We therefore introduce the R package PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them. There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions. An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and Oâ Hara (2020) <doi:10.1016/j.tree.2019.08.006>.

This package provides a simple way to add page numbers to base/ggplot/lattice graphics.

This package performs partial verification bias (PVB) correction for binary diagnostic tests, where PVB arises from selective patient verification in diagnostic accuracy studies. Supports correction of important accuracy measures -- sensitivity, specificity, positive predictive values and negative predictive value -- under missing-at-random and missing-not-at-random missing data mechanisms. Available methods and references are "Begg and Greenes methods" in Alonzo & Pepe (2005) <doi:10.1111/j.1467-9876.2005.00477.x> and deGroot et al. (2011) <doi:10.1016/j.annepidem.2010.10.004>; "Multiple imputation" in Harel & Zhou (2006) <doi:10.1002/sim.2494>, "EM-based logistic regression" in Kosinski & Barnhart (2003) <doi:10.1111/1541-0420.00019>; "Inverse probability weighting" in Alonzo & Pepe (2005) <doi:10.1111/j.1467-9876.2005.00477.x>; "Inverse probability bootstrap sampling" in Nahorniak et al. (2015) <doi:10.1371/journal.pone.0131765> and Arifin & Yusof (2022) <doi:10.3390/diagnostics12112839>; "Scaled inverse probability resampling methods" in Arifin & Yusof (2025) <doi:10.1371/journal.pone.0321440>.

Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take advantage of more accurate multiple sequence alignment by programs such as HHBlits Remmert et al. (2012) <DOI:10.1038/nmeth.1818> and HMMer Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by this package can be used for protein fold classification, protein structural class prediction, sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>, Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.