Crop production systems are increasingly challenged by climate variability, resource limitations, and bioticâ abiotic stresses. In this context, stress tolerance indices and physiological trait estimators are essential tools to identify stable and superior genotypes, quantify yield stability under stress versus non-stress conditions, and understand plant adaptive responses. The PhysioIndexR package provides a unified framework to compute commonly used stress indices, physiological traits, and derived metrics that are critical in crop improvement, crop physiology, and other agricultural sciences. The package includes functions to calculate classical stress tolerance indices (See Lamba et al., 2023; <doi:10.1038/s41598-023-37634-8>) such as Tolerance (TOL), Stress Tolerance Index (STI), Stress Susceptibility Percentage Index (SSPI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Mean Relative Performance (MRP), and Percent Yield Reduction (PYR), along with a convenience wrapper all_indices() that returns all indices simultaneously. The function mfvst_from_indices() integrates these indices into a composite stress score using direction-aware membership values (0â 1 scaling) and also averaging, facilitating genotype ranking and selection (See Vinu et al., 2025; <doi:10.1007/s12355-025-01595-1>). The package also implements two novel composite functions: WMFVST(), which computes the Weighted Mean Membership Function Value for Stress Tolerance, and WASI(), which computes the Weighted Average Stress Index, both derived from membership function values (MFV) and raw stress index values, respectively. Beyond stress indices, the package provides functions for key physiological traits relevant to sugarcane and other crops: bmap() computes biomass accumulation and partitioning between leaf, cane/shoot, and root fractions. chl() estimates total chlorophyll content from Soil-Plant Analysis Development (SPAD) and Chlorophyll Content Index (CCI) values using validated quadratic models particularly for sugarcane (See Krishnapriya et al., 2020; <doi:10.37580/JSR.2019.2.9.150-163>). ctd() calculates canopy temperature depression (CTD) from ambient and canopy temperatures, an important indicator of transpiration efficiency. growth() computes key growth analysis parameters, including Leaf Area Index (LAI), Net Assimilation Rate (NAR), and Crop Growth Rate (CGR) across crop growth stages (See Watson, 1958; <doi:10.1093/oxfordjournals.aob.a083596>). ranking() provides flexible ranking utilities for genotype performance with multiple tie-handling and NA-placement options. Through these tools, the package enables researchers to: (i) quantify crop responses to stress environments, (ii) partition physiological components of yield, (iii) integrate multiple indices into composite metrics for genotype evaluation, and (iv) facilitate informed decision making in breeding pipelines, and plant physiology experiments. By combining physiology-based traits with quantitative stress indices, PhysioIndexR supports comprehensive crop evaluation and helps researchers identify multi-stress-resilient superior genotypes, thereby contributing to genetic improvement and ensuring sustainable production of food, fuel, and fibre in the era of limited resources and climate change.

Weighted Deming regression, also known as errors-in-variable regression, is applied with suitable weights. Weights are modeled via a precision profile; thus the methods implemented here are referred to as precision profile weighted Deming (PWD) regression. The package covers two settings â one where the precision profiles are known either from external studies or from adequate replication of the X and Y readings, and one in which there is a plausible functional form for the precision profiles but the exact (unknown) function must be estimated from the (generally singlicate) readings. The function set includes tools for: estimated standard errors (via jackknifing); standardized-residual analysis function with regression diagnostic tools for normality, linearity and constant variance; and an outlier analysis identifying significant outliers for closer investigation. The following reference provides further information on mathematical derivations and applications. Hawkins, D.M., and J.J. Kraker (2026). Precision Profile Weighted Deming Regression for Methods Comparison'. The Journal of Applied Laboratory Medicine 11, 379-392 <doi:10.1093/jalm/jfaf183>. Weighted Deming regression is also now implemented for multiple instruments , as set out in Hawkins, D.M., and J.J. Kraker (2026). Multiple Instrument Methods Comparison by Precision weighted Deming Regression', on Arxiv <doi:10.48550/arXiv.2607.11776>. The â multiâ functions refer to the multiple instrument analysis, and the "PWD" functions refer to the two-instrument.

The purpose of PH1XBAR is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) <doi: 10.1002/qre.2793>, Yao and Chakraborti (2021) <doi: 10.1080/08982112.2021.1878220>, and Yao et al. (2023) <doi: 10.1080/00224065.2022.2139783>.

This package provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages.

Fits Bayesian nonparametric models based on Pólya tree processes, including adaptive Pólya trees, Markov adaptive Pólya trees, optional Pólya trees, and their conditional-density counterparts. Methods are described in Ma (2017) <doi:10.1214/16-BA1021>, Ma (2017) <doi:10.1214/17-EJS1254>, and Wong and Ma (2010) <doi:10.1214/09-AOS755>.

This package implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions <doi:10.48550/arXiv.1902.00101>. Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values <doi:10.1080/03610918.2023.2234683>.

This package provides functions to estimate statistical errors of phylogenetic metrics particularly to detect binary trait influence on diversification, as well as a function to simulate trees with fixed number of sampled taxa and trait prevalence.

This package provides functions to calculate power and sample size for testing (1) mediation effects; (2) the slope in a simple linear regression; (3) odds ratio in a simple logistic regression; (4) mean change for longitudinal study with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in a simple Poisson regression.

This package provides a common problem faced by journal reviewers and authors is the question of whether the results of a replication study are consistent with the original published study. One solution to this problem is to examine the effect size from the original study and generate the range of effect sizes that could reasonably be obtained (due to random sampling) in a replication attempt (i.e., calculate a prediction interval). This package has functions that calculate the prediction interval for the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean.

Computes sample size and power for causal inference studies that use propensity score (PS) weighting. Supports continuous, binary, and time-to-event (survival) outcomes under four estimands: average treatment effect (ATE), average treatment effect on the treated (ATT), average treatment effect on the controls (ATC), and average treatment effect on the overlap population (ATO). For continuous and binary outcomes, the asymptotic variance of the Hajek inverse probability weighting estimator is derived under a logit-normal propensity score model, approximated by a Beta distribution matched through the Bhattacharyya overlap coefficient. For survival outcomes, the asymptotic variance of the propensity-score- weighted partial likelihood estimator is used for randomized trials and observational studies. The Schoenfeld formula is also available for randomized trial settings.

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.

This package provides functions for generating progressively Type-II censored data in a mixture structure and fitting models using a constrained EM algorithm. It can also create a progressive Type-II censored version of a given real dataset to be considered for model fitting.

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>).

Investigate (analytically or visually) the inputs and outputs of probabilistic analyses of health economic models using standard health economic visualisation and metamodelling methods.

An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. Lutkepohl, H. (2007, ISBN:3540262393).

Tokenizers break text into pieces that are more usable by machine learning models. Many tokenizers share some preparation steps. This package provides those shared steps, along with a simple tokenizer.

Provide easy methods to translate pieces of text. Functions send requests to translation services online.

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.

Market odds from from Pinnacle, an online sports betting bookmaker (see <https://www.pinnacle.com> for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. These datasets can be used to build models and compare statistical information with the information from prediction markets.The Major League Baseball (MLB) 2016 dataset can be used for sabermetrics analysis and also can be used in conjunction with other popular Major League Baseball (MLB) datasets such as Retrosheets or the Lahman package by merging by GameID.

Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

This package provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>. Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>. Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>. Matzke et al. (2013) <doi:10.1002/pmic.201200269>. Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>. Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.

Computes classical process capability indices (Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Pp, Ppk, Ppu, Ppl, Z) and the generalized process capability index Cpy (Maiti, Saha & Nanda, 2010) <doi:10.1080/16843703.2010.11673233> for any continuous or discrete quality characteristic. Users supply the probability density function (PDF) and cumulative distribution function (CDF) of the characteristic, and the package returns point estimates, bootstrap confidence intervals (percentile and BCa), and sensitivity tables/plots across ranges of short-term standard deviation (sigma), long-term standard deviation (s), desired yield (p0), and significance levels. Classical indices are recoverable as special cases under the normal distribution. The package follows the theory and notation of Kane (1986) <doi:10.1080/00224065.1986.11978984>, Chan, Cheng & Spiring (1988) <doi:10.1080/00224065.1988.11979102>, Pearn, Kotz & Johnson (1992) <doi:10.1080/00224065.1992.11979403>, Kotz & Johnson (2002) <doi:10.1080/00224065.2002.11980119>, Montgomery (2020, ISBN:978-1-119-39930-8), Juran (1974, ISBN:978-0-07-033176-1), Harry & Schroeder (2000, ISBN:978-0-385-49437-2), and the AIAG SPC Reference Manual (2005, ISBN:978-1-60534-026-3).

Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations allow for transforming variables with distributions distant from 0 to normality. Transformers are implemented as S4 objects. These allow for transforming new instances to normality after optimising fitting parameters on other data. A test for central normality allows for rejecting transformations that fail to produce a suitably normal distribution, independent of sample number.