Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist. These difficulties are related to those arising from logistic and multinomial regression. References for methods are given in the separate function documents. Supported by grant NSF DMS 1712839.

Permutation based non-parametric analysis of CRISPR screen data. Details about this algorithm are published in the following paper published on BMC genomics, Jia et al. (2017) <doi:10.1186/s12864-017-3938-5>: A permutation-based non-parametric analysis of CRISPR screen data. Please cite this paper if you use this algorithm for your paper.

This package provides a RStudio addin allowing to paste the content of the clipboard as a comment block or as roxygen lines. This is very useful to insert an example in the roxygen block.

Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. Designed for researchers and practitioners in psychology, sociology, and related fields, the package facilitates the quantification and visualization of different dimensions related to personal values from survey data. It incorporates the recommended statistical adjustment to enhance the accuracy and interpretation of the results.

This function plots a contour line with a user-defined probability and tightness of fit.

References and cites R and R packages on the fly in R Markdown and Quarto'. pakret provides a minimalist API that generates preformatted citations for R and R packages, and adds their references to a .bib file directly from within your document.

Quickly visualize R project directory structures with automatic project detection and clean tree output.

Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).

This package provides specialized color palettes representing phenological transitions and biological lifecycles within Philippine landscapes. Rather than abstract gradients, these scales are anchored to topologically grounded states such as agricultural maturity, seasonal vegetation shifts, and environmental readiness. Palettes are indexed against the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale (Meier, 2023) <https://www.openagrar.de/servlets/MCRFileNodeServlet/openagrar_derivate_00010428/BBCH-Skala_en.pdf> for terrestrial vegetation and the Reef Health Index (RHI) framework (McField and Kramer, 2007) <https://www.healthyreefs.org> for marine ecosystems. This ensures scientific interoperability across archipelagic spatial models, aligning with global standards for ecological state-transition modeling (Schwartz, 2013) <doi:10.1007/978-94-007-6925-0>.

An interactive document for preprocessing the dataset using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/PREPShiny/>.

This package provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true sequence of binary splits).

This package provides a coding assistant using Perplexity's Large Language Models <https://www.perplexity.ai/> API. A set of functions and RStudio add-ins that aim to help R developers.

Collect marketing data from Pinterest Ads using the Windsor.ai API <https://windsor.ai/api-fields/>. Use four spaces when indenting paragraphs within the Description.

Personalize drug regimens using individual pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) profiles. By combining therapeutic drug monitoring (TDM) data with a population model, posologyr offers accurate posterior estimates and helps compute optimal individualized dosing regimens. The empirical Bayes estimates are computed following the method described by Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.

Reconstruct pedigrees from genotype data, by optimising the likelihood over all possible pedigrees subject to given restrictions. Tailor-made plots facilitate evaluation of the output. This package is part of the pedsuite ecosystem for pedigree analysis. In particular, it imports pedprobr for calculating pedigree likelihoods and forrel for estimating pairwise relatedness.

This package provides functions for modeling, comparing, and visualizing photosynthetic light response curves using established mechanistic and empirical models like the rectangular hyperbola Michaelis-Menton based models ((eq1 (Baly (1935) <doi:10.1098/rspb.1935.0026>)) (eq2 (Kaipiainenn (2009) <doi:10.1134/S1021443709040025>)) (eq3 (Smith (1936) <doi:10.1073/pnas.22.8.504>))), hyperbolic tangent based models ((eq4 (Jassby & Platt (1976) <doi:10.4319/LO.1976.21.4.0540>)) (eq5 (Abe et al. (2009) <doi:10.1111/j.1444-2906.2008.01619.x>))), the non-rectangular hyperbola model (eq6 (Prioul & Chartier (1977) <doi:10.1093/oxfordjournals.aob.a085354>)), exponential based models ((eq8 (Webb et al. (1974) <doi:10.1007/BF00345747>)), (eq9 (Prado & de Moraes (1997) <doi:10.1007/BF02982542>))), and finally the Ye model (eq11 (Ye (2007) <doi:10.1007/s11099-007-0110-5>)). Each of these nonlinear least squares models are commonly used to express photosynthetic response under changing light conditions and has been well supported in the literature, but distinctions in each mathematical model represent moderately different assumptions about physiology and trait relationships which ultimately produce different calculated functional trait values. These models were all thoughtfully discussed and curated by Lobo et al. (2013) <doi:10.1007/s11099-013-0045-y> to express the importance of selecting an appropriate model for analysis, and methods were established in Davis et al. (in review) to evaluate the impact of analytical choice in phylogenetic analysis of the function-valued traits. Gas exchange data on 28 wild sunflower species from Davis et al.are included as an example data set here.

Conduct post-selection inference for regression coefficients in linear models after they have been selected by adjusted R squared. The p-values and confidence intervals are valid after model selection with the same data. This allows the user to use all data for both model selection and inference without losing control over the type I error rate. The provided tests are more powerful than data splitting, which bases inference on less data since it discards all information used for selection.

This package provides functions for obtaining the density, random deviates and maximum likelihood estimates of the Poisson lognormal distribution and the bivariate Poisson lognormal distribution.

This package implements partial transfer learning (PTL) for causal effect estimation using source and target data, with bootstrap-based source detection. Provides data generating processes and nuisance functions for simulation.

This package provides a unified interface to access and manipulate various Philippine statistical classifications. It allows users to retrieve, filter, and harmonize classification data, making it easier to work with Philippine statistical data in R.

This package provides a number of functions to simplify and automate the scoring, comparison, and evaluation of different ways of creating composites of data. It is particularly aimed at facilitating the creation of physiological composites of metabolic syndrome symptom score (MetSSS) and allostatic load (AL). Provides a wrapper to calculate the MetSSS on new data using the Healthy Hearts formula.

Computes power and level tables for goodness-of-fit tests for the normal, Laplace, and uniform distributions. Generates output in LaTeX format to facilitate reporting and reproducibility. Explanatory graphs help visualize the statistical power of test statistics under various alternatives. For more details, see Lafaye De Micheaux and Tran (2016) <doi:10.18637/jss.v069.i03>.

Simulates pooled sequencing data under a variety of conditions. Also allows for the evaluation of the average absolute difference between allele frequencies computed from genotypes and those computed from pooled data. Carvalho et al., (2022) <doi:10.1101/2023.01.20.524733>.

Calculate Plant Stress Response Index (PSRI) from time-series germination data with optional radicle vigor integration. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) <doi:10.1002/agg2.20087>. Provides clean, direct PSRI calculations suitable for agricultural research and statistical analysis. Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach.