Loading NONMEM (NONlinear Mixed-Effect Modeling, <https://www.iconplc.com/solutions/technologies/nonmem/>) and PSN (Perl-speaks-NONMEM, <https://uupharmacometrics.github.io/PsN/>) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty.

Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Automate the following individual or batch processing: check local source packages; build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment.

Base package for Neuroconductor', which includes many helper functions that interact with objects of class nifti', implemented by package oro.nifti', for reading/writing and also other manipulation functions.

Mainly for maximum likelihood estimation of nonparametric and semiparametric mixture models, but can also be used for fitting finite mixtures. The algorithms are developed in Wang (2007) <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010) <doi:10.1007/s11222-009-9117-z>.

This package provides tools for working with the National Hydrography Dataset, with functions for querying, downloading, and networking both the NHD <https://www.usgs.gov/national-hydrography> and NHDPlus <https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus> datasets.

Similarity measures for hierarchical clustering of objects characterized by nominal (categorical) variables. Evaluation criteria for nominal data clustering.

Counts syllables in character vectors for English words. Imputes syllables as the number of vowel sequences for words not found.

This package provides a variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods. Its use is illustrated via a number of empirical applications and simulated examples.

Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks, incorporating node-level predictors of mixed-membership vectors, as well as dyad-level predictors. For networks observed over time, the model defines a hidden Markov process that allows the effects of node-level predictors to evolve in discrete, historical periods. In addition, the package offers a variety of utilities for exploring results of estimation, including tools for conducting posterior predictive checks of goodness-of-fit and several plotting functions. The package implements methods described in Olivella, Pratt and Imai (2019) Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts', available at <https://www.santiagoolivella.info/pdfs/socnet.pdf>.

This package performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.

This package provides a reproducible workflow for binning and visualizing NMR (nuclear magnetic resonance) spectra from environmental samples. The nmrrr package is intended for post-processing of NMR data, including importing, merging and, cleaning data from multiple files, visualizing NMR spectra, performing binning/integrations for compound classes, and relative abundance calculations. This package can be easily inserted into existing analysis workflows by users to help with analyzing and interpreting NMR data.

Retrieve and plot word frequencies through time from the "Google Ngram Viewer" <https://books.google.com/ngrams>.

Body Shape and related measurements from the US National Health and Nutrition Examination Survey (NHANES, 1999-2004). See http://www.cdc.gov/nchs/nhanes.htm for details.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).

This package implements the nonparametric trend test for one or several samples as proposed by Bathke (2009) <doi:10.1007/s00184-008-0171-x>. The method provides a unified framework for analyzing trends in both independent and dependent data samples, making it a versatile tool for various study designs. The package allows for the evaluation of different trend alternatives, including two-sided (general trend), monotonic increasing, and monotonic decreasing trends. As a nonparametric procedure, it does not require the assumption of data normality, offering a robust alternative to parametric tests.

Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.

This package provides tools for processing and analyzing data from the O-GlcNAcAtlas database <https://oglcnac.org/>, as described in Ma (2021) <doi:10.1093/glycob/cwab003>. It integrates UniProt <https://www.uniprot.org/> API calls to retrieve additional information. It is specifically designed for research workflows involving O-GlcNAcAtlas data, providing a flexible and user-friendly interface for customizing and downloading processed results. Interactive elements allow users to easily adjust parameters and handle various biological datasets.

Analysis of molecular marker data from model and non-model systems. For the later, it allows statistical analysis by simultaneously estimating linkage and linkage phases (genetic map construction) according to Wu and colleagues (2002) <doi:10.1006/tpbi.2002.1577>. All analysis are based on multi-point approaches using hidden Markov models.

In the context of data fusion, the package provides a set of functions dedicated to the solving of recoding problems using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.

This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" <doi:10.3390/risks10100197>. The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. Full_Cost: (obesity_cost_full) Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year; 4. National_Summary: (obesity_cost_national_summary) National summary cost calculations in a given year. Three functions are included to assist users in calling and adjusting the mentioned tables and they are data_load(), data_produce(), and rel_risk_fun().

Calculates autoecological data (optima and tolerance ranges) of a biological species given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa have unequal occurrences. See the detailed methodology by Birks et al. (1990) <doi:10.1098/rstb.1990.0062>, and a case example by Potapova and Charles (2003) <doi:10.1046/j.1365-2427.2003.01080.x>.

Aids practitioners to optimally design experiments that measure the slope divided by the intercept and provides confidence intervals for the ratio.

Optimal scaling of a data vector, relative to a set of targets, is obtained through a least-squares transformation subject to appropriate measurement constraints. The targets are usually predicted values from a statistical model. If the data are nominal level, then the transformation must be identity-preserving. If the data are ordinal level, then the transformation must be monotonic. If the data are discrete, then tied data values must remain tied in the optimal transformation. If the data are continuous, then tied data values can be untied in the optimal transformation.

Complete R implementation of the OpenAI Python SDK'. Provides full compatibility with the OpenAI API including chat completions, embeddings', images, audio, fine-tuning, and model management.