This package provides a spatiotemperal data object in a relational data structure to separate the recording of time variant/ invariant variables. See the Journal of Statistical Software reference: <doi:10.18637/jss.v110.i07>.

Estimation of quantile regression models for survival data.

Method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.

Access chemical, hazard, bioactivity, and exposure data from the Computational Toxicology and Exposure ('CTX') APIs <https://www.epa.gov/comptox-tools/computational-toxicology-and-exposure-apis>. ctxR was developed to streamline the process of accessing the information available through the CTX APIs without requiring prior knowledge of how to use APIs. Most data is also available on the CompTox Chemical Dashboard ('CCD') <https://comptox.epa.gov/dashboard/> and other resources found at the EPA Computational Toxicology and Exposure Online Resources <https://www.epa.gov/comptox-tools>.

Fitting and inference functions for generalized linear models with constrained coefficients.

This package provides a framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time.

Search across R files with contextual results, highlights and clickable links. Includes an add-in for further workflow enhancement.

This package contains 3 maps. 1) US States 2) US Counties 3) Countries of the world.

This package provides essential Cleaning Validation functions for complying with pharmaceutical cleaning process regulatory standards. The package includes non-parametric methods to analyze drug active-ingredient residue (DAR), cleaning agent residue (CAR), and microbial colonies (Mic) for non-Poisson distributions. Additionally, Poisson methods are provided for Mic analysis when Mic data follow a Poisson distribution.

Germline and somatic locus data which contain the total read depth and B allele read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster model can deal with the SNVs mutation and the CNAs mutation.

This package creates compact letter displays (CLDs) for pairwise comparisons from statistical post-hoc tests. Groups sharing the same letter are not significantly different from each other. Supports multiple input formats including results from stats pairwise tests, DescTools', PMCMRplus', rstatix', symmetric matrices of p-values, and data frames. Provides a consistent interface for visualizing statistical groupings across different testing frameworks.

Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and McCaffrey <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ>. Functions used in Huang and Li <doi:10.3758/s13428-021-01627-0>, Huang, Wiedermann', and Zhang <doi:10.1080/00273171.2022.2077290>, and Huang, Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness).

Imports conversation transcripts into R, concatenates them into a single dataframe appending event identifiers, cleans and formats the text, then yokes user-specified psycholinguistic database values to each word. ConversationAlign then computes alignment indices between two interlocutors across each transcript for >40 possible semantic, lexical, and affective dimensions. In addition to alignment, ConversationAlign also produces a table of analytics (e.g., token count, type-token-ratio) in a summary table describing your particular text corpus.

Filtering, also known as gating, of flow cytometry samples using the curvHDR method, which is described in Naumann, U., Luta, G. and Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.

Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with corx'.

An implementation of double generalized linear model (DGLM) building with variable selection procedures and handling of interaction terms and other complex situations. We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set.

Estimation of Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) <doi:10.1111/1467-9965.00114> as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) <doi:10.1111/j.1467-9868.2005.00508.x> are included.

This package provides a comprehensive toolkit for generating continuous test norms in psychometrics and biometrics, and analyzing model fit. The package offers both distribution-free modeling using Taylor polynomials and parametric modeling using the beta-binomial and the Sinh-Arcsinh distribution. Originally developed for achievement tests, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package provides several advantages: It minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and even supports conventional norming. It generates norm tables including confidence intervals. It also includes methods for addressing representativeness issues through Iterative Proportional Fitting. Based on Lenhard et al. (2016) <doi:10.1177/1073191116656437>, Lenhard et al. (2019) <doi:10.1371/journal.pone.0222279>, Lenhard and Lenhard (2021) <doi:10.1177/0013164420928457> and Gary et al. (2023) <doi:10.1007/s00181-023-02456-0>.

Copula-based imputation methods: parametric and nonparametric algorithms for missing multivariate data through conditional copulas.

Create Pairwise Comparison Matrices for use in the Analytic Hierarchy Process. The Pairwise Comparison Matrix created will be a logical matrix, which unlike a random comparison matrix, is similar to what a rational decision maker would create on the basis of a preference vector for the alternatives considered.

Builds the coincident profile proposed by Martinez, W and Nieto, Fabio H and Poncela, P (2016) <doi:10.1016/j.spl.2015.11.008>. This methodology studies the relationship between a couple of time series based on the the set of turning points of each time series. The coincident profile establishes if two time series are coincident, or one of them leads the second.

Retorna detalhes de dados de CEPs brasileiros, bairros, logradouros e tal. (Returns info of Brazilian postal codes, city names, addresses and so on.).

Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline. Function outputs fits and various types of inference.

Perform evaluation of automatic subject indexing methods. The main focus of the package is to enable efficient computation of set retrieval and ranked retrieval metrics across multiple dimensions of a dataset, e.g. document strata or subsets of the label set. The package also provides the possibility of computing bootstrap confidence intervals for all major metrics, with seamless integration of parallel computation and propensity scored variants of standard metrics.