This package provides functions to extract joint planes from 3D triangular mesh derived from point cloud and makes data available for structural analysis.

Set of common functions used for manipulating colors, detecting and interacting with RStudio', modeling, formatting, determining users operating system, feature scaling, and more!

This package implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.

This package provides a general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

This package performs bivariate composite likelihood and full information maximum likelihood estimation for polytomous logit-normit (graded logistic) item response theory (IRT) models.

Companion package to the book Simulation and Inference for Stochastic Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY.

Analysis of crossover interference in experimental crosses, particularly regarding the gamma model. See, for example, Broman and Weber (2000) <doi:10.1086/302923>.

This package provides functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

This package provides an easy to use library to setup, apply and make inference with discrete time and discrete space hidden Markov models.

This package provides means to run simulations for adaptive seamless designs with and without early outcomes for treatment selection and subpopulation type designs.

The r-abd package contains data sets and sample code for the Analysis of biological data by Michael Whitlock and Dolph Schluter.

This package provides support for linear order and unimodal order (univariate) isotonic regression and bivariate isotonic regression with linear order on both variables.

Redis is an advanced key-value cache and store. Redis supports many data structures including strings, hashes, lists, sets, sorted sets, bitmaps and hyperloglogs.

rdate connects to an RFC 868 time server over a TCP/IP network, printing the returned time and/or setting the system clock.

Computes the ridge partial correlation coefficients in a high or ultra-high dimensional linear regression problem. An extended Bayesian information criterion is also implemented for variable selection. Users provide the matrix of covariates as a usual dense matrix or a sparse matrix stored in a compressed sparse column format. Detail of the method is given in the manual.

Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico.

Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).

An iterative algorithm that improves the proximity matrix (PM) from a random forest (RF) and the resulting clusters as measured by the silhouette score.

This package contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).

Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data: ICC, Finn-Coefficient, Robinson's A, Kendall's W, Cohen's Kappa, ...

The Retained Component Criterion for Principal Component Analysis (RCC_PCA) is a tool to determine the optimal number of components to retain in PCA.

Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).

Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.

Simple Component Analysis (SCA) often provides much more interpretable components than Principal Components (PCA) while still representing much of the variability in the data.