Mining Association Rules and Frequent Itemsets

Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. Hahsler, Gruen and Hornik (2005) .

Identifies Patterned Responses in Scales

Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c).

'2bit' 'C' Library

A trimmed down copy of the "kent-core source tree" turned into a 'C' library for manipulation of '.2bit' files. See < https://genome.ucsc.edu/FAQ/FAQformat.html#format7> for a quick overview of the '2bit' format. The "kent-core source tree" can be found here: < https://github.com/ucscGenomeBrowser/kent-core/>. Only the '.c' and '.h' files from the source tree that are related to manipulation of '.2bit' files were kept. Note that the package is primarily useful to developers of other R packages who wish to use the '2bit' 'C' library in their own 'C'/'C++' code.

Langa-Weir Classification of Cognitive Function for 2022 HRS Data

Generates the Langa-Weir classification of cognitive function for the 2022 Health and Retirement Study (HRS) cognition data. It is particularly useful for researchers studying cognitive aging who wish to work with the most recent release of HRS data. The package provides user-friendly functions for data preprocessing, scoring, and classification allowing users to easily apply the Langa-Weir classification system. For details regarding the; HRS < https://hrsdata.isr.umich.edu/> and Langa-Weir classifications < https://hrsdata.isr.umich.edu/data-products/langa-weir-classification-cognitive-function-1995-2020>.

Wrapper for Statistics Portugal API

Set of wrapper and helper functions to facilitate interaction with the Statistics Portugal (Instituto Nacional de Estatistica - INE) API (< https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_api&INST=322751522&xlang=en>).

Primal or Dual Cone Projections with Routines for Constrained Regression

Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013) for more details.

Information Matrices for 'lmeStruct' and 'glsStruct' Objects

Provides analytic derivatives and information matrices for fitted linear mixed effects (lme) models and generalized least squares (gls) models estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively. The package includes functions for estimating the sampling variance-covariance of variance component parameters using the inverse Fisher information. The variance components include the parameters of the random effects structure (for lme models), the variance structure, and the correlation structure. The expected and average forms of the Fisher information matrix are used in the calculations, and models estimated by full maximum likelihood or restricted maximum likelihood are supported. The package also includes a function for estimating standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) ) based on fitted lme or gls models.

Nonlinear Mixed Effects Models in Population PK/PD, Data

Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 ). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ).

Bayesian and Likelihood Analysis of Dynamic Linear Models

Provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.

Color-Based Plots for Multivariate Visualization

Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps. Lower-level functions map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.