Generalized Efficient Regression-Based Imputation with Latent Processes

Implements a new multiple imputation method that draws imputations from a latent joint multivariate normal model which underpins generally structured data. This model is constructed using a sequence of flexible conditional linear models that enables the resulting procedure to be efficiently implemented on high dimensional datasets in practice. See Robbins (2021) .

Estimating Remaining Useful Life with Linear Mixed Effects Models

Provides tools for estimating the Remaining Useful Life (RUL) of degrading systems using linear mixed-effects models and creating a health index. It supports both univariate and multivariate degradation signals. For multivariate inputs, the signals are merged into a univariate health index prior to modeling. Linear and exponential degradation trajectories are supported (the latter using a log transformation). Remaining Useful Life (RUL) distributions are estimated using Bayesian updating for new units, enabling on-site predictive maintenance. Based on the methodology of Liu and Huang (2016) .

Gene Set Enrichment Meta-Analysis

Performing the different steps of gene set enrichment meta-analysis. It provides different functions that allow the application of meta-analysis based on the combination of effect sizes from different pathways in different studies to obtain significant pathways that are common to all of them.

Specification Tests for Parametric Propensity Score Models

The propensity score is one of the most widely used tools in studying the causal effect of a treatment, intervention, or policy. Given that the propensity score is usually unknown, it has to be estimated, implying that the reliability of many treatment effect estimators depends on the correct specification of the (parametric) propensity score. This package implements the data-driven nonparametric diagnostic tools for detecting propensity score misspecification proposed by Sant'Anna and Song (2019) .

Calculate Indices and Theoretical Physicochemical Properties of Protein Sequences

Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot 'XVG' output files from the 'GROMACS' molecular dynamics package.

Measure of Agreement Between Two Raters

Measure of agreement delta was originally by Martín & Femia (2004) . Since then has been considered as agreement measure for different fields, since their behavior is usually better than the usual kappa index by Cohen (1960) . The main issue with delta is that can not be computed by hand contrary to kappa. The current algorithm is based on the Version 5 of the delta windows program that can be found on < https://www.ugr.es/~bioest/software/delta/cmd.php?seccion=downloads>.

Bimodal Gumbel Distribution

Bimodal Gumbel distribution. General functions for performing extreme value analysis.

Generate Sample Censoring

Provides functions to generate censored samples of type I, II and III, from any random sample generator. It also supplies the option to create left and right censorship. Along with this, the generation of samples with interval censoring is in the testing phase, with two options of fixed length intervals and random lengths.

Build Network Based on Linear Mixed Models from EHRs

Analyzing longitudinal clinical data from Electronic Health Records (EHRs) using linear mixed models (LMM) and visualizing the results as networks. It includes functions for fitting LMM, normalizing adjacency matrices, and comparing networks. The package is designed for researchers in clinical and biomedical fields who need to model longitudinal data and explore relationships between variables For more details see Bates et al. (2015) .

Difference-in-Differences with a Continuous Treatment

Provides methods for difference-in-differences with a continuous treatment and staggered treatment adoption. Includes estimation of treatment effects and causal responses as a function of the dose, event studies indexed by length of exposure to the treatment, and aggregation into overall average effects. Uniform inference procedures are included, along with both parametric and nonparametric models for treatment effects. The methods are based on Callaway, Goodman-Bacon, and Sant'Anna (2025) .