ML Estimation for Multivariate Normal Data with Missing Values

Finds the Maximum Likelihood (ML) Estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values.

Data Sets from Mixed-Effects Models in S

Data sets and sample analyses from Pinheiro and Bates, "Mixed-effects Models in S and S-PLUS" (Springer, 2000).

Automatic Fixed Rank Kriging

Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) . For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes 'LatticeKrig' package.

Continuous Glucose Monitoring Data Analyzer

Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. It reads a series of data files, is able to convert various formats of time stamps, can deal with missing values, calculates both regular statistics and nonlinear statistics, and conducts group comparison. It also displays results in a concise format. Also contains two unique features new to 'CGM' analysis: one is the implementation of strictly standard mean difference and the class of effect size; the other is the development of a new type of plot called antenna plot. It corresponds to 'Zhang XD'(2018)'s article 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies'.

Robust Clustering Procedures

A clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) ).

Versatile Functions for Working with Pedigrees

Tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients and the relationship matrix (includes code for pedigrees from self-pollinated species). The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure ('Vazquez et al., 2010') .

Cytokine Profiling Analysis Tool

Provides comprehensive cytokine profiling analysis through quality control using biologically meaningful cutoffs on raw cytokine measurements and by testing for distributional symmetry to recommend appropriate transformations. Offers exploratory data analysis with summary statistics, enhanced boxplots, and barplots, along with univariate and multivariate analytical capabilities for in-depth cytokine profiling such as Principal Component Analysis based on Andrzej Maćkiewicz and Waldemar Ratajczak (1993) , Sparse Partial Least Squares Discriminant Analysis based on Lê Cao K-A, Boitard S, and Besse P (2011) , Random Forest based on Breiman, L. (2001) , and Extreme Gradient Boosting based on Tianqi Chen and Carlos Guestrin (2016) .

Impute Missing Glucose Values in CGM Data

Imputes missing glucose values in repeated-measures continuous glucose monitoring (CGM) data. Workflows create time-series features from raw timestamps, support model selection, and return the user's original columns plus an imputed glucose column. Methods include multiple imputation by chained equations (MICE; Azur et al. (2011) ), Random Forest regression (Breiman (2001) ), k-nearest-neighbor regression (Zhang (2016) ), XGBoost (Chen and Guestrin (2016) ), LightGBM (Ke et al. (2017) < https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>), and ARIMA forecasting with the forecast framework (Hyndman and Khandakar (2008) ). A Python-compatible backend uses 'reticulate' to call 'pandas', 'scikit-learn', 'statsmodels', Python 'xgboost', and optional Python 'lightgbm'.

Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen'

Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) to help safeguard against convergence issues.

Exploratory Reduced Reparameterized Unified Model Estimation

Perform a Bayesian estimation of the exploratory reduced reparameterized unified model (ErRUM) described by Culpepper and Chen (2018) .