Easily create functions to map between different sets of values, such as for re-labeling categorical variables.

Create dummy variables from categorical data. This package can convert categorical data (factor and ordered) into dummy variables and handle multiple columns simultaneously. This package enables to select whether a dummy variable for base group is included (for principal component analysis/factor analysis) or excluded (for regression analysis) by an option. makedummies function accepts data.frame', matrix', and tbl (tibble) class (by tibble package). matrix class data is automatically converted to data.frame class.

This package provides a supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, <doi:10.1002/sam.70055> can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set.

Create an interactive table of descriptive statistics in HTML. This table is typically used for exploratory analysis in a clinical study (referred to as Table 1').

An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>), whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).

This package provides a collection of statistical tests for the detection of differential item functioning (DIF) in multistage tests. Methods entail logistic regression, an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests. The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform DIF effects are available depending on which method is used.

R interface to MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing MLflow', tracking experiments, creating and running projects, and saving and serving models.

Estimation of relative risks, risk differences, and partial effects from mixed model. Marginalisation over random effect terms is accomplished using Markov Chain Monte Carlo.

This package creates interactive maps using MapLibre GL and deck.gl via htmlwidgets'. Provides GPU-accelerated layers for points, lines and polygons, plus linked user interface components such as filters, views and summary cards for exploratory analysis and production dashboards.

This package implements generalized maximum entropy estimation for linear regression, kink regression, and smooth transition kink regression models. The approach represents unknown parameters and disturbances as probability distributions over discrete support spaces and estimates them by maximizing entropy subject to model constraints. It is particularly suited to ill-posed problems and does not require distributional assumptions on the error term. The methods have been applied in empirical studies such as Tarkhamtham and Yamaka (2019) <https://thaijmath.com/index.php/thaijmath/article/view/867/870> and Maneejuk, Yamaka, and Sriboonchitta (2022) <doi:10.1080/03610918.2020.1836214>.

Fused lasso method to cluster and estimate regression coefficients of the same covariate across different data sets when a large number of independent data sets are combined. Package supports Gaussian, binomial, Poisson and Cox PH models.

MedDRA data is used for defining adverse events in clinical studies. You can load and merge the data for use in categorizing the adverse events using this package. The package requires the data licensed from MedDRA <https://www.meddra.org/>.

Deconvolution of thermal decay curves allows you to quantify proportions of biomass components in plant litter. Thermal decay curves derived from thermogravimetric analysis (TGA) are imported, modified, and then modelled in a three- or four- part mixture model using the Fraser-Suzuki function. The output is estimates for weights of pseudo-components corresponding to hemicellulose, cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, H. (2007) <doi:10.1016/j.jaap.2006.12.008>, à rfão, J. J. M. and Figueiredo, J. L. (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.

An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, Elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, supervised principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9>, and supervised partial least squares analysis by Inge S. Helland <https://www.jstor.org/stable/4616159>. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

Computational functions for player metrics in major league baseball including batting, pitching, fielding, base-running, and overall player statistics. This package is actively maintained with new metrics being added as they are developed.

This package creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types.

Create and optimize (semi) MDPs with discrete time steps and state space. Both hierarchical and ordinary-traditional MDPs can be modeled.

This package implements the method of successive dichotomizations by Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>, which estimates item measures, person measures and ordered rating category thresholds given ordinal rating scale data.

Model stability and variable inclusion plots [Mueller and Welsh (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al. (2008, <doi:10.1214/07-AOS517>); Jiang et al. (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.

This package provides a generalised workflow for Matching-Adjusted Indirect Comparison (MAIC) analysis, which supports both anchored and non-anchored MAIC methods. In MAIC, unbiased trial outcome comparison is achieved by weighting the subject-level outcomes of the intervention trial so that the weighted aggregate measures of prognostic or effect-modifying variables match those of the comparator trial. Measurements supported include time-to-event (e.g., overall survival) and binary (e.g., objective tumor response). The method is described in Signorovitch et al. (2010) <doi:10.2165/11538370-000000000-00000> and Signorovitch et al. (2012) <doi:10.1016/j.jval.2012.05.004>.

Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.

Implementation of commonly used p-value-based and parametric multiple testing procedures (computation of adjusted p-values and simultaneous confidence intervals) and parallel gatekeeping procedures based on the methodology presented in the book "Multiple Testing Problems in Pharmaceutical Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and Frank Bretz) published by Chapman and Hall/CRC Press 2009.

Straightforward and detailed evaluation of machine learning models. MLeval can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. MLeval accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. MLeval produces a range of evaluation metrics with confidence intervals.

This package provides functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). Methods for selecting the best dimension are also included. Xia (2002) <doi:10.1111/1467-9868.03411>; Xia (2007) <doi:10.1214/009053607000000352>; Wang (2008) <doi:10.1198/016214508000000418>.