Journal of Statistical Software
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hibayes: An R Package to Fit Individual-Level, Summary-Level and Single-Step Bayesian Regression Models for Genomic Prediction and Genome-Wide Association Studies
Get PDFWith the rapid development of sequencing technology, the costs of individual genotyping have been reduced dramatically, leading to genomic prediction and genome-wide association studies being widely promoted and used to predict the unknown phenotypes and to locate candidate genes for animal and plant economic traits and, increasingly, for human diseases. Developing new advanced statistical models to improve prediction accuracy and location precision for the traits with various genetic architectures has always been a hot topic in those two research domains. The Bayesian regression model (BRM) has played a crucial role in the past decade, and it has been used widely in relevant genetic analyses owing to its flexible model assumptions on the unknown genetic architecture of complex traits. To fully utilize the available data from either a self-designed experimental population or a public database, statistical geneticists have constantly extended the fitting capacity of BRM, and a series of new methodologies have been proposed for different application scenarios. Here we introduce the R package hibayes, a software tool that can be used to fit individual-level, summary-level, and single-step Bayesian regression models. Including also the richest methods achieved thus far, it covers most of the functionalities involved in the field of genomic prediction and genome-wide association studies, potentially helping to address a wide range of research problems, while retaining an easy-to-learn and flexible-to-use experience. We believe that package hibayes will facilitate the academic research and practical application of statistical genetics for humans, plants, and animals
dame-flame: A Python Package Providing Fast Interpretable Matching for Causal Inference
Get PDFdame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package implements the dynamic almost matching exactly (DAME) and fast, large-scale almost matching exactly (FLAME) algorithms, which match treatment and control units on subsets of the covariates. The resulting matched groups are interpretable, because the matches are made directly on covariates, and high-quality, because machine learning is used to determine which covariates are important to match on instead of human inputs. The package provides several adjustable parameters to adapt the algorithms to specific applications, and can calculate treatment effects after matching. The most recent source code of the implementation is available at https://github.com/almost-matching-exactly/DAME-FLAME-Python-Package
TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R
Get PDFTSCI implements treatment effect estimation from observational data under invalid instruments in the R statistical computing environment. Existing instrumental variable approaches rely on arguably strong and untestable identification assumptions, which limits their practical application. TSCI does not require the classical instrumental variable identification conditions and is effective even if all instruments are invalid. TSCI implements a two-stage algorithm. In the first stage, machine learning is used to cope with nonlinearities and interactions in the treatment model. In the second stage, a space to capture the instrument violations is selected in a data-adaptive way. These violations are then projected out to estimate the treatment effect
counterfactuals: An R Package for Counterfactual Explanation Methods
No full textCounterfactual explanation methods provide information on how feature values of individual observations must be changed to obtain a desired prediction. Despite the increasing amount of proposed methods in research, only a few implementations exist, whose interfaces and requirements vary widely. In this work, we introduce the counterfactuals R package, which provides a modular and unified R6-based interface for counterfactual explanation methods. We implemented three existing counterfactual explanation methods and propose some optional methodological extensions to generalize these methods to different scenarios and to make them more comparable. We explain the structure and workflow of the package using real use cases and show how to integrate additional counterfactual explanation methods into the package. In addition, we compared the implemented methods for a variety of models and datasets with regard to the quality of their counterfactual explanations and their runtime behavior
pyrichlet: A Python Package for Density Estimation and Clustering Using Gaussian Mixture Models
Get PDFBayesian nonparametric models have proven to be successful tools for clustering and density estimation. While there exists a nourished ecosystem of implementations in R, for Python there are only a few. Here we develop a Python package called pyrichlet, for Bayesian nonparametric density estimation and clustering using various state-of-the-art Gaussian mixture models that generalize the well established Dirichlet process mixture, many of which are fairly new. Implementation is performed using Markov chain Monte Carlo techniques as well as variational Bayes methods. This article contains a detailed description of pyrichlet and examples for its usage with a real dataset
RESI: An R Package for Robust Effect Sizes
Get PDFEffect size indices are useful parameters that quantify the strength of association and are unaffected by sample size. There are many available effect size parameters and estimators, but it is difficult to compare effect sizes across studies as most are defined for a specific type of population parameter. We recently introduced a new robust effect size index (RESI) and confidence interval, which is advantageous because it is not model-specific. Here we present the RESI R package, which makes it easy to report the RESI and its confidence interval for many different model classes, with a consistent interpretation across parameters and model types. The package produces coefficient, ANOVA tables, and overall Wald tests for model inputs, appending the RESI estimate and confidence interval to each. The package also includes functions for visualization and conversions to and from other effect size measures. For illustration, we analyze and interpret three datasets using different model types
Mixed-Effects Additive Transformation Models with the R Package tramME
Get PDFRegression models that accommodate correlated observations and potential nonlinear predictor-outcome relationships are fundamental in analyzing experimental and observational data. Unlike traditional parametric approaches, transformation models make weaker assumptions on the conditional response distribution, thus allowing for a more universal applicability to at least ordered univariate outcomes. This flexibility makes transformation models an attractive choice for modeling complex relationships in a wide range of domains. The R package tramME extends the transformation model framework with general random effect structures and penalized smooth terms to adapt to dependent data and nonlinear predictor-outcome relationships. This paper presents the statistical framework and implementation details of tramME, including its integration with other popular R packages for transformation modeling (mlt), mixed-effects (lme4) and additive models (mgcv). The package employs the efficient Template Model Builder framework (TMB) for fully parametric likelihood-based estimation and inference. Two illustrations demonstrate that tramME can readily model complex, dependent data structures under settings where the choice of the outcome distribution type is challenging
NUBO: A Transparent Python Package for Bayesian Optimization
Get PDFNUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimization framework for the optimization of expensive-to-evaluate black-box functions, such as physical experiments and computer simulators. Bayesian optimization is a costefficient optimization strategy that uses surrogate modelling via Gaussian processes to represent an objective function and acquisition functions to guide the selection of candidate points to approximate the global optimum of the objective function. NUBO itself focuses on transparency and user experience to make Bayesian optimization easily accessible to researchers from all disciplines. Clean and understandable code, precise references, and thorough documentation ensure transparency, while user experience is ensured by a modular and flexible design, easy-to-write syntax, and careful selection of Bayesian optimization algorithms. NUBO allows users to tailor Bayesian optimization to their specific problem by writing the optimization loop themselves using the provided building blocks. It supports sequential single-point, parallel multi-point, and asynchronous optimization of bounded, constrained, and/or mixed (discrete and continuous) parameter input spaces. Only algorithms and methods that are extensively tested and validated to perform well are included in NUBO. This ensures that the package remains compact and does not overwhelm the user with an unnecessarily large number of options. The package is written in Python but does not require expert knowledge of Python to optimize your simulators and experiments. NUBO is distributed as open-source software under the BSD 3-Clause license
StatisticalProcessMonitoring.jl: A General Framework for Statistical Process Monitoring in Julia
Get PDFStatistical process monitoring (SPM) control charts are widely used for monitoring the stability of sequential processes. Currently, there is no open-source software which provides a general and extensible implementation of control charts. StatisticalProcessMonitoring.jl is a novel Julia package which aims at addressing this gap, offering support for monitoring various type of data, such as univariate and multivariate observations, partially-observed data streams, and profiles. The package introduces an extensible SPM framework, allowing users to seamlessly design control charts for structured data types using the existing implementation. By introducing a flexible implementation of control charts, StatisticalProcessMonitoring.jl provides fully-automated and efficient algorithms for determining control limits and tuning control chart hyperparameters. These algorithms can accommodate various commonly-used performance metrics based on the run length distribution. The package further leverages existing packages in the Julia ecosystem to offer users a range of optimization and plotting functionalities
StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables
Get PDFStepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with Bolk-Croon-Hagenaars and maximum likelihood corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides an additional R wrapper