Chapman & Hall/CRC Texts in Statistical Science – serie

Statistical Rethinking

A Bayesian Course with Examples in R and STAN

Statistical Inference

Statistics in Engineering

With Examples in MATLAB® and R, Second Edition

Sampling

Design and Analysis

Introduction to Generalized Linear Models

Generalized Additive Models

An Introduction with R, Second Edition

Models for Multi-State Survival Data

Rates, Risks, and Pseudo-Values

Theory of Spatial Statistics

A Concise Introduction

Theory of Spatial Statistics

A Concise Introduction

Modern Data Science with R

Introduction to Nonparametric Statistics

Time Series

A Data Analysis Approach Using R

Bayes Rules!

An Introduction to Applied Bayesian Modeling

Geographic Data Science with Python

Survival Analysis

Principles and Applications in Clinical Trials and Beyond

Applied Nonparametric Statistical Methods

Randomization, Bootstrap and Monte Carlo Methods in Biology

Bayesian Networks

With Examples in R

Surrogates

Gaussian Process Modeling, Design, and Optimization for the Applied Sciences

Probability, Statistics, and Data

A Fresh Approach Using R

Elementary Applications of Probability Theory

Statistics in Research and Development

Applied Statistics

Handbook of GENSTAT Analysis

Design and Analysis of Experiments and Observational Studies using R

Theory of Statistical Inference

Introduction to Modern Randomization-Based Design and Analysis for Causal Inference

Theory of Statistical Inference

Randomization, Bootstrap and Monte Carlo Methods in Biology

Richly Parameterized Linear Models

Additive, Time Series, and Spatial Models Using Random Effects