R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental populations derived from inbred lines. It is implemented as an add-on package for the freely available statistical software R. The package includes functions for estimating genetic maps, identifying genotyping errors, and performing single-QTL and two-dimensional, two-QTL genome scans using multiple methods, with the possibility of including covariates. R/qtl accepts input in various formats and is available for Windows, Unix, and MacOS. A key component of R/qtl is the hidden Markov model (HMM) technology for handling missing and partially missing genotype data. The core of R/qtl is a general implementation of HMM technology for experimental crosses, with possible allowance for genotyping errors. Current specific implementations include backcrosses, intercrosses, and phase-known four-way crosses; the code may be extended for more complex crosses. R/qtl includes functions for identifying genotyping errors, visualizing genotyping data, identifying errors in marker order, and re-estimating inter-marker distances. The user may perform single-QTL genome scans and two-dimensional, two-QTL genome scans under a normal model, with the possible inclusion of covariates, using the EM algorithm, Haley–Knott regression, and multiple imputation. R/qtl also includes facilities for performing single-QTL genome scans by non-parametric interval mapping and binary trait mapping. Higher-order QTL models may be fit by multiple imputation. LOD thresholds may be estimated by permutation tests. R/qtl is under continual development, with current efforts focusing on the fit of higher-order QTL models by multiple interval mapping, techniques for model comparison and search, and proper treatment of the X chromosome. Future plans include coordinated analysis of multiple traits, analysis of recombinant inbred lines with random line effects, and analysis of multiple-QTL models for binary traits. A graphical user interface is being developed in collaboration with Kenneth F. Manly and colleagues.R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental populations derived from inbred lines. It is implemented as an add-on package for the freely available statistical software R. The package includes functions for estimating genetic maps, identifying genotyping errors, and performing single-QTL and two-dimensional, two-QTL genome scans using multiple methods, with the possibility of including covariates. R/qtl accepts input in various formats and is available for Windows, Unix, and MacOS. A key component of R/qtl is the hidden Markov model (HMM) technology for handling missing and partially missing genotype data. The core of R/qtl is a general implementation of HMM technology for experimental crosses, with possible allowance for genotyping errors. Current specific implementations include backcrosses, intercrosses, and phase-known four-way crosses; the code may be extended for more complex crosses. R/qtl includes functions for identifying genotyping errors, visualizing genotyping data, identifying errors in marker order, and re-estimating inter-marker distances. The user may perform single-QTL genome scans and two-dimensional, two-QTL genome scans under a normal model, with the possible inclusion of covariates, using the EM algorithm, Haley–Knott regression, and multiple imputation. R/qtl also includes facilities for performing single-QTL genome scans by non-parametric interval mapping and binary trait mapping. Higher-order QTL models may be fit by multiple imputation. LOD thresholds may be estimated by permutation tests. R/qtl is under continual development, with current efforts focusing on the fit of higher-order QTL models by multiple interval mapping, techniques for model comparison and search, and proper treatment of the X chromosome. Future plans include coordinated analysis of multiple traits, analysis of recombinant inbred lines with random line effects, and analysis of multiple-QTL models for binary traits. A graphical user interface is being developed in collaboration with Kenneth F. Manly and colleagues.