Amelia II is an R package for multiple imputation of missing data. It uses a novel expectation-maximization with bootstrapping (EMB) algorithm that is faster, more scalable, and easier to use than traditional Markov chain Monte Carlo methods. The algorithm allows researchers to incorporate Bayesian priors on individual cell values, improving imputation models by including valuable information. It supports cross-sectional, time-series, and time-series cross-sectional data, and provides graphical diagnostics. Amelia II is user-friendly, with both a command line and graphical interface, and is robust and efficient. It is particularly useful for social science data with missing values, as it reduces bias and increases efficiency compared to listwise deletion. The EMB algorithm works by bootstrapping the data and using the EM algorithm to estimate complete-data parameters. Amelia II also allows for the inclusion of prior information, such as ridge priors for high missingness or observational priors for specific missing values. It can handle logical bounds on variables, such as proportions between 0 and 1 or durations greater than 0, by using truncated normal distributions. The package is available for R and AmeliaView, a graphical interface, and is compatible with Windows, Mac OS X, and Linux. It is widely used for its efficiency, ease of use, and ability to handle complex data structures.Amelia II is an R package for multiple imputation of missing data. It uses a novel expectation-maximization with bootstrapping (EMB) algorithm that is faster, more scalable, and easier to use than traditional Markov chain Monte Carlo methods. The algorithm allows researchers to incorporate Bayesian priors on individual cell values, improving imputation models by including valuable information. It supports cross-sectional, time-series, and time-series cross-sectional data, and provides graphical diagnostics. Amelia II is user-friendly, with both a command line and graphical interface, and is robust and efficient. It is particularly useful for social science data with missing values, as it reduces bias and increases efficiency compared to listwise deletion. The EMB algorithm works by bootstrapping the data and using the EM algorithm to estimate complete-data parameters. Amelia II also allows for the inclusion of prior information, such as ridge priors for high missingness or observational priors for specific missing values. It can handle logical bounds on variables, such as proportions between 0 and 1 or durations greater than 0, by using truncated normal distributions. The package is available for R and AmeliaView, a graphical interface, and is compatible with Windows, Mac OS X, and Linux. It is widely used for its efficiency, ease of use, and ability to handle complex data structures.