**Estimation of Life Expectancies Using Multi-State Models** (**elect**)

Functions to compute state-specific and marginal life expectancies. The computation is based on a fitted continuous-time multi-state model that includes an absorbing death state; see Van den Hout (2017, ISBN:9781466568402). The fitted multi-state model model should be estimated using the ‘msm’ package using age as the time-scale.

**Imagine Your Data Before You Collect It** (**fabricatr**)

Helps you imagine your data before you collect it. Hierarchical data structures and correlated data can be easily simulated, either from random number generators or by resampling from existing data sources. This package is faster with ‘data.table’ and ‘mvnfast’ installed.

**Estimate Kinship and FST under Arbitrary Population Structure** (**popkin**)

Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright’s fixation index). Method described in Ochoa and Storey (2016) <doi:10.1101/083923>.

**Accompaniment Package to ModernDive: An Introduction to Statistical and Data Sciences via R** (**moderndive**)

An accompaniment R package to ModernDive: An Introduction to Statistical and Data Sciences via R available at <http://…/>, in particular wrapper functions targeted at novices to easily generate tidy linear regression output.

**Prediction and Accuracy Measures for Nonlinear Models and for Right-Censored Time-to-Event Data** (**PAmeasures**)

We propose a pair of summary measures for the predictive power of a prediction function based on a regression model. The regression model can be linear or nonlinear, parametric, semi-parametric, or nonparametric, and correctly specified or mis-specified. The first measure, R-squared, is an extension of the classical R-squared statistic for a linear model, quantifying the prediction function’s ability to capture the variability of the response. The second measure, L-squared, quantifies the prediction function’s bias for predicting the mean regression function. When used together, they give a complete summary of the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <arXiv:1611.03063> for more details.

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