Title: Safe, Multiple, Simultaneous String Substitution
Description: Designed to enable simultaneous substitution in strings in a safe fashion.
Safe means it does not rely on placeholders (which can cause errors in same length matches).
Suggests: covr, testthat, knitr, rmarkdown, qdap, microbenchmark

Title: GWR and MGWR with Spatial Autocorrelation
Author: Ghislain Geniaux and Davide Martinetti
Description: Functions for computing (Mixed) Geographycally Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.
Imports: spgwr, methods, doParallel, foreach, htmltools, nabor

Depends: R (>= 3.5.0), Rcpp, sp, leaflet, Matrix
Imports: ggplot2, sf, knitr, spgwr, methods, doParallel, foreach,
htmltools, nabor, qlcMatrix, mapview, microbenchmark, rlang

Title: Multivariate Survival Function Estimation and Regression
Description: Estimates the survival function and Cox regression parameters
for the multivariate survival setting where there are multiple
(right-censored) outcome variables. The Volterra, Dabrowska, and
Prentice-Cai estimates of the bivariate survival function may be
computed as well as the Dabrowska estimate of the trivariate survival
function. Bivariate Cox regression estimates can also be computed.
Functions are also provided to compute (bootstrap) confidence intervals
and plot the estimates of the bivariate survival function. For details,
see "The Statistical Analysis of Multivariate Failure Time Data: A
Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN:
Imports: Rcpp, boot, plot3D, survival, rootSolve

Title: Nonparametric and Semiparametric Methods for Multivariate
Description: Nonparametric survival function estimates and semiparametric
regression for the multivariate failure time data with right-censoring.
For nonparametric survival function estimates, the Volterra, Dabrowska,
and Prentice-Cai estimates for bivariate failure time data may be
computed as well as the Dabrowska estimate for the trivariate failure
time data. Bivariate marginal hazard rate regression can be fitted for
the bivariate failure time data. Functions are also provided to compute
(bootstrap) confidence intervals and plot the estimates of the bivariate
survival function. For details, see "The Statistical Analysis of
Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice,