Some accelerated three-term conjugate gradient algorithms implemented purely in R with the same user interface as optim(). The search directions and acceleration scheme are described in Andrei, N. (2013) <doi:10.1016/j.amc.2012.11.097>, Andrei, N. (2013) <doi:10.1016/j.cam.2012.10.002>, and Andrei, N (2015) <doi:10.1007/s11075-014-9845-9>. Line search is done by a hybrid algorithm incorporating the ideas in Oliveia and Takahashi (2020) <doi:10.1145/3423597> and More and Thuente (1994) <doi:10.1145/192115.192132>.

The 'tinytest' package offers a light-weight zero-dependency unit-testing
 framework to which this package adds support via the 'diffobj' package for 'diff'-style
 textual comparison of R objects, as well as via 'tinysnapshot' package for visual
 differences in plots.

Design and analysis tools for target trial emulation using longitudinal observational data. Functions are provided for checking person-period data, expanding longitudinal data into sequentially nested trials, estimating inverse probability weights for intention-to-treat and per-protocol analyses, and assessing weight distributions and covariate balance. Additional functions fit weighted pooled discrete-time outcome models, obtain standardized risks and treatment contrasts, and estimate weighted Kaplan-Meier and Aalen-Johansen curves. Two worked examples based on fully synthetic data illustrate an active-comparator new-user study comparing sodium-glucose cotransporter 2 inhibitors with dipeptidyl peptidase-4 inhibitors and an analysis of sequentially nested trials comparing angiotensin receptor blocker and calcium channel blocker strategies.

Analysis of treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events. This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercurrent events (ICE) under the five strategies outlined in the ICH E9 (R1) addendum, see Deng (2025) <doi:10.1002/sim.70091>. This package can be used for analyzing data from both randomized controlled trials and observational studies. In general, the data involve a primary outcome event and, potentially, an intercurrent event. Two data structures are allowed: competing risks, where only the time to the first event is recorded, and semicompeting risks, where the times to both the primary outcome event and intercurrent event (or censoring) are recorded. For estimation methods, users can choose nonparametric estimation (which does not use covariates) and semiparametrically efficient estimation.