A ‘DBI’ Interface to the ‘Yandex Clickhouse’ Database Providing Basic ‘dplyr’ Support (RClickhouse)
Yandex Clickhouse’ (<https://…/> ) is a high-performance relational column-store database to enable big data exploration and ‘analytics’ scaling to petabytes of data. Methods are provided that enable working with ‘Yandex Clickhouse’ databases via ‘DBI’ ‘methods’ and using ‘dplyr’/’dbplyr’ idioms.

Alluvial Diagrams in ‘ggplot2’ (ggalluvial)
Alluvial diagrams encompass a variety of charts that use x-splines (alluvia and flows), sometimes augmented with stacked bars (lodes or strata), to visualize incidence structures derived from several data types, including repeated categorical measures, evolving classifications, and multi-dimensional categorical data. This package contains stat and geom layers that interpret multiple data formats compatible with this framework while hewing to the principles of tidy data and the grammar of graphics.

Tipping Point Analyses (tipr)
The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, a plausible residual effect size for an unmeasured continuous or binary confounder, and a realistic mean difference or prevalence difference for this hypothetical confounder. Building on the methods put forth by Lin, Psaty, & Kronmal (1998) <doi:10.2307/2533848>, we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance, rendering the study inconclusive.

(Precipitation) Frequency Analysis and Variability with L-Moments from ‘lmom’ (lmomPi)
It is an extension of ‘lmom’ R package: ‘pel’,’cdf’,qua’ function families are lumped and called from one function per each family respectively in order to create robust automatic tools to fit data with different probability distributions and then to estimate probability values and return periods. The implemented functions are able to manage time series with constant and/or missing values without stopping the execution with error messages. The package also contains tools to calculate several indices based on variability (e.g. ‘SPI’ , Standardized Precipitation Index, see <https://…/standardized-precipitation-index-spi> and <http://…/> ) for multiple time series or spatio-temporal gridded values.

Efficient Design and Analysis of Cluster Randomized Trials (cvcrand)
Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline values of cluster-level covariates and cluster permutation test on the individual-level outcome for cluster randomized trials.

Tidy Data Validation Reports (ruler)
Tools for creating data validation pipelines and tidy reports. This package offers a framework for exploring and validating data frame like objects using ‘dplyr’ grammar of data manipulation.