* Fast Estimators for Design-Based Inference* (

**estimatr**)

Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>, difference-in-means, and Horvitz-Thompson estimation.

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**Imputation Regularized Optimization Algorithm****IROmiss**)

Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at <https://…/ica10.pdf> for detail. The publication ‘An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond’ will be appear on Journal of the Royal Statistical Society Series B soon.

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**Translation of Base R-Like Functions for ‘data.table’ Objects****R2DT**)

Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.

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**Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended****PMCMRplus**)

For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons tests (Tukey-Kramer test, Scheffe test, LSD-test) and many-to-one tests (Dunnett test) for normally distributed residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane’s T2 test, Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals and heterogeneous variances are provided. Van der Waerden’s normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks. Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included. In addition, a Friedman-test for one-way ANOVA with repeated measures on ranks (CRBD) and Skillings-Mack test for unbalanced CRBD is provided with consequent all-pairs tests (Nemenyi test, Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test). A trend can be tested with Pages’s test. Durbin’s test for a two-way balanced incomplete block design (BIBD) is given in this package as well as Gore’s test for CRBD with multiple observations per cell is given. Outlier tests, Mandel’s k- and h statistic as well as functions for Type I error and Power analysis as well as generic summary, print and plot methods are provided.

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**Time Series Analysis ‘OpenBudgets’****TimeSeries.OBeu**)

Estimate and return the needed parameters for visualisations designed for ‘OpenBudgets’ <http://…/> time series data. Calculate time series model and forecast parameters in Budget time series data of municipalities across Europe, according to the ‘OpenBudgets’ data model. There are functions for measuring deterministic and stochastic trend of the input time series data, decomposing with local regression models or seasonal trend decomposition, modelling the appropriate auto regressive integrated moving average model and provide forecasts for the input ‘OpenBudgets’ time series fiscal data. Also, can be used generally to extract visualisation parameters convert them to ‘JSON’ format and use them as input in a different graphical interface.