Consists of custom wrapper functions using packages openxlsx', flextable', and officer to create highly formatted MS office friendly output of your data frames. These viewer friendly outputs are intended to match expectations of professional looking presentations in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data frame to have certain properties in order to take advantage of the automated formatting.

This package provides a lightweight message system relying purely on base R. Comes with built-in and pre styled message types and provides an easy way to create custom messages. Supports individually styled and colored text as well as timing information. Designed to make console output more informative and visually organized.

Fit linear splines to species time series to detect population growth scenarios based on Hyndman, R J and Mesgaran, M B and Cousens, R D (2015) <doi:10.1007/s10530-015-0962-8>.

Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.

Pivot easily by specifying rows, columns, values and split.

This package implements a set of methodological tools that enable researchers to apply matching methods to time-series cross-sectional data. Imai, Kim, and Wang (2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results.

Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".

This package implements permutation tests for any test statistic and randomization scheme and constructs associated confidence intervals as described in Glazer and Stark (2024) <doi:10.48550/arXiv.2405.05238>.

Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. pathfindR is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. pathfindR also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in pathfindR are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.

This package implements (1) panel cointegration rank tests, (2) estimators for panel vector autoregressive (VAR) models, and (3) identification methods for panel structural vector autoregressive (SVAR) models as described in the accompanying vignette. The implemented functions allow to account for cross-sectional dependence and for structural breaks in the deterministic terms of the VAR processes. Among the large set of functions, particularly noteworthy are those that implement (1) the correlation-augmented inverse normal test on the cointegration rank by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>), (2) the two-step estimator for pooled cointegrating vectors by Breitung (2005, <doi:10.1081/ETC-200067895>), and (3) the pooled identification based on independent component analysis by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).

Manipulation and analysis of phylogenetically simulated data sets and phylogenetically based analyses using GLS.

Generates Weibull-parameterized estimates of phenology for any percentile of a distribution using the framework established in Cooke (1979) <doi:10.1093/biomet/66.2.367>. Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020) <doi:10.1111/2041-210X.13448>. Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest.

Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The Phenotype can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.

Design, backtest, and analyze portfolio strategies using simple, English-like function chains. Includes technical indicators, flexible stock selection, portfolio construction methods (equal weighting, signal weighting, inverse volatility, hierarchical risk parity), and a compact backtesting engine for portfolio returns, drawdowns, and summary metrics.

This package provides a shiny application for visualizing high-dimensional data using non-linear dimensionality reduction (NLDR) techniques such as t-SNE and UMAP. It provides an interactive platform to explore high-dimensional datasets, diagnose the quality of the embeddings using the quollr package, and compare different NLDR methods.

Quickly and easily add a mini map to your rmarkdown html documents.

Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`.

Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.

This package implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

An implementation of the Partition Of variation (POV) method as developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in 1993 for the analysis of semiconductor data for hard drive manufacturing. POV is based on sequential sum of squares and is an exact method that explains all observed variation. It quantitates both the between and within factor variation effects and can quantitate the influence of both continuous and categorical factors.

This package provides tools for examining Rprof profile output.

This package provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 <doi:10.4135/9781412993869.d33>; Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian estimation is (optionally) supported via Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized linear model extension thereof using GEE.

Mixtures of Poisson Generalized Linear Models for high dimensional count data clustering. The (multivariate) responses can be partitioned into set of blocks. Three different parameterizations of the linear predictor are considered. The models are estimated according to the EM algorithm with an efficient initialization scheme <doi:10.1016/j.csda.2014.07.005>.

Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.