Flexible and comprehensive functions for statistical power, minimum required sample size, and minimum detectable effect calculations across a wide range of commonly used hypothesis tests in psychological, biomedical, and social sciences.

This package provides a transparent and modular implementation of Partial Least Squares Structural Equation Modeling (PLS-SEM) focused on reflective measurement models (Mode A). The package separates estimation, bootstrap inference, and predictive evaluation into independent components, emphasising algorithmic transparency, reproducibility, and researcher-controlled analysis. Methods are based on Tenenhaus, Esposito Vinzi, Chatelin & Lauro (2005) <doi:10.1016/j.csda.2004.03.005>, Hair, Risher, Sarstedt & Ringle (2019) <doi:10.1108/EBR-11-2018-0203>, and Henseler, Ringle & Sarstedt (2015) <doi:10.1007/s11747-014-0403-8>.

This package provides functions for estimation and data generation for several piecewise lifetime distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) <doi:10.2307/2528247> for methodological details.

Interactive shiny application for working with Probability Distributions. Calculations and Graphs are provided.

Generates a position balanced or nearly position balanced block design with given parameters. This package can also convert a given proper and equireplicate block design into a position balanced or nearly position balanced block design.

In short, this package is a locator for cool, refreshing beverages. It will find and return the nearest location where you can get a cold one.

Collection of functions to get files in parquet format. Parquet is a columnar storage file format <https://parquet.apache.org/>. The files to convert can be of several formats ("csv", "RData", "rds", "RSQLite", "json", "ndjson", "SAS", "SPSS"...).

Jointly segment several ChIP-seq samples to find the peaks which are the same and different across samples. The fast approximate maximum Poisson likelihood algorithm is described in "PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples" <doi:10.48550/arXiv.1506.01286> by TD Hocking and G Bourque.

Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the lavaan', 2)a sample of sequential path analysis in metan suggested by Olivoto and Lúcio (2020) <doi:10.1111/2041-210X.13384>, 3)the simple PATHSAS macro written in SAS by Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the semPlot() function of OpenMx as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a Minitab macro developed by Arminian, A. in the paper by Arminian et al. (2008) <doi:10.1080/15427520802043182>.

This package provides functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.

Format and submit few-shot prompts to OpenAI's Large Language Models (LLMs). Designed to be particularly useful for text classification problems in the social sciences. Methods are described in Ornstein, Blasingame, and Truscott (2024) <https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf>.

An implementation of a formal grammar and parser for R Markdown documents using the Boost Spirit X3 library. It also includes a collection of high level functions for working with the resulting abstract syntax tree.

This program contains a function to find the peaks and troughs of a data set. It filters the set of peaks to remove noise based on the expected height and expected slope of a peak. Peaks that are too short (caused by random noise), or too shallow (part of the background data) are filtered out.

This package provides functions to calculate commonly used public health statistics and their confidence intervals using methods approved for use in the production of Public Health England indicators such as those presented via Fingertips (<https://fingertips.phe.org.uk/>). It provides functions for the generation of proportions, crude rates, means, directly standardised rates, indirectly standardised rates, standardised mortality ratios, slope and relative index of inequality and life expectancy. Statistical methods are referenced in the following publications. Breslow NE, Day NE (1987) <doi:10.1002/sim.4780080614>. Dobson et al (1991) <doi:10.1002/sim.4780100317>. Armitage P, Berry G (2002) <doi:10.1002/9780470773666>. Wilson EB. (1927) <doi:10.1080/01621459.1927.10502953>. Altman DG et al (2000, ISBN: 978-0-727-91375-3). Chiang CL. (1968, ISBN: 978-0-882-75200-6). Newell C. (1994, ISBN: 978-0-898-62451-9). Eayres DP, Williams ES (2004) <doi:10.1136/jech.2003.009654>. Silcocks PBS et al (2001) <doi:10.1136/jech.55.1.38>. Low and Low (2004) <doi:10.1093/pubmed/fdh175>. Fingertips Public Health Technical Guide: <https://fingertips.phe.org.uk/profile/guidance/supporting-information/PH-methods/>.

Psychometric mixture models based on flexmix infrastructure. At the moment Rasch mixture models with different parameterizations of the score distribution (saturated vs. mean/variance specification), Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated with or without concomitant variables. See Frick et al. (2012) <doi:10.18637/jss.v048.i07> and Frick et al. (2015) <doi:10.1177/0013164414536183> for details on the Rasch mixture models.

This package performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.

This package provides a function to estimate panel-corrected standard errors. Data may contain balanced or unbalanced panels.

This package provides a cohesive framework for the spectral and spatial analysis of colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) <doi:10.1111/2041-210X.12069> and Maia, Gruson, Endler & White (2019) <doi:10.1111/2041-210X.13174>.

This package provides robust functions for proportional allocation of numeric values. It guarantees sum consistency after rounding or integer truncation using one of two adjustment methods: the largest remainder method or max-value adjustment. Handles edge cases like NA weights and vector total values seamlessly.

This package provides a toolbox for writing knitr', Sweave or other LaTeX'- or markdown'-based reports and to prettify the output of various estimated models.

Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.

Generates Plus Code of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by Google Inc. present in the repository <https://github.com/google/open-location-code/blob/main/java/src/main/java/com/google/openlocationcode/OpenLocationCode.java>. For details about Plus Code', visit <https://maps.google.com/pluscodes/> or <https://github.com/google/open-location-code>.

Proteins reside in either the cell plasma or in the cell membrane. A membrane protein goes through the membrane at least once. Given the amino acid sequence of a membrane protein, the tool PureseqTM (<https://github.com/PureseqTM/pureseqTM_package>, as described in "Efficient And Accurate Prediction Of Transmembrane Topology From Amino acid sequence only.", Wang, Qing, et al (2019), <doi:10.1101/627307>), can predict the topology of a membrane protein. This package allows one to use PureseqTM from R.

Providing functions to diagnose and make inferences from various linear models, such as those obtained from aov', lm', glm', gls', lme', lmer', glmmTMB and semireg'. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests, adjusted R-square and graphs.