Read and write GraphPad Prism .pzfx files in R.

Screens and sorts phylogenetic trees in both traditional and extended Newick format. Allows for the fast and flexible screening (within a tree) of Exclusive clades that comprise only the target taxa and/or Non- Exclusive clades that includes a defined portion of non-target taxa.

Identifies potential target sequences for a given set of primers and generates phylogenetic trees annotated with the taxonomies of the predicted amplification products.

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.

Allow to run pylint on Python files with a R command or a RStudio addin. The report appears in the RStudio viewer pane as a formatted HTML file.

This package implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.

This package provides tools for analyzing data generated from conjoint survey experiments, a method widely used in the social sciences for studying multidimensional preferences. The package implements estimation of marginal means (MMs) and average marginal component effects (AMCEs), with corrections for measurement error. Methods include profile-level and choice-level estimators, bias correction using intra-respondent reliability (IRR), and visualization utilities. For details on the methodology, see Clayton, Horiuchi, Kaufman, King, and Komisarchik (2025) <https://gking.harvard.edu/conjointE>.

This package provides a quadratic time dynamic programming algorithm can be used to compute an approximate solution to the problem of finding the most likely changepoints with respect to the Poisson likelihood, subject to a constraint on the number of segments, and the changes which must alternate: up, down, up, down, etc. For more info read <http://proceedings.mlr.press/v37/hocking15.html> "PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data" by TD Hocking et al, proceedings of ICML2015.

This package provides a collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate.

Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.

Extract and interact with data from the Scottish Health and Social Care Open Data platform <https://www.opendata.nhs.scot>.

Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.

Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast. Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible. The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the European Centre for Medium-Range Weather Forecasts (ECMWF, <https://www.ecmwf.int/>) and GDP growth Greenbook data by the US Federal Reserve. See Schmidt, Katzfuss and Gneiting (2015) <arXiv:1506.01917> for more details on the identification and estimation of a directive behind a point forecast.

Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.

This package implements a Bayesian profile regression using a generalized linear mixed model as output model. The package allows for binary (probit mixed model) and continuous (linear mixed model) outcomes and both continuous and categorical clustering variables. The package utilizes RcppArmadillo and RcppDist for high-performance statistical computing in C++. For more details see Amestoy & al. (2025) <doi:10.48550/arXiv.2510.08304>.

Genotyping arrays enable the direct measurement of an individuals genotype at thousands of markers. plinkQC facilitates genotype quality control for genetic association studies as described by Anderson and colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes PLINK basic statistics (e.g. missing genotyping rates per individual, allele frequencies per genetic marker) and relationship functions accessible from R and generates a per-individual and per-marker quality control report. Individuals and markers that fail the quality control can subsequently be removed to generate a new, clean dataset. Removal of individuals based on relationship status is optimised to retain as many individuals as possible in the study. Additionally, there is a trained classifier to predict genomic ancestry of human samples.

Identification, model fitting and estimation for time series with periodic structure. Additionally, procedures for simulation of periodic processes and real data sets are included. Hurd, H. L., Miamee, A. G. (2007) <doi:10.1002/9780470182833> Box, G. E. P., Jenkins, G. M., Reinsel, G. (1994) <doi:10.1111/jtsa.12194> Brockwell, P. J., Davis, R. A. (1991, ISBN:978-1-4419-0319-8) Bretz, F., Hothorn, T., Westfall, P. (2010, ISBN: 9780429139543) Westfall, P. H., Young, S. S. (1993, ISBN:978-0-471-55761-6) Bloomfield, P., Hurd, H. L.,Lund, R. (1994) <doi:10.1111/j.1467-9892.1994.tb00181.x> Dehay, D., Hurd, H. L. (1994, ISBN:0-7803-1023-3) Vecchia, A. (1985) <doi:10.1080/00401706.1985.10488076> Vecchia, A. (1985) <doi:10.1111/j.1752-1688.1985.tb00167.x> Jones, R., Brelsford, W. (1967) <doi:10.1093/biomet/54.3-4.403> Makagon, A. (1999) <https://www.math.uni.wroc.pl/~pms/files/19.2/Article/19.2.5.pdf> Sakai, H. (1989) <doi:10.1111/j.1467-9892.1991.tb00069.x> Gladyshev, E. G. (1961) <https://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=dan&paperid=24851> Ansley (1979) <doi:10.1093/biomet/66.1.59> Hurd, H. L., Gerr, N. L. (1991) <doi:10.1111/j.1467-9892.1991.tb00088.x>.

This package provides a framework for defining pipelines of functions for applying data transformations, model estimation and inverse-transformations, resulting in predicted value generation (or model-scoring) functions that automatically apply the entire pipeline of functions required to go from input to predicted output.

Functional claims reserving methods based on aggregated chain-ladder data, also known as a run-off triangle, implemented in three nonparametric algorithms (PARALLAX, REACT, and MACRAME) proposed in Maciak, Mizera, and Pešta (2022) <doi:10.1017/asb.2022.4>. Additional methods including permutation bootstrap for completed run-off triangles are also provided.

Determine minimal protein set explaining peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants. Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions.

This package provides a framework for creating interactive figures for data exploration. All plots are automatically linked and support several kinds of interactive features, including selection, zooming, panning, and parameter manipulation. The figures can be interacted with either manually, using a mouse and a keyboard, or by running code from inside an active R session.

Plot marginal effects for interactions estimated from linear models.

This package provides path_chain class and functions, which facilitates loading and saving directory structure in YAML configuration files via config package. The file structure you created during exploration can be transformed into legible section in the config file, and then easily loaded for further usage.

Build piecewise exponential survival model for study design (planning) and event/timeline prediction.