Allows users to seamlessly query several CDC PLACES APIs (<https://data.cdc.gov/browse?q=PLACES%20&sortBy=relevance>) by geography, state, measure, and release year. This package also contains a function to explore the available measures for each release year.

This package implements controlled interrupted time series (CITS) analysis for evaluating interventions in comparative time-series data. The package provides tools for preparing panel time-series datasets, fitting models using generalized least squares (GLS) with optional autoregressiveâ moving-average (ARMA) error structures, and computing fitted values and robust standard errors using cluster-robust variance estimators (CR2). Visualization functions enable clear presentation of estimated effects and counterfactual trajectories following interventions. Background on methods for causal inference in interrupted time series can be found in Linden and Adams (2011) <doi:10.1111/j.1365-2753.2010.01504.x> and Lopez Bernal, Cummins, and Gasparrini (2018) <doi:10.1093/ije/dyy135>.

This package provides access to the Calcite Design System javascript components via integration with the htmltools and shiny packages. Pre-built and interactive components can be used to generate either static html or interactive web applications. Learn more about the Calcite Design System at <https://developers.arcgis.com/calcite-design-system/>.

Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 <DOI:10.1111/j.1438-8677.2012.00590.x>), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Model for apple "Gala" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Taiwan Model (Lu et al., 2012 <DOI:10.17660/ActaHortic.2012.962.35>), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the chillR package (Luedeling, 2018), Unified Model (Chuine et al., 2016 <DOI:10.1111/gcb.13383>) and Heat Restriction model.

The concaveman function ports the concaveman (<https://github.com/mapbox/concaveman>) library from mapbox'. It computes the concave polygon(s) for one or several set of points.

Allow to run Cppcheck (<https://cppcheck.sourceforge.io/>) on C and C++ files with a R command or a RStudio addin. The report appears in the RStudio viewer pane as a formatted HTML file. It is also possible to get this report with a shiny application. Cppcheck can spot many error types and it can also give some recommendations on the code.

This package provides functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.

Based on â Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysisâ (M. Even and J. Josse, 2025), this package provides implementations of three approaches - nearest neighbor matching, distributional regression forests, and efficient influence functions - to estimate the causal win ratio, win proportion, and net benefit.

Tree-based classification and soft-clustering method for preference rankings, with tools for external validation of fuzzy clustering, and Kemeny-equivalent augmented unfolding. It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). The package depends on the ConsRank R package. Options for validate the tree-based method are both test-set procedure and V-fold cross validation. The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index). The package also contains routines to perform the Kemeny-equivalent augmented unfolding. The mds endine is the function sacofSym from the package smacof'. Essential references: D'Ambrosio, A., Vera, J.F., and Heiser, W.J. (2021) <doi:10.1080/00273171.2021.1899892>; D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) <doi:10.1007/s00357-020-09367-0>; D'Ambrosio, A., and Heiser, W.J. (2019) <doi:10.1007/s41237-018-0069-5>; D'Ambrosio, A., and Heiser W.J. (2016) <doi:10.1007/s11336-016-9505-1>; Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) <doi:10.1109/TFUZZ.2011.2179303>; Marden, J.J. <ISBN:0412995212>.

Gain access to the Spark Catalog API making use of the sparklyr API. Catalog <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).

This package provides tools for detecting cellwise outliers and robust methods to analyze data which may contain them. Contains the implementation of the algorithms described in Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access) Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access), Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access), Raymaekers and Rousseeuw (2022) <doi:10.1080/01621459.2023.2267777> (open access) Rousseeuw (2022) <doi:10.1016/j.ecosta.2023.01.007> (open access). Examples can be found in the vignettes: "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples", "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples", and "cellwise_weights_examples".

Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.

Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) <https://proceedings.mlr.press/v204/tyagi23a.html>.

Wraps the CIRCE (<https://github.com/ohdsi/circe-be>) Java library allowing cohort definition expressions to be edited and converted to Markdown or SQL'.

This package provides tools for interpretable conditional forecasting and scenario analysis in reduced-form vector autoregressive (VAR) models. Implements a Kalman smoothing framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions, and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications. The methodology is described in more detail in Caspi and Ginker (2026) <doi:10.13140/RG.2.2.25225.51040>.

Fits emulators, also known as surrogates or response surfaces, using conformal inference with random forests. The conformal calibration is performed using out-of-bag samples from the forest, eliminating the need for a separate hold-out set. The method is based on Johansson et al. (2014 <doi:10.1007/s10994-014-5453-0>).

Accelerate Bayesian analytics workflows in R through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the numpyro python package.

Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.

Customized training is a simple technique for transductive learning, when the test covariates are known at the time of training. The method identifies a subset of the training set to serve as the training set for each of a few identified subsets in the training set. This package implements customized training for the glmnet() and cv.glmnet() functions.

Uses the CMS application programming interface <https://dnav.cms.gov/api/healthdata> to provide users databases containing yearly Medicare reimbursement rates in the United States. Data can be acquired for the entire United States or only for specific localities. Currently, support is only provided for the Medicare Physician Fee Schedule, but support will be expanded for other CMS databases in future versions.

This package implements functions for comparing strings, sequences and numeric vectors for clustering and record linkage applications. Supported comparison functions include: generalized edit distances for comparing sequences/strings, Monge-Elkan similarity for fuzzy comparison of token sets, and L-p distances for comparing numeric vectors. Where possible, comparison functions are implemented in C/C++ to ensure good performance.

This package provides tools for evaluating link prediction and clustering algorithms with respect to ground truth. Includes efficient implementations of common performance measures such as pairwise precision/recall, cluster homogeneity/completeness, variation of information, Rand index etc.

Constructs co-occurrence networks from several types of input data, such as delimited fields, long/bipartite tables, binary matrices, or wide sequences. Returns tidy edge data frames and supports optional scaling, splitting into several networks, thresholding, and subsetting. Provides eight similarity measures, including Jaccard, cosine, and association strength. Supports export to several network and file formats. Network construction and analysis methods follow Saqr, Lopez-Pernas, Conde, and Hernandez-Garcia (2024, <doi:10.1007/978-3-031-54464-4_15>).

Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data.