Open mpadge opened 2 years ago
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Thanks, about to send the query.
:rocket:
Error: Issue template has no 'repourl'
:wave:
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Hello @mpadge, here are the things you can ask me to do:
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:heavy_check_mark: This package complies with > 50% of all standads and may be submitted.
Note: The following R packages were unable to be installed/upgraded on our system: [viridisLite, StanHeaders, rstan]; some checks may be unreliable.
:heavy_check_mark: This package complies with > 50% of all standads and may be submitted.
git hash: a8d932ca
+.dynamiteformula
, print.dynamiteformula, get_priors.dynamiteformula, get_priors.dynamitefit, get_code.dynamiteformula, get_code.dynamitefit, get_data.dynamiteformula, get_data.dynamitefit, fitted.dynamitefit, mcmc_diagnostics.dynamitefit, plot_nus, predict.dynamitefit, print.dynamitefit, summary.dynamitefit]Important: All failing checks above must be addressed prior to proceeding
Package License: GPL (>= 3)
srr
package)This package is in the following category:
:heavy_check_mark: All applicable standards [v0.1.0] have been documented in this package (129 complied with; 42 N/A standards)
Click to see the report of author-reported standards compliance of the package with links to associated lines of code, which can be re-generated locally by running the srr_report()
function from within a local clone of the repository.
The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.
|type |package | ncalls|
|:----------|:----------|------:|
|internal |base | 1102|
|internal |dynamite | 418|
|internal |graphics | 21|
|internal |methods | 3|
|imports |utils | 81|
|imports |stats | 59|
|imports |dplyr | 29|
|imports |rlang | 16|
|imports |checkmate | 11|
|imports |glue | 11|
|imports |cli | 6|
|imports |ggplot2 | 4|
|imports |posterior | 4|
|imports |tidyr | 4|
|imports |rstan | 3|
|imports |data.table | 2|
|imports |bayesplot | 1|
|imports |MASS | NA|
|suggests |covr | NA|
|suggests |knitr | NA|
|suggests |plm | NA|
|suggests |rmarkdown | NA|
|suggests |testthat | NA|
|linking_to |NA | NA|
Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(
c (108), list (96), length (69), paste0 (66), args (53), for (47), attr (44), data.frame (39), as.list (32), match.call (32), rep (31), seq_len (25), unique (25), do.call (22), character (19), which (18), vapply (17), is.na (16), names (16), logical (15), seq_along (15), drop (13), by (12), deparse1 (11), is.null (10), mean (10), nzchar (10), all (9), apply (9), debug (9), lapply (9), parent.frame (9), as.integer (7), integer (7), rank (7), seq.int (7), sort (6), unlist (6), array (5), assign (5), colnames (5), dim (5), log (5), message (5), mode (5), nrow (5), try (5), vector (5), as.numeric (4), call (4), diff (4), eval (4), gsub (4), as.data.frame (3), cbind (3), I (3), identical (3), setdiff (3), sub (3), sum (3), any (2), aperm (2), expand.grid (2), levels (2), max (2), ncol (2), seq (2), structure (2), suppressWarnings (2), t (2), beta (1), class (1), det (1), diag (1), duplicated (1), get (1), gl (1), gregexec (1), ifelse (1), intersect (1), is.factor (1), is.finite (1), match (1), min (1), new.env (1), numeric (1), parse (1), paste (1), prod (1), range (1), regmatches (1), replace (1), replicate (1), row.names (1), sample (1), sample.int (1), signif (1), strsplit (1), substitute (1), typeof (1), union (1), warning (1), which.max (1), which.min (1), with (1)
ifelse_ (84), paste_rows (41), get_responses (17), data_lines_default (10), get_predictors (10), onlyif (10), model_lines_default (9), warning_ (9), prepare_channel_default (8), formula_rhs (6), get_quoted (5), as.data.frame.dynamitefit (4), get_families (4), has_past (4), coef.dynamitefit (3), evaluate_specials (3), get_formulas (3), assign_deterministic (2), complete_lags (2), create_blocks (2), cs (2), default_priors (2), default_priors_categorical (2), deterministic_response (2), extract_lags (2), extract_nonlags (2), find_lags (2), formula_lhs (2), formula_past (2), formula_terms (2), full_model.matrix (2), full_model.matrix_predict (2), get_originals (2), get_terms (2), indenter_ (2), join_dynamiteformulas (2), lag_ (2), parse_global_lags (2), parse_lags (2), parse_new_lags (2), parse_singleton_lags (2), prepare_eval_envs (2), prepare_lagged_response (2), stop_ (2), which_deterministic (2), which_stochastic (2), abort_factor (1), abort_negative (1), abort_nonunit (1), add_dynamiteformula (1), as_data_frame_alpha (1), as_data_frame_beta (1), as_data_frame_corr_nu (1), as_data_frame_default (1), as_data_frame_delta (1), as_data_frame_lambda (1), as_data_frame_nu (1), as_data_frame_omega (1), as_data_frame_omega_alpha (1), as_data_frame_phi (1), as_data_frame_sigma (1), as_data_frame_sigma_nu (1), as_data_frame_tau (1), as_data_frame_tau_alpha (1), as_draws_df.dynamitefit (1), as_draws.dynamitefit (1), assign_initial_values (1), assign_lags (1), assign_lags_init (1), aux (1), check_ndraws (1), check_newdata (1), check_priors (1), clear_nonfixed (1), confint.dynamitefit (1), create_blocks.default (1), create_data (1), create_functions (1), create_generated_quantities (1), create_model (1), create_parameters (1), create_transformed_data (1), create_transformed_parameters (1), data_lines_bernoulli (1), data_lines_beta (1), data_lines_binomial (1), data_lines_categorical (1), data_lines_exponential (1), data_lines_gamma (1), data_lines_gaussian (1), data_lines_negbin (1), data_lines_poisson (1), drop_terms (1), drop_unused (1), dynamite (1), dynamitechannel (1), dynamitefamily (1), dynamiteformula (1), dynamiteformula_ (1), evaluate_deterministic (1), fill_time (1), fill_time_predict (1), fitted.dynamitefit (1), formula_specials (1), formula.dynamitefit (1), generate_random_intercept (1), generate_sim_call (1), get_code (1), get_code.dynamitefit (1), get_code.dynamiteformula (1), get_data (1), get_data.dynamitefit (1), get_data.dynamiteformula (1), get_priors (1), get_priors.dynamitefit (1), get_priors.dynamiteformula (1), get_special_term_indices (1), impute_newdata (1), increment_formula (1), initialize_deterministic (1), is_supported (1), is.dynamitefamily (1), is.dynamitefit (1), is.dynamiteformula (1), lags (1), lines_wrap (1), locf (1), mcmc_diagnostics (1), mcmc_diagnostics.dynamitefit (1), message_ (1), model_lines_bernoulli (1), model_lines_beta (1), model_lines_binomial (1), model_lines_categorical (1), model_lines_exponential (1), model_lines_gamma (1), model_lines_gaussian (1), model_lines_negbin (1), model_lines_poisson (1), ndraws.dynamitefit (1), nobs.dynamitefit (1), parameters_lines_bernoulli (1), parameters_lines_beta (1), parameters_lines_binomial (1), parameters_lines_categorical (1), parameters_lines_default (1), parameters_lines_exponential (1), parameters_lines_gamma (1), parameters_lines_gaussian (1), parameters_lines_negbin (1), parameters_lines_poisson (1), parse_data (1), parse_newdata (1), parse_past (1), parse_present_lags (1), plot_betas (1), plot_deltas (1), plot_nus (1), plot.dynamitefit (1), predict_dynamitefit (1), predict.dynamitefit (1), prepare_channel_bernoulli (1), prepare_channel_beta (1), prepare_channel_binomial (1), prepare_channel_categorical (1), prepare_channel_exponential (1), prepare_channel_gamma (1), prepare_channel_gaussian (1), prepare_channel_negbin (1), prepare_channel_poisson (1), prepare_common_priors (1), prepare_prior (1), prepare_splines (1), prepare_stan_input (1), values (1), verify_lag (1)
data (79), capture.output (1), combn (1)
formula (21), var (7), df (5), sd (4), D (3), model.matrix.lm (3), na.action (3), na.pass (3), offset (3), complete.cases (2), setNames (2), terms (2), sigma (1)
bind_rows (12), filter (7), mutate (3), summarise (3), left_join (2), matches (1), n (1)
mtext (10), title (9), pairs (2)
caller_env (16)
test_character (3), test_flag (3), test_string (3), test_int (2)
glue (11)
cli_abort (2), qty (2), cli_inform (1), cli_warn (1)
labs (3), position_dodge (1)
summarise_draws (2), as_draws (1), ndraws (1)
expand_grid (2), full_seq (1), unnest (1)
is (2), new (1)
extract (2), check_hmc_diagnostics (1)
setDT (1), setkeyv (1)
mcmc_combo (1)
base
dynamite
utils
stats
dplyr
graphics
rlang
checkmate
glue
cli
ggplot2
posterior
tidyr
methods
rstan
data.table
bayesplot
This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.
The package has: - code in R (100% in 30 files) and - 2 authors - 1 vignette - 6 internal data files - 14 imported packages - 36 exported functions (median 7 lines of code) - 404 non-exported functions in R (median 9 lines of code) --- Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages The following terminology is used: - `loc` = "Lines of Code" - `fn` = "function" - `exp`/`not_exp` = exported / not exported All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by [the `checks_to_markdown()` function](https://docs.ropensci.org/pkgcheck/reference/checks_to_markdown.html) The final measure (`fn_call_network_size`) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile. |measure | value| percentile|noteworthy | |:------------------------|-------:|----------:|:----------| |files_R | 30| 89.3| | |files_vignettes | 2| 85.7| | |files_tests | 11| 91.7| | |loc_R | 5956| 96.5|TRUE | |loc_vignettes | 877| 89.0| | |loc_tests | 2413| 95.3|TRUE | |num_vignettes | 1| 64.8| | |data_size_total | 2661885| 98.5|TRUE | |data_size_median | 349085| 96.0|TRUE | |n_fns_r | 440| 96.6|TRUE | |n_fns_r_exported | 36| 82.0| | |n_fns_r_not_exported | 404| 97.8|TRUE | |n_fns_per_file_r | 8| 83.4| | |num_params_per_fn | 2| 11.9| | |loc_per_fn_r | 9| 24.3| | |loc_per_fn_r_exp | 7| 13.5| | |loc_per_fn_r_not_exp | 9| 27.1| | |rel_whitespace_R | 4| 76.1| | |rel_whitespace_vignettes | 13| 68.1| | |rel_whitespace_tests | 9| 86.4| | |doclines_per_fn_exp | 37| 45.3| | |doclines_per_fn_not_exp | 0| 0.0|TRUE | |fn_call_network_size | 696| 96.9|TRUE | ---
Click to see the interactive network visualisation of calls between objects in package
goodpractice
and other checks#### 3a. Continuous Integration Badges [![R-CMD-check](https://github.com/santikka/dynamite/workflows/R-CMD-check/badge.svg)](https://github.com/santikka/dynamite/actions) **GitHub Workflow Results** | id|name |conclusion |sha | run_number|date | |----------:|:-------------|:----------|:------|----------:|:----------| | 2903919221|R-CMD-check |success |a8d932 | 295|2022-08-22 | | 2903919220|test-coverage |success |a8d932 | 295|2022-08-22 | --- #### 3b. `goodpractice` results #### `R CMD check` with [rcmdcheck](https://r-lib.github.io/rcmdcheck/) R CMD check generated the following note: 1. checking installed package size ... NOTE installed size is 11.1Mb sub-directories of 1Mb or more: data 7.2Mb doc 1.0Mb R 2.5Mb R CMD check generated the following check_fail: 1. rcmdcheck_reasonable_installed_size #### Test coverage with [covr](https://covr.r-lib.org/) Package coverage: 97.82 #### Cyclocomplexity with [cyclocomp](https://github.com/MangoTheCat/cyclocomp) No functions have cyclocomplexity >= 15 #### Static code analyses with [lintr](https://github.com/jimhester/lintr) [lintr](https://github.com/jimhester/lintr) found the following 6 potential issues: message | number of times --- | --- Avoid library() and require() calls in packages | 5 unexpected symbol | 1
:heavy_multiplication_x: The following 10 function names are duplicated in other packages: - - `aux` from seewave - - `get_code` from norgeo, rmonad, xpose - - `get_data` from canvasXpress.data, cbsodataR, cimir, completejourney, CVXR, danstat, deckgl, ecb, finnishgrid, ggPMX, ggvis, hydroscoper, insight, jtools, mapbayr, metacoder, missCompare, optimall, qrmtools, r4googleads, radiant.data, radous, rbedrock, rchallenge, rsimsum, SWIM, swissparl, tidyLPA, tidySEM, trending, tsmp, ugatsdb, xpose - - `get_priors` from CausalQueries, insight - - `lags` from smooth, tis, TTR - - `mcmc_diagnostics` from bpr, rater, rnmamod - - `obs` from metacoder, observer - - `plot_deltas` from spruce - - `random` from CoOL, decisionSupport, distributions3, gam, gamlss, ggdmc, lidR, messydates, simr, sodium - - `splines` from rpatrec
|package |version | |:--------|:---------| |pkgstats |0.1.1.20 | |pkgcheck |0.1.0.9 | |srr |0.0.1.178 |
Processing may not proceed until the items marked with :heavy_multiplication_x: have been resolved.
Submitting Author Name: Eunseop Kim Submitting Author Github Handle: !--author1-->@markean<!--end-author1-- Repository: https://github.com/markean/melt Submission type: Pre-submission Language: en
(Copied from software-review#549 to test bot pre-review commands)
Scope
Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check an appropriate box below):
Data Lifecycle Packages
[ ] data retrieval
[ ] data extraction
[ ] data munging
[ ] data deposition
[ ] data validation and testing
[ ] workflow automation
[ ] version control
[ ] citation management and bibliometrics
[ ] scientific software wrappers
[ ] field and lab reproducibility tools
[ ] database software bindings
[ ] geospatial data
[ ] text analysis
Statistical Packages
[ ] Bayesian and Monte Carlo Routines
[ ] Dimensionality Reduction, Clustering, and Unsupervised Learning
[ ] Machine Learning
[x] Regression and Supervised Learning
[ ] Exploratory Data Analysis (EDA) and Summary Statistics
[ ] Spatial Analyses
[ ] Time Series Analyses
Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of: The package performs hypothesis testing with empirical likelihood for linear models and generalized linear models.
If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package? Yes.
Who is the target audience and what are scientific applications of this package?
Academic statisticians who are interested in empirical likelihood-based inference for (generalized) linear models.
Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category? No, at least in my understanding.
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research? Yes,
Any other questions or issues we should be aware of?: How long does the peer-review process take on average? The plan is to submit the package with a manuscript to the Journal of Statistical Software.