ℹ️ About Codex in GitHub

\\n
\\n\\n[Your team has set up Codex to review pull requests in this repo](https://chatgpt.com/codex/cloud/settings/general). Reviews are triggered when you\\n- Open a pull request for review\\n- Mark a draft as ready\\n- Comment \\\"@codex review\\\".\\n\\nIf Codex has suggestions, it will comment; otherwise it will react with 👍.\\n\\n\\n\\n\\nCodex can also answer questions or update the PR. Try commenting \\\"@codex address that feedback\\\".\\n \\n

\",\"path\":null,\"side\":null,\"line\":null}","text_elements",[],{"_441":1007,"_428":429,"_443":431},{"_444":1008,"_446":-5,"_42":-5,"_447":-5,"_448":-5,"_97":301,"_93":450},[1009],{"_46":1003,"_93":1010},"input_text","turn/started",{"_428":429,"_88":1013},{"_104":-5,"_42":431,"_432":1014,"_434":1015},[],"inProgress",{"_434":1017,"_428":429},{"_1018":1019,"_93":1020},"activeFlags",[],"active",{"_441":1022,"_428":429,"_443":1023},{"_444":1024,"_446":-5,"_42":-5,"_447":-5,"_448":-5,"_97":301,"_93":450},"auto-compact-0",[1025,1026],{"_46":1028,"_93":1010},{"_46":1027,"_93":1010},"\n /workspace/fmrireg\n bash\n 2026-04-19\n Etc/UTC\n","# AGENTS.md instructions for /workspace/fmrireg\n\n\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.\n### Available skills\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /opt/codex/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /opt/codex/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.\n- How to use a skill (progressive disclosure):\n 1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.\n 2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.\n 3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.\n 4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n 5) If `assets/` or templates exist, reuse them instead of recreating from scratch.\n- Coordination and sequencing:\n - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.\n- Context hygiene:\n - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.\n - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.\n - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.\n- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.\n",{"_441":1030,"_428":429,"_443":1023},{"_444":1031,"_446":-5,"_42":-5,"_447":-5,"_448":-5,"_97":1032,"_93":450},[1033],"developer",{"_46":1034,"_93":1010},"\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n","thread/started",{"_1037":1038},"thread",{"_1039":-5,"_1040":-5,"_1041":1042,"_1043":1044,"_486":487,"_1045":15,"_1046":-5,"_1047":-5,"_1048":-5,"_1049":1050,"_42":429,"_1051":1052,"_169":-5,"_1053":-5,"_158":1054,"_1055":187,"_1056":-5,"_492":1057,"_434":1058,"_1059":-5,"_78":1060,"_1061":1044},"agentNickname","agentRole","cliVersion","0.114.0","createdAt",1776625045,"ephemeral","extra","forkedFromId","gitInfo","historyMode","legacy","modelProvider","openai","parentThreadId","/opt/codex/sessions/2026/04/19/rollout-2026-04-19T18-57-25-019da71a-d15b-7ac0-bd21-39d5b927e971.jsonl","preview","recencyAt","vscode",{"_93":438},"threadSource",[],"updatedAt",[],"messages",[1065],{"_42":1066,"_1067":1068,"_1069":1070,"_1071":1070,"_444":1072,"_434":1073,"_446":-5,"_1074":32,"_1075":1076,"_1077":1078,"_1079":-5},"4c7ed3d3-be6d-4748-b8b4-8c183e4e21be","author",{"_97":301,"_169":-5,"_1075":1088},"create_time",1776625045.469061,"update_time",{"_1084":46,"_1085":1086},"finished_successfully","weight","metadata",{"_1080":1081},"recipient","all","channel","l1239dk1",{"_1082":1083},"inline_comments",[],"content_type","parts",[1087],"@codex resolve the merge conflicts in this pull request\nFix multiple bugs: division by zero, undefined variable, edge cases\n\n- Fix division by zero in Huber weighting (fmri_robust_fitting.R)\n When residuals are exactly 0, abs(u) would be 0 causing division\n by zero. Added protection using pmax with machine epsilon.\n\n- Fix undefined variable xf in estimate_hrf (fmri_betas.R)\n The variable xf was used in predict() but its definition was\n commented out, causing runtime errors when has_fixed=TRUE.\n Properly handle both has_fixed=TRUE and has_fixed=FALSE cases.\n\n- Fix bootstrap block size edge case (bootstrap.R)\n When nrows < block_size, nblocks becomes 0 and rep(1:0, ...)\n causes unexpected behavior. Now handles this by using a single\n block when nblocks < 1.\n\n- Change silent zero fallback to warning with NA (fmri_betas.R)\n When mixed model solvers fail, returning zeros silently could\n produce misleading results. Now returns NA_real_ with warning\n to make failures visible.\n\n- Remove debug code and commented browser() statements\n@copilot resolve the merge conflicts in this pull request\n**_{_{![P1 Badge](https://img.shields.io/badge/P1-orange?style=flat)}} Preserve tibble shape for single-run contrasts**\n\nReturn `contrasts` in the same tabular structure as the multi-run branch: this line now returns the raw list (`conres_list[[1]]`) instead of a bound tibble, so single-run outputs have a different type than multi-run outputs. Downstream consumers expect `x$result$contrasts` to be a data frame (e.g., `pull_stat()` filters on `type`), so runwise fits with only one run will fail when users request contrast coefficients/statistics.\n\nUseful? React with 👍 / 👎.\n\n### 💡 Codex Review\n\nHere are some automated review suggestions for this pull request.\n\n**Reviewed commit:** `79b52fd10c`\n \n\n

ℹ️ About Codex in GitHub

\n
\n\n[Your team has set up Codex to review pull requests in this repo](https://chatgpt.com/codex/cloud/settings/general). Reviews are triggered when you\n- Open a pull request for review\n- Mark a draft as ready\n- Comment \"@codex review\".\n\nIf Codex has suggestions, it will comment; otherwise it will react with 👍.\n\n\n\n\nCodex can also answer questions or update the PR. Try commenting \"@codex address that feedback\".\n \n

",{},"created",1776625131.7717094,"Completing the task","event_type","info",{"_93":450,"_97":94,"_444":1454},{"_93":1122,"_1123":1124},{"_93":1097,"_1098":1099,"_1100":1101,"_1102":1103,"_1104":-5},"pr","pr_title","Fix single-run contrasts shape in runwise pooling","pr_message","### Motivation\n- Ensure `result$contrasts` has a consistent tabular shape across runwise pooling paths so downstream consumers (e.g., `pull_stat()`) that expect a tibble do not break.\n- Restore the post-merge behavior that was lost when single-run contrasts were returned as a raw list instead of a bound tibble.\n\n### Description\n- Change `pool_runwise_results()` in `R/fmri_lm_runwise.R` so the single-run branch binds per-contrast tibbles with `dplyr::bind_rows()` when contrasts are present. \n- Return an empty tibble (`tibble::tibble()`) when there are no contrasts in the single-run path. \n- Leave all other pooled outputs (`betas`, `sigma`, `rss`, `rdf`, `resvar`, etc.) unchanged.\n\n### Testing\n- Attempted a syntax parse using `Rscript -e \"parse(file='R/fmri_lm_runwise.R'); cat('parse ok\\n')\"`, but it failed because `Rscript` is not available in this environment (test failed due to missing runtime, not due to code syntax).\n- No other automated R unit tests were executed in this environment.","output_diff",{"_93":1102,"_1105":487,"_129":130,"_1106":-5,"_300":1107,"_1108":1109,"_1110":796,"_1111":1112,"_1113":1114,"_1115":1116,"_1117":1118},"pre_apply_patch","repo_id","preapply_patch_base_commit_sha","diff --git a/R/fmri_lm_runwise.R b/R/fmri_lm_runwise.R\nindex fd177f9cd6af6799cce6ce10a4a6daab36cadd8f..add259cafec1cc811bcc1be7cc9b3226831eefe3 100644\n--- a/R/fmri_lm_runwise.R\n+++ b/R/fmri_lm_runwise.R\n@@ -484,40 +484,47 @@ pool_runwise_results <- function(cres, event_indices, baseline_indices, Vu) {\n }))\n sigma <- sqrt(colSums(sigma_mat) / sum(rdf_vals))\n rss <- colSums(do.call(rbind, lapply(cres, function(x) as.matrix(x$rss))))\n rdf <- sum(rdf_vals)\n resvar <- rss / rdf\n \n # Pool over runs\n if (length(cres) > 1) {\n meta_con <- meta_contrasts(conres_list)\n # Include all beta indices (event + baseline)\n all_indices <- c(event_indices, baseline_indices)\n meta_beta <- meta_betas(bstats_list, all_indices)\n \n list(\n contrasts = meta_con,\n betas = meta_beta,\n event_indices = event_indices,\n baseline_indices = baseline_indices,\n cov.unscaled = Vu,\n sigma = sigma,\n rss = rss,\n rdf = rdf,\n resvar = resvar\n )\n } else {\n- # Single run - return same structure as multi-run for consistency\n- # conres_list[[1]] is already a list of contrast tibbles\n+ # Single run - combine contrast list into one tibble to match\n+ # downstream expectations in pull_stat()/print methods.\n+ single_contrasts <- conres_list[[1]]\n+ if (length(single_contrasts) > 0) {\n+ combined_contrasts <- dplyr::bind_rows(single_contrasts)\n+ } else {\n+ combined_contrasts <- tibble::tibble()\n+ }\n+\n list(\n- contrasts = conres_list[[1]], # List of contrast tibbles (same as meta_contrasts)\n+ contrasts = combined_contrasts,\n betas = bstats_list[[1]],\n event_indices = event_indices,\n baseline_indices = baseline_indices,\n cov.unscaled = Vu,\n sigma = cres[[1]]$sigma,\n rss = cres[[1]]$rss,\n rdf = cres[[1]]$rdf,\n resvar = cres[[1]]$resvar\n )\n }\n }\n","external_storage_diff",{"_1119":1120,"_1121":-5},"files_modified","lines_added",10,"lines_removed",3,"commit_message","Fix runwise single-run contrasts shape","additional_parent_commit_shas",[],"file_id","file_0000000008b871f79d61021518e33ec8","ttl","partial_repo_snapshot","files",[1125],{"_158":775,"_1126":-5,"_1127":1128,"_1129":-5},"contents","line_range_contents",[1130],"missing_reason",{"_1131":796,"_1132":1133,"_444":1134,"_1135":59},"line_range_start","line_range_end",530,[1136,1137,1138,1139,187,187,1140,1141,1142,1143,1141,1144,1145,1146,1147,1148,1149,1150,1151,1152,1153,1154,1155,1156,1157,1158,1159,1160,1161,1162,1163,1161,1164,1165,1166,1161,1167,1168,1169,1170,1171,1161,1172,1173,1174,1175,1176,1161,1177,1178,1179,1180,1161,1181,1182,1183,1161,1184,1185,1186,1187,1188,1189,1190,1161,1191,1186,1187,1188,1189,1192,1161,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,1205,1206,1207,1208,1209,1171,1161,1210,1211,1212,1197,1198,1199,1200,1201,1202,1203,1213,1214,1205,1215,1208,1216,1217,1197,1198,1199,1218,1202,1203,1205,1215,1208,1171,1161,1219,1220,1221,187,1222,1155,1223,1224,1225,1226,1161,1227,1228,187,1229,1230,1231,1232,1233,1234,1235,1236,1237,1232,1238,1239,1240,1241,1242,1243,1244,1245,1246,1247,1248,1237,1232,1249,1250,1251,1245,1252,1237,187,1253,1254,1255,1256,1257,1258,1259,1200,1201,1202,1203,1260,1261,1208,187,1262,1263,1264,1202,1203,1265,1266,1267,1268,1208,1232,1269,1171,1161,1270,1271,1221,187,1272,1155,1223,1273,1274,1275,1161,1276,1277,1161,1227,1173,1161,1229,1230,1278,1232,1279,1280,1232,1281,1282,1283,1232,1284,1285,1286,1287,1232,1262,1288,1289,1202,1203,1290,1291,1292,1293,1208,1232,1269,1171,1161,1294,1221,187,1295,1155,1223,1296,1297,1298,1299,1161,1228,187,1300,1161,1227,1161,1229,1230,1301,1232,1279,1280,1232,1281,1302,1303,1304,1305,1232,1306,1307,1308,1232,1309,1310,1311,1312,1313,1314,1232,1315,1316,1317,1318,1319,1320,1321,1322,1318,1323,1324,1325,1326,1327,1328,1329,1330,1326,1331,1332,1333,1334,1335,1326,1336,1337,1338,1326,1339,1340,1341,1342,1326,1343,1344,1345,1346,1347,1348,1349,1350,1351,1352,1353,1354,1355,1356,1357,1358,1359,1360,1361,1353,1362,1363,1346,1347,1348,1364,1354,1355,1365,1366,1367,1359,1368,1369,1353,1330,1370,1318,1371,1372,1373,1374,1375,1376,1377,1378,1330,1322,1318,1245,1379,1372,1325,1326,1327,1328,1380,1330,1326,1331,1332,1333,1334,1335,1326,1336,1337,1338,1381,1326,1339,1340,1341,1342,1326,1343,1344,1345,1346,1347,1348,1349,1350,1351,1352,1353,1354,1382,1383,1384,1385,1362,1363,1346,1347,1348,1364,1354,1386,1387,1388,1389,1330,1326,1390,1391,1330,1322,1237,1232,1392,1393,1232,1394,1395,1396,1397,1398,1399,1400,1401,1402,1403,1404,1208,1232,1405,1406,1396,1407,1397,1408,1409,1399,1401,1402,1403,1404,1208,1232,1262,1410,1411,1202,1203,1412,1291,1413,1414,1208,1232,1269,1171,1161,1415,1220,1221,187,1416,1155,1223,1417,1418,1419,1420,1161,1421,1422,1423,1424,1425,1426,1427,1428,1429,1161,1430,1431,1432,1433,1434,1435,1232,1436,1437,1438,1202,1203,1439,1440,1290,1291,1441,1208,1216,1442,1443,1444,1445,1446,1245,1447,1237,187,1436,1448,1449,1202,1203,1439,1450,1451,1452,1453,1208,1171,1221],"contains_end_of_file","# Runwise Linear Model Strategy","# Implementation of the runwise fitting strategy using modular components","#' @importFrom future.apply future_lapply","NULL","#' Perform Runwise Linear Modeling on fMRI Dataset","#'","#' This function performs a runwise linear model analysis on an fMRI dataset,","#' running the linear model on each run separately and then pooling results.","#' @param dset An \\code{fmri_dataset} object.","#' @param model The \\code{fmri_model} used for the analysis.","#' @param contrast_objects The list of full contrast objects.","#' @param cfg An \\code{fmri_lm_config} object containing all fitting options.","#' @param verbose Logical. Whether to display progress messages (default is \\code{FALSE}).","#' @param use_fast_path Logical. If \\code{TRUE}, use matrix-based computation for speed. Default is \\code{FALSE}.","#' @param progress Logical. Display a progress bar for run processing. Default is \\code{FALSE}.","#' @param phi_fixed Optional fixed AR parameters.","#' @param sigma_fixed Optional fixed robust scale estimate.","#' @param parallel_voxels Logical. If TRUE, process voxels in parallel using future.apply.","#' @return A list containing the combined results from runwise linear model analysis.","#' @keywords internal","runwise_lm <- function(dset, model, contrast_objects, cfg, verbose = FALSE,"," use_fast_path = FALSE, progress = FALSE,"," phi_fixed = NULL,"," sigma_fixed = NULL,"," parallel_voxels = FALSE) {"," "," # Validate config"," assert_that(inherits(cfg, \"fmri_lm_config\"), msg = \"'cfg' must be an 'fmri_lm_config' object\")"," # Get run chunks"," chunk_iter <- exec_strategy(\"runwise\")(dset)"," chunks <- collect_chunks(chunk_iter)"," # Progress bar setup"," if (progress) {"," pb <- cli::cli_progress_bar(\"Fitting runs\", total = length(chunks), clear = FALSE)"," on.exit(cli::cli_progress_done(id = pb), add = TRUE)"," }"," # Extract model components"," form <- get_formula(model)"," tmats <- term_matrices(model)"," event_indices <- attr(tmats, \"event_term_indices\")"," baseline_indices <- attr(tmats, \"baseline_term_indices\")"," # Global design matrix for Vu calculation"," modmat_global <- design_matrix(model)"," proj_global <- .fast_preproject(modmat_global)"," Vu <- proj_global$XtXinv"," # Separate contrast types"," simple_conlist <- Filter(function(x) inherits(x, \"contrast\"), contrast_objects)"," fconlist <- Filter(function(x) inherits(x, \"Fcontrast\"), contrast_objects)"," # Extract weights with colind attributes"," simple_conlist_weights <- lapply(simple_conlist, function(x) {"," w <- x$weights"," attr(w, \"colind\") <- attr(x, \"colind\")"," w"," })"," names(simple_conlist_weights) <- sapply(simple_conlist, `[[`, \"name\")"," fconlist_weights <- lapply(fconlist, function(x) {"," names(fconlist_weights) <- sapply(fconlist, `[[`, \"name\")"," # Determine if we're using voxelwise AR"," if (!use_fast_path && cfg$ar$voxelwise && cfg$ar$struct != \"iid\") {"," # Voxelwise AR path"," result <- runwise_lm_voxelwise("," chunks = chunks,"," model = model,"," cfg = cfg,"," simple_conlist_weights = simple_conlist_weights,"," fconlist_weights = fconlist_weights,"," event_indices = event_indices,"," baseline_indices = baseline_indices,"," Vu = Vu,"," verbose = verbose,"," progress = progress,"," parallel_voxels = parallel_voxels"," )"," return(result)"," # Process runs"," cres <- if (use_fast_path) {"," runwise_lm_fast("," phi_fixed = phi_fixed,"," sigma_fixed = sigma_fixed,"," progress = progress"," } else {"," runwise_lm_slow("," contrast_objects = contrast_objects,"," # Pool results across runs"," pool_runwise_results(cres, event_indices, baseline_indices, Vu)","}","#' Runwise LM Fast Path","#' @noRd","runwise_lm_fast <- function(chunks, model, cfg, simple_conlist_weights, fconlist_weights,"," event_indices, baseline_indices, phi_fixed = NULL, "," sigma_fixed = NULL, verbose = FALSE, progress = FALSE) {"," cres <- vector(\"list\", length(chunks))"," ar_order <- get_ar_order(cfg)"," for (i in seq_along(chunks)) {"," ym <- chunks[[i]]"," if (verbose) message(\"Processing run (fast path) \", ym$chunk_num)"," "," # Skip empty runs"," if (ncol(ym$data) == 0 || nrow(ym$data) == 0) {"," warning(paste(\"Skipping empty run\", ym$chunk_num))"," next"," }"," # Determine which processing function to use"," if (cfg$robust$type != FALSE && cfg$ar$struct != \"iid\") {"," # Combined AR + Robust"," res <- process_run_ar_robust(ym, model, cfg, phi_fixed, sigma_fixed)"," } else if (cfg$robust$type != FALSE) {"," # Robust only"," res <- process_run_robust(ym, model, cfg, sigma_fixed)"," } else {"," # Standard (with or without AR)"," res <- process_run_standard(ym, model, cfg, phi_fixed, "," simple_conlist_weights, fconlist_weights)"," # Extract robust weights if available"," robust_weights_for_stats <- if (cfg$robust$type != FALSE && !is.null(res$robust_weights)) {"," res$robust_weights"," NULL"," # Compute chunk statistics using shared helper"," chunk_stats <- compute_chunk_stats("," betas = res$betas,"," sigma2 = res$sigma2,"," XtXinv = res$XtXinv,"," dfres = res$dfres,"," varnames = colnames(res$X_final),"," robust_weights = robust_weights_for_stats,"," ar_order = res$ar_order"," cres[[i]] <- list("," conres = chunk_stats$contrasts,"," bstats = chunk_stats$bstats,"," rss = res$rss,"," rdf = res$dfres,"," resvar = res$sigma2,"," sigma = sqrt(res$sigma2)"," if (progress) cli::cli_progress_update()"," # Filter out NULL results from skipped empty runs"," Filter(Negate(is.null), cres)","#' Runwise LM Slow Path","runwise_lm_slow <- function(chunks, model, cfg, contrast_objects, "," event_indices, baseline_indices,"," verbose = FALSE, progress = FALSE) {"," # Determine fitting function"," lmfun <- if (cfg$robust$type != FALSE) multiresponse_rlm else multiresponse_lm"," if (verbose) message(\"Processing run (slow path) \", ym$chunk_num)"," tmats <- term_matrices(model, ym$chunk_num)"," vnames <- attr(tmats, \"varnames\")"," data_env <- list2env(tmats)"," data_env$.y <- as.matrix(ym$data)"," ret <- lmfun(form, data_env, contrast_objects, vnames, fcon = NULL)"," rss <- colSums(as.matrix(ret$fit$residuals^2))"," rdf <- ret$fit$df.residual"," resvar <- rss / rdf"," sigma <- sqrt(resvar)"," conres = ret$contrasts,"," bstats = ret$bstats,"," rss = rss,"," rdf = rdf,"," resvar = resvar,"," sigma = sigma"," cres","#' Runwise LM with Voxelwise AR","runwise_lm_voxelwise <- function(chunks, model, cfg, simple_conlist_weights, fconlist_weights,"," event_indices, baseline_indices, Vu,"," verbose = FALSE, progress = FALSE, "," parallel_voxels = FALSE) {"," if (verbose) message(\"Using voxelwise AR(\", ar_order, \") modeling...\")"," if (verbose) message(\"Processing run \", ym$chunk_num, \" with voxelwise AR\")"," Y_run <- as.matrix(ym$data)"," data_env$.y <- Y_run"," form <- get_formula(model)"," X_run <- model.matrix(form, data_env)"," n_voxels <- ncol(Y_run)"," n_timepoints <- nrow(Y_run)"," p <- ncol(X_run)"," # Storage for voxel-wise results"," betas_voxelwise <- matrix(NA_real_, p, n_voxels)"," sigma_voxelwise <- numeric(n_voxels)"," rss_voxelwise <- numeric(n_voxels)"," phi_voxelwise <- matrix(NA_real_, ar_order, n_voxels)"," XtXinv_list <- vector(\"list\", n_voxels)"," # Setup for parallel processing if requested"," if (parallel_voxels && requireNamespace(\"future.apply\", quietly = TRUE)) {"," if (verbose) message(\"Processing voxels in parallel...\")"," "," # Use progressr for parallel progress"," if (progress && requireNamespace(\"progressr\", quietly = TRUE)) {"," p_prog <- progressr::progressor(steps = n_voxels)"," }"," # Process voxels in parallel"," voxel_results <- future.apply::future_lapply(seq_len(n_voxels), function(v) {"," y_voxel <- Y_run[, v]"," "," # Skip if all zeros or constant"," if (var(y_voxel) < .Machine$double.eps) {"," return(NULL)"," }"," # Initial OLS fit"," Y_voxel_mat <- matrix(y_voxel, ncol = 1)"," proj_voxel <- .fast_preproject(X_run)"," glm_ctx_voxel <- glm_context(X = X_run, Y = Y_voxel_mat, proj = proj_voxel)"," initial_fit <- solve_glm_core(glm_ctx_voxel)"," # Estimate voxel-specific AR parameters"," resid_ols <- Y_voxel_mat - X_run %*% initial_fit$betas"," phi_voxel <- estimate_ar_parameters(resid_ols[, 1], ar_order)"," # AR whitening for this voxel"," tmp <- ar_whiten_transform(X_run, Y_voxel_mat, phi_voxel, cfg$ar$exact_first)"," X_voxel_w <- tmp$X"," Y_voxel_w <- tmp$Y"," # Fit on whitened data"," if (cfg$robust$type != FALSE) {"," # Robust fit on whitened data"," proj_voxel_w <- .fast_preproject(X_voxel_w)"," glm_ctx_voxel_w <- glm_context(X = X_voxel_w, Y = Y_voxel_w, "," proj = proj_voxel_w, phi_hat = phi_voxel)"," robust_fit <- robust_iterative_fitter("," initial_glm_ctx = glm_ctx_voxel_w,"," cfg_robust_options = cfg$robust,"," X_orig_for_resid = X_voxel_w"," )"," "," list("," betas = robust_fit$betas_robust[, 1],"," rss = sum((Y_voxel_w - X_voxel_w %*% robust_fit$betas_robust)^2),"," sigma = robust_fit$sigma_robust_scale_final,"," phi = phi_voxel,"," XtXinv = robust_fit$XtWXi_final,"," robust_weights = robust_fit$robust_weights_final"," } else {"," # OLS on whitened data"," fit_w <- solve_glm_core(glm_ctx_voxel_w)"," betas = fit_w$betas[, 1],"," rss = fit_w$rss,"," sigma = sqrt(fit_w$sigma2),"," XtXinv = proj_voxel_w$XtXinv,"," robust_weights = NULL"," }, future.seed = TRUE)"," # Collect results"," for (v in seq_len(n_voxels)) {"," if (!is.null(voxel_results[[v]])) {"," betas_voxelwise[, v] <- voxel_results[[v]]$betas"," rss_voxelwise[v] <- voxel_results[[v]]$rss"," sigma_voxelwise[v] <- voxel_results[[v]]$sigma"," phi_voxelwise[, v] <- voxel_results[[v]]$phi"," XtXinv_list[[v]] <- voxel_results[[v]]$XtXinv"," # Sequential processing"," next"," phi_voxelwise[, v] <- phi_voxel"," betas_voxelwise[, v] <- robust_fit$betas_robust[, 1]"," rss_voxelwise[v] <- sum((Y_voxel_w - X_voxel_w %*% robust_fit$betas_robust)^2)"," sigma_voxelwise[v] <- robust_fit$sigma_robust_scale_final"," XtXinv_list[[v]] <- robust_fit$XtWXi_final"," betas_voxelwise[, v] <- fit_w$betas[, 1]"," rss_voxelwise[v] <- fit_w$rss"," sigma_voxelwise[v] <- sqrt(fit_w$sigma2)"," XtXinv_list[[v]] <- proj_voxel_w$XtXinv"," if (progress && v %% 100 == 0) {"," message(\" Processed \", v, \" / \", n_voxels, \" voxels\")"," # Calculate statistics and contrasts"," rdf <- n_timepoints - p"," # Beta statistics with voxelwise XtXinv"," bstats <- beta_stats_matrix_voxelwise("," betas_voxelwise,"," XtXinv_list,"," sigma_voxelwise,"," rdf,"," vnames,"," robust_weights_list = if (cfg$robust$type != FALSE) {"," lapply(XtXinv_list, function(x) attr(x, \"robust_weights\"))"," } else NULL,"," ar_order = ar_order"," # Contrasts with voxelwise XtXinv"," conres <- fit_lm_contrasts_voxelwise("," sigma_voxelwise^2,"," simple_conlist_weights,"," fconlist_weights,"," conres = conres,"," bstats = bstats,"," rss = rss_voxelwise,"," resvar = rss_voxelwise / rdf,"," sigma = sigma_voxelwise"," # Pool results","#' Pool Runwise Results","pool_runwise_results <- function(cres, event_indices, baseline_indices, Vu) {"," # Extract components for pooling"," bstats_list <- lapply(cres, `[[`, \"bstats\")"," conres_list <- lapply(cres, `[[`, \"conres\")"," # Compute overall statistics"," rdf_vals <- unlist(lapply(cres, `[[`, \"rdf\"))"," sigma_mat <- do.call(rbind, lapply(seq_along(cres), function(i) {"," as.matrix(cres[[i]]$sigma^2) * rdf_vals[i]"," }))"," sigma <- sqrt(colSums(sigma_mat) / sum(rdf_vals))"," rss <- colSums(do.call(rbind, lapply(cres, function(x) as.matrix(x$rss))))"," rdf <- sum(rdf_vals)"," resvar <- rss / rdf"," # Pool over runs"," if (length(cres) > 1) {"," meta_con <- meta_contrasts(conres_list)"," # Include all beta indices (event + baseline)"," all_indices <- c(event_indices, baseline_indices)"," meta_beta <- meta_betas(bstats_list, all_indices)"," list("," contrasts = meta_con,"," betas = meta_beta,"," cov.unscaled = Vu,"," sigma = sigma,"," resvar = resvar"," # Single run - combine contrast list into one tibble to match"," # downstream expectations in pull_stat()/print methods."," single_contrasts <- conres_list[[1]]"," if (length(single_contrasts) > 0) {"," combined_contrasts <- dplyr::bind_rows(single_contrasts)"," combined_contrasts <- tibble::tibble()"," contrasts = combined_contrasts,"," betas = bstats_list[[1]],"," sigma = cres[[1]]$sigma,"," rss = cres[[1]]$rss,"," rdf = cres[[1]]$rdf,"," resvar = cres[[1]]$resvar",[1455,1456,1457,1458,1459],{"_1084":46,"_46":1467},{"_1084":1461,"_158":775,"_1131":1465,"_1132":1466},{"_1084":46,"_46":1464},{"_1084":1461,"_158":775,"_1131":1462,"_1132":1463},{"_1084":46,"_46":1460},"\n\n**Testing**\n* ⚠️ `Rscript -e \"parse(file='R/fmri_lm_runwise.R'); cat('parse ok\\n')\"` (warning: `Rscript` is not available in this environment, so syntax validation could not be executed).","repo_file_citation",518,528,"\n* Kept all other pooled outputs unchanged (`betas`, `sigma`, `rss`, `rdf`, `resvar`) while only normalizing the single-run contrasts container type. ",509,520,"### Summary\n* Updated `pool_runwise_results()` so the single-run branch now returns `contrasts` as a **bound tibble** (or an empty tibble), instead of returning the raw contrast list. This restores shape consistency with downstream expectations and avoids failures in consumers expecting tabular contrast output. ",{"_93":301,"_42":85,"_97":301,"_1470":1471,"_1472":1473,"_1474":1475,"_133":-5,"_120":1476},[84],"input_items",[1494,1495,1496,1497,1498],"external_post_config",{"_1477":1478,"_247":184,"_1479":282,"_258":-5,"_1105":241,"_1480":-5,"_1481":-5,"_1482":1483,"_1484":1485,"_1486":-5,"_1487":-5,"_1488":1489,"_1490":15,"_1491":59,"_1492":54,"_1493":15},"user_model_slug","slug",1776625036.610796,"installation_id",67092630,"pr_number","gitlab_service_account_project_id","review_trigger","comment_id",4276590902,"comment_type","issue","pr_write_guard_token","pending_pr_reaction_id","pending_comment_reaction_id",348568837,"comment_if_no_findings","comment_on_error","share_url","is_parallel_review",{"_93":450,"_97":301,"_444":1510},{"_289":290,"_291":292,"_1507":296,"_1508":130,"_298":-5,"_93":1509},{"_93":1499,"_291":1506,"_158":-5,"_1501":-5,"_1502":-5},{"_93":1499,"_291":1503,"_158":775,"_1501":1504,"_1502":1505},{"_93":1499,"_291":1500,"_158":-5,"_1501":-5,"_1502":-5},"pull_request_comment","\n### 💡 Codex Review\n\nHere are some automated review suggestions for this pull request.\n\n**Reviewed commit:** `79b52fd10c`\n \n\n

ℹ️ About Codex in GitHub

","side","line","**_{_{![P1 Badge](https://img.shields.io/badge/P1-orange?style=flat)}} Preserve tibble shape for single-run contrasts**\n\nReturn `contrasts` in the same tabular structure as the multi-run branch: this line now returns the raw list (`conres_list[[1]]`) instead of a bound tibble, so single-run outputs have a different type than multi-run outputs. Downstream consumers expect `x$result$contrasts` to be a data frame (e.g., `pull_stat()` filters on `type`), so runwise fits with only one run will fail when users request contrast coefficients/statistics.\n\nUseful? 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